Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

Lykon/dreamshaper-xl-v2-turbo

Lykon

diffusers

text-to-image

--- language: - en license: openrail++ tags: - stable-diffusion - stable-diffusion-diffusers - stable-diffusion-xl - stable-diffusion-xl-turbo - text-to-image - art - artistic - diffusers - anime - dreamshaper - turbo - lcm duplicated_from: lykon/dreamshaper-xl-v2-turbo --- # Dreamshaper XL v2 Turbo `lykon/dreamshaper-xl-v2-turbo` is a Stable Diffusion model that has been fine-tuned on [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). Please consider supporting me: - on [Patreon](https://www.patreon.com/Lykon275) - or [buy me a coffee](https://snipfeed.co/lykon) ## Diffusers For more general information on how to run text-to-image models with 🧨 Diffusers, see [the docs](https://huggingface.co/docs/diffusers/using-diffusers/conditional_image_generation). 1. Installation ``` pip install diffusers transformers accelerate ``` 2. Run ```py from diffusers import AutoPipelineForText2Image, DPMSolverMultistepScheduler import torch pipe = AutoPipelineForText2Image.from_pretrained('lykon/dreamshaper-xl-v2-turbo', torch_dtype=torch.float16, variant="fp16") pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe = pipe.to("cuda") prompt = "portrait photo of muscular bearded guy in a worn mech suit, light bokeh, intricate, steel metal, elegant, sharp focus, soft lighting, vibrant colors" generator = torch.manual_seed(0) image = pipe(prompt, num_inference_steps=6, guidance_scale=2).images[0] image.save("./image.png") ```

tohoku-nlp/bert-base-japanese-char-v2

tohoku-nlp

transformers

fill-mask

--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia widget: - text: 東北大学で[MASK]の研究をしています。 --- # BERT base Japanese (character-level tokenization with whole word masking, jawiki-20200831) This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language. This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by character-level tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/tree/v2.0). ## Model architecture The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. ## Training Data The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020. The generated corpus files are 4.0GB in total, containing approximately 30M sentences. We used the [MeCab](https://taku910.github.io/mecab/) morphological parser with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary to split texts into sentences. ## Tokenization The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into characters. The vocabulary size is 6144. We used [`fugashi`](https://github.com/polm/fugashi) and [`unidic-lite`](https://github.com/polm/unidic-lite) packages for the tokenization. ## Training The models are trained with the same configuration as the original BERT; 512 tokens per instance, 256 instances per batch, and 1M training steps. For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once. For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TensorFlow Research Cloud program](https://www.tensorflow.org/tfrc/). The training took about 5 days to finish. ## Licenses The pretrained models are distributed under the terms of the [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/). ## Acknowledgments This model is trained with Cloud TPUs provided by [TensorFlow Research Cloud](https://www.tensorflow.org/tfrc/) program.

timm/regnety_032.ra_in1k

timm

timm

image-classification

--- license: apache-2.0 library_name: timm tags: - image-classification - timm datasets: - imagenet-1k --- # Model card for regnety_032.ra_in1k A RegNetY-3.2GF image classification model. Trained on ImageNet-1k by Ross Wightman in `timm`. The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 19.4 - GMACs: 3.2 - Activations (M): 11.3 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('regnety_032.ra_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'regnety_032.ra_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 32, 112, 112]) # torch.Size([1, 72, 56, 56]) # torch.Size([1, 216, 28, 28]) # torch.Size([1, 576, 14, 14]) # torch.Size([1, 1512, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'regnety_032.ra_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1512, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). For the comparison summary below, the ra_in1k, ra3_in1k, ch_in1k, sw_*, and lion_* tagged weights are trained in `timm`. |model |img_size|top1 |top5 |param_count|gmacs|macts | |-------------------------|--------|------|------|-----------|-----|------| |[regnety_1280.swag_ft_in1k](https://huggingface.co/timm/regnety_1280.swag_ft_in1k)|384 |88.228|98.684|644.81 |374.99|210.2 | |[regnety_320.swag_ft_in1k](https://huggingface.co/timm/regnety_320.swag_ft_in1k)|384 |86.84 |98.364|145.05 |95.0 |88.87 | |[regnety_160.swag_ft_in1k](https://huggingface.co/timm/regnety_160.swag_ft_in1k)|384 |86.024|98.05 |83.59 |46.87|67.67 | |[regnety_160.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.sw_in12k_ft_in1k)|288 |86.004|97.83 |83.59 |26.37|38.07 | |[regnety_1280.swag_lc_in1k](https://huggingface.co/timm/regnety_1280.swag_lc_in1k)|224 |85.996|97.848|644.81 |127.66|71.58 | |[regnety_160.lion_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.lion_in12k_ft_in1k)|288 |85.982|97.844|83.59 |26.37|38.07 | |[regnety_160.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.sw_in12k_ft_in1k)|224 |85.574|97.666|83.59 |15.96|23.04 | |[regnety_160.lion_in12k_ft_in1k](https://huggingface.co/timm/regnety_160.lion_in12k_ft_in1k)|224 |85.564|97.674|83.59 |15.96|23.04 | |[regnety_120.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_120.sw_in12k_ft_in1k)|288 |85.398|97.584|51.82 |20.06|35.34 | |[regnety_2560.seer_ft_in1k](https://huggingface.co/timm/regnety_2560.seer_ft_in1k)|384 |85.15 |97.436|1282.6 |747.83|296.49| |[regnetz_e8.ra3_in1k](https://huggingface.co/timm/regnetz_e8.ra3_in1k)|320 |85.036|97.268|57.7 |15.46|63.94 | |[regnety_120.sw_in12k_ft_in1k](https://huggingface.co/timm/regnety_120.sw_in12k_ft_in1k)|224 |84.976|97.416|51.82 |12.14|21.38 | |[regnety_320.swag_lc_in1k](https://huggingface.co/timm/regnety_320.swag_lc_in1k)|224 |84.56 |97.446|145.05 |32.34|30.26 | |[regnetz_040_h.ra3_in1k](https://huggingface.co/timm/regnetz_040_h.ra3_in1k)|320 |84.496|97.004|28.94 |6.43 |37.94 | |[regnetz_e8.ra3_in1k](https://huggingface.co/timm/regnetz_e8.ra3_in1k)|256 |84.436|97.02 |57.7 |9.91 |40.94 | |[regnety_1280.seer_ft_in1k](https://huggingface.co/timm/regnety_1280.seer_ft_in1k)|384 |84.432|97.092|644.81 |374.99|210.2 | |[regnetz_040.ra3_in1k](https://huggingface.co/timm/regnetz_040.ra3_in1k)|320 |84.246|96.93 |27.12 |6.35 |37.78 | |[regnetz_d8.ra3_in1k](https://huggingface.co/timm/regnetz_d8.ra3_in1k)|320 |84.054|96.992|23.37 |6.19 |37.08 | |[regnetz_d8_evos.ch_in1k](https://huggingface.co/timm/regnetz_d8_evos.ch_in1k)|320 |84.038|96.992|23.46 |7.03 |38.92 | |[regnetz_d32.ra3_in1k](https://huggingface.co/timm/regnetz_d32.ra3_in1k)|320 |84.022|96.866|27.58 |9.33 |37.08 | |[regnety_080.ra3_in1k](https://huggingface.co/timm/regnety_080.ra3_in1k)|288 |83.932|96.888|39.18 |13.22|29.69 | |[regnety_640.seer_ft_in1k](https://huggingface.co/timm/regnety_640.seer_ft_in1k)|384 |83.912|96.924|281.38 |188.47|124.83| |[regnety_160.swag_lc_in1k](https://huggingface.co/timm/regnety_160.swag_lc_in1k)|224 |83.778|97.286|83.59 |15.96|23.04 | |[regnetz_040_h.ra3_in1k](https://huggingface.co/timm/regnetz_040_h.ra3_in1k)|256 |83.776|96.704|28.94 |4.12 |24.29 | |[regnetv_064.ra3_in1k](https://huggingface.co/timm/regnetv_064.ra3_in1k)|288 |83.72 |96.75 |30.58 |10.55|27.11 | |[regnety_064.ra3_in1k](https://huggingface.co/timm/regnety_064.ra3_in1k)|288 |83.718|96.724|30.58 |10.56|27.11 | |[regnety_160.deit_in1k](https://huggingface.co/timm/regnety_160.deit_in1k)|288 |83.69 |96.778|83.59 |26.37|38.07 | |[regnetz_040.ra3_in1k](https://huggingface.co/timm/regnetz_040.ra3_in1k)|256 |83.62 |96.704|27.12 |4.06 |24.19 | |[regnetz_d8.ra3_in1k](https://huggingface.co/timm/regnetz_d8.ra3_in1k)|256 |83.438|96.776|23.37 |3.97 |23.74 | |[regnetz_d32.ra3_in1k](https://huggingface.co/timm/regnetz_d32.ra3_in1k)|256 |83.424|96.632|27.58 |5.98 |23.74 | |[regnetz_d8_evos.ch_in1k](https://huggingface.co/timm/regnetz_d8_evos.ch_in1k)|256 |83.36 |96.636|23.46 |4.5 |24.92 | |[regnety_320.seer_ft_in1k](https://huggingface.co/timm/regnety_320.seer_ft_in1k)|384 |83.35 |96.71 |145.05 |95.0 |88.87 | |[regnetv_040.ra3_in1k](https://huggingface.co/timm/regnetv_040.ra3_in1k)|288 |83.204|96.66 |20.64 |6.6 |20.3 | |[regnety_320.tv2_in1k](https://huggingface.co/timm/regnety_320.tv2_in1k)|224 |83.162|96.42 |145.05 |32.34|30.26 | |[regnety_080.ra3_in1k](https://huggingface.co/timm/regnety_080.ra3_in1k)|224 |83.16 |96.486|39.18 |8.0 |17.97 | |[regnetv_064.ra3_in1k](https://huggingface.co/timm/regnetv_064.ra3_in1k)|224 |83.108|96.458|30.58 |6.39 |16.41 | |[regnety_040.ra3_in1k](https://huggingface.co/timm/regnety_040.ra3_in1k)|288 |83.044|96.5 |20.65 |6.61 |20.3 | |[regnety_064.ra3_in1k](https://huggingface.co/timm/regnety_064.ra3_in1k)|224 |83.02 |96.292|30.58 |6.39 |16.41 | |[regnety_160.deit_in1k](https://huggingface.co/timm/regnety_160.deit_in1k)|224 |82.974|96.502|83.59 |15.96|23.04 | |[regnetx_320.tv2_in1k](https://huggingface.co/timm/regnetx_320.tv2_in1k)|224 |82.816|96.208|107.81 |31.81|36.3 | |[regnety_032.ra_in1k](https://huggingface.co/timm/regnety_032.ra_in1k)|288 |82.742|96.418|19.44 |5.29 |18.61 | |[regnety_160.tv2_in1k](https://huggingface.co/timm/regnety_160.tv2_in1k)|224 |82.634|96.22 |83.59 |15.96|23.04 | |[regnetz_c16_evos.ch_in1k](https://huggingface.co/timm/regnetz_c16_evos.ch_in1k)|320 |82.634|96.472|13.49 |3.86 |25.88 | |[regnety_080_tv.tv2_in1k](https://huggingface.co/timm/regnety_080_tv.tv2_in1k)|224 |82.592|96.246|39.38 |8.51 |19.73 | |[regnetx_160.tv2_in1k](https://huggingface.co/timm/regnetx_160.tv2_in1k)|224 |82.564|96.052|54.28 |15.99|25.52 | |[regnetz_c16.ra3_in1k](https://huggingface.co/timm/regnetz_c16.ra3_in1k)|320 |82.51 |96.358|13.46 |3.92 |25.88 | |[regnetv_040.ra3_in1k](https://huggingface.co/timm/regnetv_040.ra3_in1k)|224 |82.44 |96.198|20.64 |4.0 |12.29 | |[regnety_040.ra3_in1k](https://huggingface.co/timm/regnety_040.ra3_in1k)|224 |82.304|96.078|20.65 |4.0 |12.29 | |[regnetz_c16.ra3_in1k](https://huggingface.co/timm/regnetz_c16.ra3_in1k)|256 |82.16 |96.048|13.46 |2.51 |16.57 | |[regnetz_c16_evos.ch_in1k](https://huggingface.co/timm/regnetz_c16_evos.ch_in1k)|256 |81.936|96.15 |13.49 |2.48 |16.57 | |[regnety_032.ra_in1k](https://huggingface.co/timm/regnety_032.ra_in1k)|224 |81.924|95.988|19.44 |3.2 |11.26 | |[regnety_032.tv2_in1k](https://huggingface.co/timm/regnety_032.tv2_in1k)|224 |81.77 |95.842|19.44 |3.2 |11.26 | |[regnetx_080.tv2_in1k](https://huggingface.co/timm/regnetx_080.tv2_in1k)|224 |81.552|95.544|39.57 |8.02 |14.06 | |[regnetx_032.tv2_in1k](https://huggingface.co/timm/regnetx_032.tv2_in1k)|224 |80.924|95.27 |15.3 |3.2 |11.37 | |[regnety_320.pycls_in1k](https://huggingface.co/timm/regnety_320.pycls_in1k)|224 |80.804|95.246|145.05 |32.34|30.26 | |[regnetz_b16.ra3_in1k](https://huggingface.co/timm/regnetz_b16.ra3_in1k)|288 |80.712|95.47 |9.72 |2.39 |16.43 | |[regnety_016.tv2_in1k](https://huggingface.co/timm/regnety_016.tv2_in1k)|224 |80.66 |95.334|11.2 |1.63 |8.04 | |[regnety_120.pycls_in1k](https://huggingface.co/timm/regnety_120.pycls_in1k)|224 |80.37 |95.12 |51.82 |12.14|21.38 | |[regnety_160.pycls_in1k](https://huggingface.co/timm/regnety_160.pycls_in1k)|224 |80.288|94.964|83.59 |15.96|23.04 | |[regnetx_320.pycls_in1k](https://huggingface.co/timm/regnetx_320.pycls_in1k)|224 |80.246|95.01 |107.81 |31.81|36.3 | |[regnety_080.pycls_in1k](https://huggingface.co/timm/regnety_080.pycls_in1k)|224 |79.882|94.834|39.18 |8.0 |17.97 | |[regnetz_b16.ra3_in1k](https://huggingface.co/timm/regnetz_b16.ra3_in1k)|224 |79.872|94.974|9.72 |1.45 |9.95 | |[regnetx_160.pycls_in1k](https://huggingface.co/timm/regnetx_160.pycls_in1k)|224 |79.862|94.828|54.28 |15.99|25.52 | |[regnety_064.pycls_in1k](https://huggingface.co/timm/regnety_064.pycls_in1k)|224 |79.716|94.772|30.58 |6.39 |16.41 | |[regnetx_120.pycls_in1k](https://huggingface.co/timm/regnetx_120.pycls_in1k)|224 |79.592|94.738|46.11 |12.13|21.37 | |[regnetx_016.tv2_in1k](https://huggingface.co/timm/regnetx_016.tv2_in1k)|224 |79.44 |94.772|9.19 |1.62 |7.93 | |[regnety_040.pycls_in1k](https://huggingface.co/timm/regnety_040.pycls_in1k)|224 |79.23 |94.654|20.65 |4.0 |12.29 | |[regnetx_080.pycls_in1k](https://huggingface.co/timm/regnetx_080.pycls_in1k)|224 |79.198|94.55 |39.57 |8.02 |14.06 | |[regnetx_064.pycls_in1k](https://huggingface.co/timm/regnetx_064.pycls_in1k)|224 |79.064|94.454|26.21 |6.49 |16.37 | |[regnety_032.pycls_in1k](https://huggingface.co/timm/regnety_032.pycls_in1k)|224 |78.884|94.412|19.44 |3.2 |11.26 | |[regnety_008_tv.tv2_in1k](https://huggingface.co/timm/regnety_008_tv.tv2_in1k)|224 |78.654|94.388|6.43 |0.84 |5.42 | |[regnetx_040.pycls_in1k](https://huggingface.co/timm/regnetx_040.pycls_in1k)|224 |78.482|94.24 |22.12 |3.99 |12.2 | |[regnetx_032.pycls_in1k](https://huggingface.co/timm/regnetx_032.pycls_in1k)|224 |78.178|94.08 |15.3 |3.2 |11.37 | |[regnety_016.pycls_in1k](https://huggingface.co/timm/regnety_016.pycls_in1k)|224 |77.862|93.73 |11.2 |1.63 |8.04 | |[regnetx_008.tv2_in1k](https://huggingface.co/timm/regnetx_008.tv2_in1k)|224 |77.302|93.672|7.26 |0.81 |5.15 | |[regnetx_016.pycls_in1k](https://huggingface.co/timm/regnetx_016.pycls_in1k)|224 |76.908|93.418|9.19 |1.62 |7.93 | |[regnety_008.pycls_in1k](https://huggingface.co/timm/regnety_008.pycls_in1k)|224 |76.296|93.05 |6.26 |0.81 |5.25 | |[regnety_004.tv2_in1k](https://huggingface.co/timm/regnety_004.tv2_in1k)|224 |75.592|92.712|4.34 |0.41 |3.89 | |[regnety_006.pycls_in1k](https://huggingface.co/timm/regnety_006.pycls_in1k)|224 |75.244|92.518|6.06 |0.61 |4.33 | |[regnetx_008.pycls_in1k](https://huggingface.co/timm/regnetx_008.pycls_in1k)|224 |75.042|92.342|7.26 |0.81 |5.15 | |[regnetx_004_tv.tv2_in1k](https://huggingface.co/timm/regnetx_004_tv.tv2_in1k)|224 |74.57 |92.184|5.5 |0.42 |3.17 | |[regnety_004.pycls_in1k](https://huggingface.co/timm/regnety_004.pycls_in1k)|224 |74.018|91.764|4.34 |0.41 |3.89 | |[regnetx_006.pycls_in1k](https://huggingface.co/timm/regnetx_006.pycls_in1k)|224 |73.862|91.67 |6.2 |0.61 |3.98 | |[regnetx_004.pycls_in1k](https://huggingface.co/timm/regnetx_004.pycls_in1k)|224 |72.38 |90.832|5.16 |0.4 |3.14 | |[regnety_002.pycls_in1k](https://huggingface.co/timm/regnety_002.pycls_in1k)|224 |70.282|89.534|3.16 |0.2 |2.17 | |[regnetx_002.pycls_in1k](https://huggingface.co/timm/regnetx_002.pycls_in1k)|224 |68.752|88.556|2.68 |0.2 |2.16 | ## Citation ```bibtex @InProceedings{Radosavovic2020, title = {Designing Network Design Spaces}, author = {Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Doll{'a}r}, booktitle = {CVPR}, year = {2020} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

microsoft/wavlm-large

microsoft

transformers

feature-extraction

--- language: - en tags: - speech inference: false --- # WavLM-Large [Microsoft's WavLM](https://github.com/microsoft/unilm/tree/master/wavlm) The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more in-detail explanation of how to fine-tune the model. The model was pre-trained on: - 60,000 hours of [Libri-Light](https://arxiv.org/abs/1912.07875) - 10,000 hours of [GigaSpeech](https://arxiv.org/abs/2106.06909) - 24,000 hours of [VoxPopuli](https://arxiv.org/abs/2101.00390) [Paper: WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) Authors: Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei **Abstract** *Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.* The original model can be found under https://github.com/microsoft/unilm/tree/master/wavlm. # Usage This is an English pre-trained speech model that has to be fine-tuned on a downstream task like speech recognition or audio classification before it can be used in inference. The model was pre-trained in English and should therefore perform well only in English. The model has been shown to work well on the [SUPERB benchmark](https://superbbenchmark.org/). **Note**: The model was pre-trained on phonemes rather than characters. This means that one should make sure that the input text is converted to a sequence of phonemes before fine-tuning. ## Speech Recognition To fine-tune the model for speech recognition, see [the official speech recognition example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition). ## Speech Classification To fine-tune the model for speech classification, see [the official audio classification example](https://github.com/huggingface/transformers/tree/master/examples/pytorch/audio-classification). ## Speaker Verification TODO ## Speaker Diarization TODO # Contribution The model was contributed by [cywang](https://huggingface.co/cywang) and [patrickvonplaten](https://huggingface.co/patrickvonplaten). # License The official license can be found [here](https://github.com/microsoft/UniSpeech/blob/main/LICENSE) 

mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF

mradermacher

transformers

--- base_model: sophosympatheia/New-Dawn-Llama-3-70B-32K-v1.0 language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - mergekit - merge - Not-for-all-Audiences --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/sophosympatheia/New-Dawn-Llama-3-70B-32K-v1.0 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.IQ3_XS.gguf) | IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.IQ3_M.gguf) | IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/New-Dawn-Llama-3-70B-32K-v1.0-GGUF/resolve/main/New-Dawn-Llama-3-70B-32K-v1.0.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

mradermacher/MultiPL-T-CodeLlama_70b-GGUF

mradermacher

transformers

--- base_model: nuprl/MultiPL-T-CodeLlama_70b datasets: - nuprl/MultiPL-T language: - en library_name: transformers license: openrail quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/nuprl/MultiPL-T-CodeLlama_70b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q2_K.gguf) | Q2_K | 25.6 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.IQ3_XS.gguf) | IQ3_XS | 28.4 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q3_K_S.gguf) | Q3_K_S | 30.0 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.IQ3_M.gguf) | IQ3_M | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q3_K_L.gguf) | Q3_K_L | 36.2 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.IQ4_XS.gguf) | IQ4_XS | 37.3 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q5_K_S.gguf) | Q5_K_S | 47.6 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q5_K_M.gguf) | Q5_K_M | 48.9 | | | [PART 1](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality | | [PART 1](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_70b-GGUF/resolve/main/MultiPL-T-CodeLlama_70b.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

google/gemma-1.1-7b-it

google

transformers

text-generation

--- library_name: transformers widget: - messages: - role: user content: How does the brain work? inference: parameters: max_new_tokens: 200 extra_gated_heading: Access Gemma on Hugging Face extra_gated_prompt: >- To access Gemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license license: gemma --- # Gemma Model Card **Model Page**: [Gemma](https://ai.google.dev/gemma/docs) This model card corresponds to the latest 7B instruct version of the Gemma model. Here you can find other models in the Gemma family: | | Base | Instruct | |----|----------------------------------------------------|----------------------------------------------------------------------| | 2B | [gemma-2b](https://huggingface.co/google/gemma-2b) | [gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) | | 7B | [gemma-7b](https://huggingface.co/google/gemma-7b) | [**gemma-1.1-7b-it**](https://huggingface.co/google/gemma-1.1-7b-it) | **Release Notes** This is Gemma 1.1 7B (IT), an update over the original instruction-tuned Gemma release. Gemma 1.1 was trained using a novel RLHF method, leading to substantial gains on quality, coding capabilities, factuality, instruction following and multi-turn conversation quality. We also fixed a bug in multi-turn conversations, and made sure that model responses don't always start with `"Sure,"`. We believe this release represents an improvement for most use cases, but we encourage users to test in their particular applications. The previous model [will continue to be available in the same repo](https://huggingface.co/google/gemma-7b-it). We appreciate the enthusiastic adoption of Gemma, and we continue to welcome all feedback from the community. **Resources and Technical Documentation**: * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma) * [Gemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/335) **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent) **Authors**: Google ## Model Information Summary description and brief definition of inputs and outputs. ### Description Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. ### Usage Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers`, then copy the snippet from the section that is relevant for your usecase. #### Running the model on a CPU As explained below, we recommend `torch.bfloat16` as the default dtype. You can use [a different precision](#precisions) if necessary. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt") outputs = model.generate(**input_ids, max_new_tokens=50) print(tokenizer.decode(outputs[0])) ``` #### Running the model on a single / multi GPU ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` <a name="precisions"></a> #### Running the model on a GPU using different precisions The native weights of this model were exported in `bfloat16` precision. You can use `float16`, which may be faster on certain hardware, indicating the `torch_dtype` when loading the model. For convenience, the `float16` revision of the repo contains a copy of the weights already converted to that precision. You can also use `float32` if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to `float32`). See examples below. * _Using `torch.float16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", device_map="auto", torch_dtype=torch.float16, revision="float16", ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", device_map="auto", torch_dtype=torch.bfloat16 ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Upcasting to `torch.float32`_ ```python # pip install accelerate from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", device_map="auto" ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_8bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", quantization_config=quantization_config ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` * _Using 4-bit precision_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig quantization_config = BitsAndBytesConfig(load_in_4bit=True) tokenizer = AutoTokenizer.from_pretrained("google/gemma-1.1-7b-it") model = AutoModelForCausalLM.from_pretrained( "google/gemma-1.1-7b-it", quantization_config=quantization_config ) input_text = "Write me a poem about Machine Learning." input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") outputs = model.generate(**input_ids) print(tokenizer.decode(outputs[0])) ``` #### Other optimizations * _Flash Attention 2_ First make sure to install `flash-attn` in your environment `pip install flash-attn` ```diff model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, + attn_implementation="flash_attention_2" ).to(0) ``` #### Running the model in JAX / Flax Use the `flax` branch of the repository: ```python import jax.numpy as jnp from transformers import AutoTokenizer, FlaxGemmaForCausalLM model_id = "google/gemma-1.1-7b-it" tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.padding_side = "left" model, params = FlaxGemmaForCausalLM.from_pretrained( model_id, dtype=jnp.bfloat16, revision="flax", _do_init=False, ) inputs = tokenizer("Valencia and Málaga are", return_tensors="np", padding=True) output = model.generate(**inputs, params=params, max_new_tokens=20, do_sample=False) output_text = tokenizer.batch_decode(output.sequences, skip_special_tokens=True) ``` [Check this notebook](https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/jax_gemma.ipynb) for a comprehensive walkthrough on how to parallelize JAX inference. ### Chat Template The instruction-tuned models use a chat template that must be adhered to for conversational use. The easiest way to apply it is using the tokenizer's built-in chat template, as shown in the following snippet. Let's load the model and apply the chat template to a conversation. In this example, we'll start with a single user interaction: ```py from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "google/gemma-1.1-7b-it" dtype = torch.bfloat16 tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, device_map="cuda", torch_dtype=dtype, ) chat = [ { "role": "user", "content": "Write a hello world program" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` At this point, the prompt contains the following text: ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model ``` As you can see, each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (either `user`, for content supplied by the user, or `model` for LLM responses). Turns finish with the `<end_of_turn>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. After the prompt is ready, generation can be performed like this: ```py inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150) ``` ### Fine-tuning You can find some fine-tuning scripts under the [`examples/` directory](https://huggingface.co/google/gemma-7b/tree/main/examples) of [`google/gemma-7b`](https://huggingface.co/google/gemma-7b) repository. To adapt them to this model, simply change the model-id to `google/gemma-1.1-7b-it`. We provide: * A script to perform Supervised Fine-Tuning (SFT) on UltraChat dataset using QLoRA * A script to perform SFT using FSDP on TPU devices * A notebook that you can run on a free-tier Google Colab instance to perform SFT on the English quotes dataset ### Inputs and outputs * **Input:** Text string, such as a question, a prompt, or a document to be summarized. * **Output:** Generated English-language text in response to the input, such as an answer to a question, or a summary of a document. ## Model Data Data used for model training and how the data was processed. ### Training Dataset These models were trained on a dataset of text data that includes a wide variety of sources, totaling 6 trillion tokens. Here are the key components: * Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content. * Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions. * Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries. The combination of these diverse data sources is crucial for training a powerful language model that can handle a wide variety of different tasks and text formats. ### Data Preprocessing Here are the key data cleaning and filtering methods applied to the training data: * CSAM Filtering: Rigorous CSAM (Child Sexual Abuse Material) filtering was applied at multiple stages in the data preparation process to ensure the exclusion of harmful and illegal content * Sensitive Data Filtering: As part of making Gemma pre-trained models safe and reliable, automated techniques were used to filter out certain personal information and other sensitive data from training sets. * Additional methods: Filtering based on content quality and safely in line with [our policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11). ## Implementation Information Details about the model internals. ### Hardware Gemma was trained using the latest generation of [Tensor Processing Unit (TPU)](https://cloud.google.com/tpu/docs/intro-to-tpu) hardware (TPUv5e). Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain: * Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs. * Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality. * Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing. * Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training. * These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/). ### Software Training was done using [JAX](https://github.com/google/jax) and [ML Pathways](https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/ml-pathways). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. ML Pathways is Google's latest effort to build artificially intelligent systems capable of generalizing across multiple tasks. This is specially suitable for [foundation models](https://ai.google/discover/foundation-models/), including large language models like these ones. Together, JAX and ML Pathways are used as described in the [paper about the Gemini family of models](https://arxiv.org/abs/2312.11805); "the 'single controller' programming model of Jax and Pathways allows a single Python process to orchestrate the entire training run, dramatically simplifying the development workflow." ## Evaluation Model evaluation metrics and results. ### Benchmark Results The pre-trained base models were evaluated against a large collection of different datasets and metrics to cover different aspects of text generation: | Benchmark | Metric | 2B Params | 7B Params | | ------------------------------ | ------------- | ----------- | --------- | | [MMLU](https://arxiv.org/abs/2009.03300) | 5-shot, top-1 | 42.3 | 64.3 | | [HellaSwag](https://arxiv.org/abs/1905.07830) | 0-shot |71.4 | 81.2 | | [PIQA](https://arxiv.org/abs/1911.11641) | 0-shot | 77.3 | 81.2 | | [SocialIQA](https://arxiv.org/abs/1904.09728) | 0-shot | 49.7 | 51.8 | | [BooIQ](https://arxiv.org/abs/1905.10044) | 0-shot | 69.4 | 83.2 | | [WinoGrande](https://arxiv.org/abs/1907.10641) | partial score | 65.4 | 72.3 | | [CommonsenseQA](https://arxiv.org/abs/1811.00937) | 7-shot | 65.3 | 71.3 | | [OpenBookQA](https://arxiv.org/abs/1809.02789) | | 47.8 | 52.8 | | [ARC-e](https://arxiv.org/abs/1911.01547) | | 73.2 | 81.5 | | [ARC-c](https://arxiv.org/abs/1911.01547) | | 42.1 | 53.2 | | [TriviaQA](https://arxiv.org/abs/1705.03551) | 5-shot | 53.2 | 63.4 | | [Natural Questions](https://github.com/google-research-datasets/natural-questions) | 5-shot | 12.5 | 23 | | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 22.0 | 32.3 | | [MBPP](https://arxiv.org/abs/2108.07732) | 3-shot | 29.2 | 44.4 | | [GSM8K](https://arxiv.org/abs/2110.14168) | maj@1 | 17.7 | 46.4 | | [MATH](https://arxiv.org/abs/2108.07732) | 4-shot | 11.8 | 24.3 | | [AGIEval](https://arxiv.org/abs/2304.06364) | | 24.2 | 41.7 | | [BIG-Bench](https://arxiv.org/abs/2206.04615) | | 35.2 | 55.1 | | ------------------------------ | ------------- | ----------- | --------- | | **Average** | | **45.0** | **56.9** | ## Ethics and Safety Ethics and safety evaluation approach and results. ### Evaluation Approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Text-to-Text Content Safety: Human evaluation on prompts covering safety policies including child sexual abuse and exploitation, harassment, violence and gore, and hate speech. * Text-to-Text Representational Harms: Benchmark against relevant academic datasets such as [WinoBias](https://arxiv.org/abs/1804.06876) and [BBQ Dataset](https://arxiv.org/abs/2110.08193v2). * Memorization: Automated evaluation of memorization of training data, including the risk of personally identifiable information exposure. * Large-scale harm: Tests for "dangerous capabilities," such as chemical, biological, radiological, and nuclear (CBRN) risks. ### Evaluation Results The results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety, representational harms, memorization, large-scale harms. On top of robust internal evaluations, the results of well known safety benchmarks like BBQ, BOLD, Winogender, Winobias, RealToxicity, and TruthfulQA are shown here. #### Gemma 1.0 | Benchmark | Metric | Gemma 1.0 IT 2B | Gemma 1.0 IT 7B | | ------------------------ | ------------- | --------------- | --------------- | | [RealToxicity][realtox] | average | 6.86 | 7.90 | | [BOLD][bold] | | 45.57 | 49.08 | | [CrowS-Pairs][crows] | top-1 | 45.82 | 51.33 | | [BBQ Ambig][bbq] | 1-shot, top-1 | 62.58 | 92.54 | | [BBQ Disambig][bbq] | top-1 | 54.62 | 71.99 | | [Winogender][winogender] | top-1 | 51.25 | 54.17 | | [TruthfulQA][truthfulqa] | | 44.84 | 31.81 | | [Winobias 1_2][winobias] | | 56.12 | 59.09 | | [Winobias 2_2][winobias] | | 91.10 | 92.23 | | [Toxigen][toxigen] | | 29.77 | 39.59 | | ------------------------ | ------------- | --------------- | --------------- | #### Gemma 1.1 | Benchmark | Metric | Gemma 1.1 IT 2B | Gemma 1.1 IT 7B | | ------------------------ | ------------- | --------------- | --------------- | | [RealToxicity][realtox] | average | 7.03 | 8.04 | | [BOLD][bold] | | 47.76 | | | [CrowS-Pairs][crows] | top-1 | 45.89 | 49.67 | | [BBQ Ambig][bbq] | 1-shot, top-1 | 58.97 | 86.06 | | [BBQ Disambig][bbq] | top-1 | 53.90 | 85.08 | | [Winogender][winogender] | top-1 | 50.14 | 57.64 | | [TruthfulQA][truthfulqa] | | 44.24 | 45.34 | | [Winobias 1_2][winobias] | | 55.93 | 59.22 | | [Winobias 2_2][winobias] | | 89.46 | 89.2 | | [Toxigen][toxigen] | | 29.64 | 38.75 | | ------------------------ | ------------- | --------------- | --------------- | ## Usage and Limitations These models have certain limitations that users should be aware of. ### Intended Usage Open Large Language Models (LLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. * Content Creation and Communication * Text Generation: These models can be used to generate creative text formats such as poems, scripts, code, marketing copy, and email drafts. * Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications. * Text Summarization: Generate concise summaries of a text corpus, research papers, or reports. * Research and Education * Natural Language Processing (NLP) Research: These models can serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field. * Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice. * Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics. ### Limitations * Training Data * The quality and diversity of the training data significantly influence the model's capabilities. Biases or gaps in the training data can lead to limitations in the model's responses. * The scope of the training dataset determines the subject areas the model can handle effectively. * Context and Task Complexity * LLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * A model's performance can be influenced by the amount of context provided (longer context generally leads to better outputs, up to a certain point). * Language Ambiguity and Nuance * Natural language is inherently complex. LLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * Factual Accuracy * LLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * Common Sense * LLMs rely on statistical patterns in language. They might lack the ability to apply common sense reasoning in certain situations. ### Ethical Considerations and Risks The development of large language models (LLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * LLMs trained on large-scale, real-world text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * LLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible). * Transparency and Accountability: * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making LLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * Perpetuation of biases: It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * Generation of harmful content: Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * Misuse for malicious purposes: Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * Privacy violations: Models were trained on data filtered for removal of PII (Personally Identifiable Information). Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Benefits At the time of release, this family of models provides high-performance open large language model implementations designed from the ground up for Responsible AI development compared to similarly sized models. Using the benchmark evaluation metrics described in this document, these models have shown to provide superior performance to other, comparably-sized open model alternatives.

mohitsha/tiny-random-testing-bert2gpt2

mohitsha

transformers

text2text-generation

Entry not found

cross-encoder/stsb-distilroberta-base

cross-encoder

transformers

text-classification

--- license: apache-2.0 --- # Cross-Encoder for Quora Duplicate Questions Detection This model was trained using [SentenceTransformers](https://sbert.net) [Cross-Encoder](https://www.sbert.net/examples/applications/cross-encoder/README.html) class. ## Training Data This model was trained on the [STS benchmark dataset](http://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). The model will predict a score between 0 and 1 how for the semantic similarity of two sentences. ## Usage and Performance Pre-trained models can be used like this: ``` from sentence_transformers import CrossEncoder model = CrossEncoder('model_name') scores = model.predict([('Sentence 1', 'Sentence 2'), ('Sentence 3', 'Sentence 4')]) ``` The model will predict scores for the pairs `('Sentence 1', 'Sentence 2')` and `('Sentence 3', 'Sentence 4')`. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

DionTimmer/controlnet_qrcode-control_v1p_sd15

DionTimmer

diffusers

image-to-image

--- tags: - stable-diffusion - controlnet - image-to-image license: openrail++ language: - en library_name: diffusers pipeline_tag: image-to-image --- # QR Code Conditioned ControlNet Models for Stable Diffusion 1.5  ## Model Description This repo holds the safetensors & diffusers versions of the QR code conditioned ControlNet for Stable Diffusion v1.5. The Stable Diffusion 2.1 version is marginally more effective, as it was developed to address my specific needs. However, this 1.5 version model was also trained on the same dataset for those who are using the older version. ## How to use with Diffusers ```bash pip -q install diffusers transformers accelerate torch xformers ``` ```python import torch from PIL import Image from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler from diffusers.utils import load_image controlnet = ControlNetModel.from_pretrained("DionTimmer/controlnet_qrcode-control_v1p_sd15", torch_dtype=torch.float16) pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16 ) pipe.enable_xformers_memory_efficient_attention() pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() def resize_for_condition_image(input_image: Image, resolution: int): input_image = input_image.convert("RGB") W, H = input_image.size k = float(resolution) / min(H, W) H *= k W *= k H = int(round(H / 64.0)) * 64 W = int(round(W / 64.0)) * 64 img = input_image.resize((W, H), resample=Image.LANCZOS) return img # play with guidance_scale, controlnet_conditioning_scale and strength to make a valid QR Code Image # qr code image source_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/6064e095abd8d3692e3e2ed6/A_RqHaAM6YHBodPLwqtjn.png") # initial image, anything init_image = load_image("https://s3.amazonaws.com/moonup/production/uploads/noauth/KfMBABpOwIuNolv1pe3qX.jpeg") condition_image = resize_for_condition_image(source_image, 768) init_image = resize_for_condition_image(init_image, 768) generator = torch.manual_seed(123121231) image = pipe(prompt="a bilboard in NYC with a qrcode", negative_prompt="ugly, disfigured, low quality, blurry, nsfw", image=init_image, control_image=condition_image, width=768, height=768, guidance_scale=20, controlnet_conditioning_scale=1.5, generator=generator, strength=0.9, num_inference_steps=150, ) image.images[0] ``` ## Performance and Limitations These models perform quite well in most cases, but please note that they are not 100% accurate. In some instances, the QR code shape might not come through as expected. You can increase the ControlNet weight to emphasize the QR code shape. However, be cautious as this might negatively impact the style of your output.**To optimize for scanning, please generate your QR codes with correction mode 'H' (30%).** To balance between style and shape, a gentle fine-tuning of the control weight might be required based on the individual input and the desired output, aswell as the correct prompt. Some prompts do not work until you increase the weight by a lot. The process of finding the right balance between these factors is part art and part science. For the best results, it is recommended to generate your artwork at a resolution of 768. This allows for a higher level of detail in the final product, enhancing the quality and effectiveness of the QR code-based artwork. ## Installation The simplest way to use this is to place the .safetensors model and its .yaml config file in the folder where your other controlnet models are installed, which varies per application. For usage in auto1111 they can be placed in the webui/models/ControlNet folder. They can be loaded using the controlnet webui extension which you can install through the extensions tab in the webui (https://github.com/Mikubill/sd-webui-controlnet). Make sure to enable your controlnet unit and set your input image as the QR code. Set the model to either the SD2.1 or 1.5 version depending on your base stable diffusion model, or it will error. No pre-processor is needed, though you can use the invert pre-processor for a different variation of results. 768 is the preferred resolution for generation since it allows for more detail. Make sure to look up additional info on how to use controlnet if you get stuck, once you have the webui up and running its really easy to install the controlnet extension aswell.

vinai/xphonebert-base

vinai

transformers

fill-mask

# <a name="introduction"></a> XPhoneBERT : A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech XPhoneBERT is the first pre-trained multilingual model for phoneme representations for text-to-speech(TTS). XPhoneBERT has the same model architecture as BERT-base, trained using the RoBERTa pre-training approach on 330M phoneme-level sentences from nearly 100 languages and locales. Experimental results show that employing XPhoneBERT as an input phoneme encoder significantly boosts the performance of a strong neural TTS model in terms of naturalness and prosody and also helps produce fairly high-quality speech with limited training data. The general architecture and experimental results of XPhoneBERT can be found in [our INTERSPEECH 2023 paper](https://www.doi.org/10.21437/Interspeech.2023-444): @inproceedings{xphonebert, title = {{XPhoneBERT: A Pre-trained Multilingual Model for Phoneme Representations for Text-to-Speech}}, author = {Linh The Nguyen and Thinh Pham and Dat Quoc Nguyen}, booktitle = {Proceedings of the 24th Annual Conference of the International Speech Communication Association (INTERSPEECH)}, year = {2023}, pages = {5506--5510} } **Please CITE** our paper when XPhoneBERT is used to help produce published results or is incorporated into other software. For further information or requests, please go to [XPhoneBERT's homepage](https://github.com/VinAIResearch/XPhoneBERT)! ## <a name="transformers"></a> Using XPhoneBERT with `transformers` ### Installation <a name="install2"></a> - Install `transformers` with pip: `pip install transformers`, or install `transformers` [from source](https://huggingface.co/docs/transformers/installation#installing-from-source). - Install `text2phonemesequence`: `pip install text2phonemesequence` <br> Our [`text2phonemesequence`](https://github.com/thelinhbkhn2014/Text2PhonemeSequence) package is to convert text sequences into phoneme-level sequences, employed to construct our multilingual phoneme-level pre-training data. We build `text2phonemesequence` by incorporating the [CharsiuG2P](https://github.com/lingjzhu/CharsiuG2P/tree/main) and the [segments](https://pypi.org/project/segments/) toolkits that perform text-to-phoneme conversion and phoneme segmentation, respectively. - **Notes** - Initializing `text2phonemesequence` for each language requires its corresponding ISO 639-3 code. The ISO 639-3 codes of supported languages are available at [HERE](https://github.com/VinAIResearch/XPhoneBERT/blob/main/LanguageISO639-3Codes.md). - `text2phonemesequence` takes a word-segmented sequence as input. And users might also perform text normalization on the word-segmented sequence before feeding into `text2phonemesequence`. When creating our pre-training data, we perform word and sentence segmentation on all text documents in each language by using the [spaCy](https://spacy.io) toolkit, except for Vietnamese where we employ the [VnCoreNLP](https://github.com/vncorenlp/VnCoreNLP) toolkit. We also use the text normalization component from the [NVIDIA NeMo toolkit](https://github.com/NVIDIA/NeMo) for English, German, Spanish and Chinese, and the [Vinorm](https://github.com/v-nhandt21/Vinorm) text normalization package for Vietnamese. ### <a name="models2"></a> Pre-trained model Model | #params | Arch. | Max length | Pre-training data ---|---|---|---|--- `vinai/xphonebert-base` | 88M | base | 512 | 330M phoneme-level sentences from nearly 100 languages and locales ### Example usage <a name="usage2"></a> ```python from transformers import AutoModel, AutoTokenizer from text2phonemesequence import Text2PhonemeSequence # Load XPhoneBERT model and its tokenizer xphonebert = AutoModel.from_pretrained("vinai/xphonebert-base") tokenizer = AutoTokenizer.from_pretrained("vinai/xphonebert-base") # Load Text2PhonemeSequence # text2phone_model = Text2PhonemeSequence(language='eng-us', is_cuda=True) text2phone_model = Text2PhonemeSequence(language='jpn', is_cuda=True) # Input sequence that is already WORD-SEGMENTED (and text-normalized if applicable) # sentence = "That is , it is a testing text ." sentence = "これ は 、 テスト テキスト です ." input_phonemes = text2phone_model.infer_sentence(sentence) input_ids = tokenizer(input_phonemes, return_tensors="pt") with torch.no_grad(): features = xphonebert(**input_ids) ```

InstantX/InstantID

InstantX

diffusers

text-to-image

--- license: apache-2.0 language: - en library_name: diffusers pipeline_tag: text-to-image --- # InstantID Model Card <div align="center"> [**Project Page**](https://instantid.github.io/) **|** [**Paper**](https://arxiv.org/abs/2401.07519) **|** [**Code**](https://github.com/InstantID/InstantID) **|** [🤗 **Gradio demo**](https://huggingface.co/spaces/InstantX/InstantID) </div> ## Introduction InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. <div align="center"> <img src='examples/applications.png'> </div> ## Usage You can directly download the model in this repository. You also can download the model in python script: ```python from huggingface_hub import hf_hub_download hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/config.json", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ControlNetModel/diffusion_pytorch_model.safetensors", local_dir="./checkpoints") hf_hub_download(repo_id="InstantX/InstantID", filename="ip-adapter.bin", local_dir="./checkpoints") ``` For face encoder, you need to manutally download via this [URL](https://github.com/deepinsight/insightface/issues/1896#issuecomment-1023867304) to `models/antelopev2`. ```python # !pip install opencv-python transformers accelerate insightface import diffusers from diffusers.utils import load_image from diffusers.models import ControlNetModel import cv2 import torch import numpy as np from PIL import Image from insightface.app import FaceAnalysis from pipeline_stable_diffusion_xl_instantid import StableDiffusionXLInstantIDPipeline, draw_kps # prepare 'antelopev2' under ./models app = FaceAnalysis(name='antelopev2', root='./', providers=['CUDAExecutionProvider', 'CPUExecutionProvider']) app.prepare(ctx_id=0, det_size=(640, 640)) # prepare models under ./checkpoints face_adapter = f'./checkpoints/ip-adapter.bin' controlnet_path = f'./checkpoints/ControlNetModel' # load IdentityNet controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16) pipe = StableDiffusionXLInstantIDPipeline.from_pretrained( ... "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet, torch_dtype=torch.float16 ... ) pipe.cuda() # load adapter pipe.load_ip_adapter_instantid(face_adapter) ``` Then, you can customized your own face images ```python # load an image image = load_image("your-example.jpg") # prepare face emb face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR)) face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face face_emb = face_info['embedding'] face_kps = draw_kps(face_image, face_info['kps']) pipe.set_ip_adapter_scale(0.8) prompt = "analog film photo of a man. faded film, desaturated, 35mm photo, grainy, vignette, vintage, Kodachrome, Lomography, stained, highly detailed, found footage, masterpiece, best quality" negative_prompt = "(lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured (lowres, low quality, worst quality:1.2), (text:1.2), watermark, painting, drawing, illustration, glitch,deformed, mutated, cross-eyed, ugly, disfigured" # generate image image = pipe( ... prompt, image_embeds=face_emb, image=face_kps, controlnet_conditioning_scale=0.8 ... ).images[0] ``` For more details, please follow the instructions in our [GitHub repository](https://github.com/InstantID/InstantID). ## Usage Tips 1. If you're not satisfied with the similarity, try to increase the weight of "IdentityNet Strength" and "Adapter Strength". 2. If you feel that the saturation is too high, first decrease the Adapter strength. If it is still too high, then decrease the IdentityNet strength. 3. If you find that text control is not as expected, decrease Adapter strength. 4. If you find that realistic style is not good enough, go for our Github repo and use a more realistic base model. ## Demos <div align="center"> <img src='examples/0.png'> </div> <div align="center"> <img src='examples/1.png'> </div> ## Disclaimer This project is released under Apache License and aims to positively impact the field of AI-driven image generation. Users are granted the freedom to create images using this tool, but they are obligated to comply with local laws and utilize it responsibly. The developers will not assume any responsibility for potential misuse by users. ## Citation ```bibtex @article{wang2024instantid, title={InstantID: Zero-shot Identity-Preserving Generation in Seconds}, author={Wang, Qixun and Bai, Xu and Wang, Haofan and Qin, Zekui and Chen, Anthony}, journal={arXiv preprint arXiv:2401.07519}, year={2024} } ```

Hate-speech-CNERG/english-abusive-MuRIL

Hate-speech-CNERG

transformers

text-classification

--- language: en license: afl-3.0 --- This model is used detecting **abusive speech** in **English**. It is finetuned on MuRIL model using English abusive speech dataset. The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive) LABEL_0 :-> Normal LABEL_1 :-> Abusive ### For more details about our paper Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022. ***Please cite our paper in any published work that uses any of these resources.*** ~~~ @article{das2022data, title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages}, author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh}, journal={arXiv preprint arXiv:2204.12543}, year={2022} } ~~~

Qwen/Qwen2-7B-Instruct

Qwen

transformers

text-generation

--- license: apache-2.0 language: - en pipeline_tag: text-generation tags: - chat --- # Qwen2-7B-Instruct ## Introduction Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model. Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2/), [GitHub](https://github.com/QwenLM/Qwen2), and [Documentation](https://qwen.readthedocs.io/en/latest/). <br> ## Model Details Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. ## Training details We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization. ## Requirements The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2-7B-Instruct", torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct") prompt = "Give me a short introduction to large language model." messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(device) generated_ids = model.generate( model_inputs.input_ids, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts To handle extensive inputs exceeding 32,768 tokens, we utilize [YARN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For deployment, we recommend using vLLM. You can enable the long-context capabilities by following these steps: 1. **Install vLLM**: You can install vLLM by running the following command. ```bash pip install "vllm>=0.4.3" ``` Or you can install vLLM from [source](https://github.com/vllm-project/vllm/). 2. **Configure Model Settings**: After downloading the model weights, modify the `config.json` file by including the below snippet: ```json { "architectures": [ "Qwen2ForCausalLM" ], // ... "vocab_size": 152064, // adding the following snippets "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` This snippet enable YARN to support longer contexts. 3. **Model Deployment**: Utilize vLLM to deploy your model. For instance, you can set up an openAI-like server using the command: ```bash python -m vllm.entrypoints.openai.api_server --served-model-name Qwen2-7B-Instruct --model path/to/weights ``` Then you can access the Chat API by: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "Qwen2-7B-Instruct", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Your Long Input Here."} ] }' ``` For further usage instructions of vLLM, please refer to our [Github](https://github.com/QwenLM/Qwen2). **Note**: Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation We briefly compare Qwen2-7B-Instruct with similar-sized instruction-tuned LLMs, including Qwen1.5-7B-Chat. The results are shown below: | Datasets | Llama-3-8B-Instruct | Yi-1.5-9B-Chat | GLM-4-9B-Chat | Qwen1.5-7B-Chat | Qwen2-7B-Instruct | | :--- | :---: | :---: | :---: | :---: | :---: | | _**English**_ | | | | | | | MMLU | 68.4 | 69.5 | **72.4** | 59.5 | 70.5 | | MMLU-Pro | 41.0 | - | - | 29.1 | **44.1** | | GPQA | **34.2** | - | **-** | 27.8 | 25.3 | | TheroemQA | 23.0 | - | - | 14.1 | **25.3** | | MT-Bench | 8.05 | 8.20 | 8.35 | 7.60 | **8.41** | | _**Coding**_ | | | | | | | Humaneval | 62.2 | 66.5 | 71.8 | 46.3 | **79.9** | | MBPP | **67.9** | - | - | 48.9 | 67.2 | | MultiPL-E | 48.5 | - | - | 27.2 | **59.1** | | Evalplus | 60.9 | - | - | 44.8 | **70.3** | | LiveCodeBench | 17.3 | - | - | 6.0 | **26.6** | | _**Mathematics**_ | | | | | | | GSM8K | 79.6 | **84.8** | 79.6 | 60.3 | 82.3 | | MATH | 30.0 | 47.7 | **50.6** | 23.2 | 49.6 | | _**Chinese**_ | | | | | | | C-Eval | 45.9 | - | 75.6 | 67.3 | **77.2** | | AlignBench | 6.20 | 6.90 | 7.01 | 6.20 | **7.21** | ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```

facebook/dpr-ctx_encoder-multiset-base

facebook

transformers

--- language: en license: cc-by-nc-4.0 tags: - dpr datasets: - nq_open inference: false --- # `dpr-ctx_encoder-multiset-base` ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** [Dense Passage Retrieval (DPR)](https://github.com/facebookresearch/DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-ctx_encoder-multiset-base` is the context encoder trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), and [CuratedTREC (TREC)](https://huggingface.co/datasets/trec). - **Developed by:** See [GitHub repo](https://github.com/facebookresearch/DPR) for model developers - **Model Type:** BERT-based encoder - **Language(s):** [CC-BY-NC-4.0](https://github.com/facebookresearch/DPR/blob/main/LICENSE), also see [Code of Conduct](https://github.com/facebookresearch/DPR/blob/main/CODE_OF_CONDUCT.md) - **License:** English - **Related Models:** - [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base) - [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base) - [`dpr-question-encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base) - [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base) - [`dpr-ctx_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/2004.04906) - [GitHub Repo](https://github.com/facebookresearch/DPR) - [Hugging Face DPR docs](https://huggingface.co/docs/transformers/main/en/model_doc/dpr) - [BERT Base Uncased Model Card](https://huggingface.co/bert-base-uncased) ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import DPRContextEncoder, DPRContextEncoderTokenizer tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") model = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"] embeddings = model(input_ids).pooler_output ``` ## Uses #### Direct Use `dpr-ctx_encoder-multiset-base`, [`dpr-question_encoder-multiset-base`](https://huggingface.co/facebook/dpr-question_encoder-multiset-base), and [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base) can be used for the task of open-domain question answering. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propogate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Training #### Training Data This model was trained using the following datasets: - **[Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open)** ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)) - **[TriviaQA](https://huggingface.co/datasets/trivia_qa)** ([Joshi et al., 2017](https://aclanthology.org/P17-1147/)) - **[WebQuestions (WQ)](https://huggingface.co/datasets/web_questions)** ([Berant et al., 2013](https://aclanthology.org/D13-1160/)) - **[CuratedTREC (TREC)](https://huggingface.co/datasets/trec)** ([Baudiš & Šedivý, 2015](https://www.aminer.cn/pub/599c7953601a182cd263079b/reading-wikipedia-to-answer-open-domain-questions)) #### Training Procedure The training procedure is described in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf): > Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run-time. > Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval. At run-time, DPR applies a different encoder EQ(·) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector. The authors report that for encoders, they used two independent BERT ([Devlin et al., 2019](https://aclanthology.org/N19-1423/)) networks (base, un-cased) and use FAISS ([Johnson et al., 2017](https://arxiv.org/abs/1702.08734)) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in-batch negatives. ## Evaluation The following evaluation information is extracted from the [associated paper](https://arxiv.org/pdf/2004.04906.pdf). #### Testing Data, Factors and Metrics The model developers report the performance of the model on five QA datasets, using the top-k accuracy (k ∈ {20, 100}). The datasets were [NQ](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), [CuratedTREC (TREC)](https://huggingface.co/datasets/trec), and [SQuAD v1.1](https://huggingface.co/datasets/squad). #### Results | | Top 20 | | | | | Top 100| | | | | |:----:|:------:|:---------:|:--:|:----:|:-----:|:------:|:---------:|:--:|:----:|:-----:| | | NQ | TriviaQA | WQ | TREC | SQuAD | NQ | TriviaQA | WQ | TREC | SQuAD | | | 79.4 | 78.8 |75.0| 89.1 | 51.6 | 86.0 | 84.7 |82.9| 93.9 | 67.6 | ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and based on the [associated paper](https://arxiv.org/abs/2004.04906). - **Hardware Type:** 8 32GB GPUs - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/abs/2004.04906) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @inproceedings{karpukhin-etal-2020-dense, title = "Dense Passage Retrieval for Open-Domain Question Answering", author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", doi = "10.18653/v1/2020.emnlp-main.550", pages = "6769--6781", } ``` ## Model Card Authors This model card was written by the team at Hugging Face.

urchade/gliner_base

urchade

gliner

token-classification

--- license: cc-by-nc-4.0 language: - en pipeline_tag: token-classification datasets: - Universal-NER/Pile-NER-type library_name: gliner --- # Model Card for GLiNER-base GLiNER is a Named Entity Recognition (NER) model capable of identifying any entity type using a bidirectional transformer encoder (BERT-like). It provides a practical alternative to traditional NER models, which are limited to predefined entities, and Large Language Models (LLMs) that, despite their flexibility, are costly and large for resource-constrained scenarios. ## Links * Paper: https://arxiv.org/abs/2311.08526 * Repository: https://github.com/urchade/GLiNER ## Available models | Release | Model Name | # of Parameters | Language | License | | - | - | - | - | - | | v0 | [urchade/gliner_base](https://huggingface.co/urchade/gliner_base)<br>[urchade/gliner_multi](https://huggingface.co/urchade/gliner_multi) | 209M<br>209M | English<br>Multilingual | cc-by-nc-4.0 | | v1 | [urchade/gliner_small-v1](https://huggingface.co/urchade/gliner_small-v1)<br>[urchade/gliner_medium-v1](https://huggingface.co/urchade/gliner_medium-v1)<br>[urchade/gliner_large-v1](https://huggingface.co/urchade/gliner_large-v1) | 166M<br>209M<br>459M | English <br> English <br> English | cc-by-nc-4.0 | | v2 | [urchade/gliner_small-v2](https://huggingface.co/urchade/gliner_small-v2)<br>[urchade/gliner_medium-v2](https://huggingface.co/urchade/gliner_medium-v2)<br>[urchade/gliner_large-v2](https://huggingface.co/urchade/gliner_large-v2) | 166M<br>209M<br>459M | English <br> English <br> English | apache-2.0 | | v2.1 | [urchade/gliner_small-v2.1](https://huggingface.co/urchade/gliner_small-v2.1)<br>[urchade/gliner_medium-v2.1](https://huggingface.co/urchade/gliner_medium-v2.1)<br>[urchade/gliner_large-v2.1](https://huggingface.co/urchade/gliner_large-v2.1) <br>[urchade/gliner_multi-v2.1](https://huggingface.co/urchade/gliner_multi-v2.1) | 166M<br>209M<br>459M<br>209M | English <br> English <br> English <br> Multilingual | apache-2.0 | ## Installation To use this model, you must install the GLiNER Python library: ``` !pip install gliner ``` ## Usage Once you've downloaded the GLiNER library, you can import the GLiNER class. You can then load this model using `GLiNER.from_pretrained` and predict entities with `predict_entities`. ```python from gliner import GLiNER model = GLiNER.from_pretrained("urchade/gliner_base") text = """ Cristiano Ronaldo dos Santos Aveiro (Portuguese pronunciation: [kɾiʃˈtjɐnu ʁɔˈnaldu]; born 5 February 1985) is a Portuguese professional footballer who plays as a forward for and captains both Saudi Pro League club Al Nassr and the Portugal national team. Widely regarded as one of the greatest players of all time, Ronaldo has won five Ballon d'Or awards,[note 3] a record three UEFA Men's Player of the Year Awards, and four European Golden Shoes, the most by a European player. He has won 33 trophies in his career, including seven league titles, five UEFA Champions Leagues, the UEFA European Championship and the UEFA Nations League. Ronaldo holds the records for most appearances (183), goals (140) and assists (42) in the Champions League, goals in the European Championship (14), international goals (128) and international appearances (205). He is one of the few players to have made over 1,200 professional career appearances, the most by an outfield player, and has scored over 850 official senior career goals for club and country, making him the top goalscorer of all time. """ labels = ["person", "award", "date", "competitions", "teams"] entities = model.predict_entities(text, labels) for entity in entities: print(entity["text"], "=>", entity["label"]) ``` ``` Cristiano Ronaldo dos Santos Aveiro => person 5 February 1985 => date Al Nassr => teams Portugal national team => teams Ballon d'Or => award UEFA Men's Player of the Year Awards => award European Golden Shoes => award UEFA Champions Leagues => competitions UEFA European Championship => competitions UEFA Nations League => competitions Champions League => competitions European Championship => competitions ``` ## Named Entity Recognition benchmark result  ## Model Authors The model authors are: * [Urchade Zaratiana](https://huggingface.co/urchade) * Nadi Tomeh * Pierre Holat * Thierry Charnois ## Citation ```bibtex @misc{zaratiana2023gliner, title={GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer}, author={Urchade Zaratiana and Nadi Tomeh and Pierre Holat and Thierry Charnois}, year={2023}, eprint={2311.08526}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

chavinlo/alpaca-native

chavinlo

transformers

text-generation

# Stanford Alpaca This is a replica of Alpaca by Stanford' tatsu Trained using the original instructions with a minor modification in FSDP mode # Other versions: 13B: https://huggingface.co/chavinlo/alpaca-13b 13B -> GPT4 : https://huggingface.co/chavinlo/gpt4-x-alpaca ## Compute Used Trained on 4xA100s for 6H Donated by redmond.ai NO LORA HAS BEEN USED, this is a natively-finetuned model, hence "alpaca-native" If you are interested on more llama-based models, you can check out my profile or search for other models at https://huggingface.co/models?other=llama This (MIGHT) be a quantized version of this model, but be careful: https://boards.4channel.org/g/thread/92173062#p92182396 CONFIGURATION (default except fsdp): ```shell torchrun --nproc_per_node=4 --master_port=3045 train.py \ --model_name_or_path /workspace/llama-7b-hf \ --data_path ./alpaca_data.json \ --bf16 True \ --output_dir /workspace/output \ --num_train_epochs 3 \ --per_device_train_batch_size 4 \ --per_device_eval_batch_size 4 \ --gradient_accumulation_steps 8 \ --evaluation_strategy "no" \ --save_strategy "steps" \ --save_steps 200 \ --save_total_limit 1 \ --learning_rate 2e-5 \ --weight_decay 0. \ --warmup_ratio 0.03 \ --lr_scheduler_type "cosine" \ --logging_steps 1 \ --fsdp "shard_grad_op auto_wrap" \ --fsdp_transformer_layer_cls_to_wrap 'LLaMADecoderLayer' \ --tf32 True --report_to="wandb" ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__alpaca-native) | Metric | Value | |-----------------------|---------------------------| | Avg. | 41.96 | | ARC (25-shot) | 52.3 | | HellaSwag (10-shot) | 77.09 | | MMLU (5-shot) | 41.6 | | TruthfulQA (0-shot) | 37.58 | | Winogrande (5-shot) | 69.46 | | GSM8K (5-shot) | 1.44 | | DROP (3-shot) | 14.23 |

mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF

mradermacher

transformers

--- base_model: Qwen/Qwen2-57B-A14B-Instruct language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - chat --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/Qwen/Qwen2-57B-A14B-Instruct **The Qwen2-57B models seem to be broken. I have tried my best, but they likely need to be fixed upstream first. You have been warned.** <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 21.2 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 22.3 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 23.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 25.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 25.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 25.3 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 27.6 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 29.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 30.8 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 32.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 32.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 35.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 39.7 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 40.9 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2-57B-A14B-Instruct-i1-GGUF/resolve/main/Qwen2-57B-A14B-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 47.2 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->

bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF

bartowski

text-generation

--- license: apache-2.0 base_model: 01-ai/Yi-1.5-34B-32k tags: - generated_from_trainer - axolotl datasets: - cognitivecomputations/Dolphin-2.9 - teknium/OpenHermes-2.5 - m-a-p/CodeFeedback-Filtered-Instruction - cognitivecomputations/dolphin-coder - cognitivecomputations/samantha-data - microsoft/orca-math-word-problems-200k - Locutusque/function-calling-chatml - internlm/Agent-FLAN quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of dolphin-2.9.3-Yi-1.5-34B-32k Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3197">b3197</a> for quantization. Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|im_start|> system {system_prompt}<|im_end|> <|im_start|> user {prompt}<|im_end|> <|im_start|> assistant ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_1.gguf) | Q8_0_L | 37.40GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf) | Q8_0 | 36.54GB | Extremely high quality, generally unneeded but max available quant. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K_L.gguf) | Q6_K_L | 29.29GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q6_K.gguf) | Q6_K | 28.21GB | Very high quality, near perfect, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_L.gguf) | Q5_K_L | 25.46GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_M.gguf) | Q5_K_M | 24.32GB | High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q5_K_S.gguf) | Q5_K_S | 23.70GB | High quality, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_L.gguf) | Q4_K_L | 21.85GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf) | Q4_K_M | 20.65GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_S.gguf) | Q4_K_S | 19.59GB | Slightly lower quality with more space savings, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ4_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ4_XS.gguf) | IQ4_XS | 18.47GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_XL.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF//main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_XL.gguf) | Q3_K_XL | | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_L.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_L.gguf) | Q3_K_L | 18.13GB | Lower quality but usable, good for low RAM availability. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_M.gguf) | Q3_K_M | 16.65GB | Even lower quality. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_M.gguf) | IQ3_M | 15.56GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q3_K_S.gguf) | Q3_K_S | 14.96GB | Low quality, not recommended. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XS.gguf) | IQ3_XS | 14.23GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XXS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ3_XXS.gguf) | IQ3_XXS | 13.33GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [dolphin-2.9.3-Yi-1.5-34B-32k-Q2_K.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-Q2_K.gguf) | Q2_K | 12.82GB | Very low quality but surprisingly usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_M.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_M.gguf) | IQ2_M | 11.79GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_S.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_S.gguf) | IQ2_S | 10.89GB | Very low quality, uses SOTA techniques to be usable. | | [dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_XS.gguf](https://huggingface.co/bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF/blob/main/dolphin-2.9.3-Yi-1.5-34B-32k-IQ2_XS.gguf) | IQ2_XS | 10.30GB | Very low quality, uses SOTA techniques to be usable. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF --include "dolphin-2.9.3-Yi-1.5-34B-32k-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF --include "dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0.gguf/*" --local-dir dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0 ``` You can either specify a new local-dir (dolphin-2.9.3-Yi-1.5-34B-32k-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

jbochi/madlad400-3b-mt

jbochi

transformers

translation

--- license: apache-2.0 language: - multilingual - en - ru - es - fr - de - it - pt - pl - nl - vi - tr - sv - id - ro - cs - zh - hu - ja - th - fi - fa - uk - da - el - "no" - bg - sk - ko - ar - lt - ca - sl - he - et - lv - hi - sq - ms - az - sr - ta - hr - kk - is - ml - mr - te - af - gl - fil - be - mk - eu - bn - ka - mn - bs - uz - ur - sw - yue - ne - kn - kaa - gu - si - cy - eo - la - hy - ky - tg - ga - mt - my - km - tt - so - ku - ps - pa - rw - lo - ha - dv - fy - lb - ckb - mg - gd - am - ug - ht - grc - hmn - sd - jv - mi - tk - ceb - yi - ba - fo - or - xh - su - kl - ny - sm - sn - co - zu - ig - yo - pap - st - haw - as - oc - cv - lus - tet - gsw - sah - br - rm - sa - bo - om - se - ce - cnh - ilo - hil - udm - os - lg - ti - vec - ts - tyv - kbd - ee - iba - av - kha - to - tn - nso - fj - zza - ak - ada - otq - dz - bua - cfm - ln - chm - gn - krc - wa - hif - yua - srn - war - rom - bik - pam - sg - lu - ady - kbp - syr - ltg - myv - iso - kac - bho - ay - kum - qu - za - pag - ngu - ve - pck - zap - tyz - hui - bbc - tzo - tiv - ksd - gom - min - ang - nhe - bgp - nzi - nnb - nv - zxx - bci - kv - new - mps - alt - meu - bew - fon - iu - abt - mgh - mnw - tvl - dov - tlh - ho - kw - mrj - meo - crh - mbt - emp - ace - ium - mam - gym - mai - crs - pon - ubu - fip - quc - gv - kj - btx - ape - chk - rcf - shn - tzh - mdf - ppk - ss - gag - cab - kri - seh - ibb - tbz - bru - enq - ach - cuk - kmb - wo - kek - qub - tab - bts - kos - rwo - cak - tuc - bum - cjk - gil - stq - tsg - quh - mak - arn - ban - jiv - sja - yap - tcy - toj - twu - xal - amu - rmc - hus - nia - kjh - bm - guh - mas - acf - dtp - ksw - bzj - din - zne - mad - msi - mag - mkn - kg - lhu - ch - qvi - mh - djk - sus - mfe - srm - dyu - ctu - gui - pau - inb - bi - mni - guc - jam - wal - jac - bas - gor - skr - nyu - noa - sda - gub - nog - cni - teo - tdx - sxn - rki - nr - frp - alz - taj - lrc - cce - rn - jvn - hvn - nij - dwr - izz - msm - bus - ktu - chr - maz - tzj - suz - knj - bim - gvl - bqc - tca - pis - prk - laj - mel - qxr - niq - ahk - shp - hne - spp - koi - krj - quf - luz - agr - tsc - mqy - gof - gbm - miq - dje - awa - bjj - qvz - sjp - tll - raj - kjg - bgz - quy - cbk - akb - oj - ify - mey - ks - cac - brx - qup - syl - jax - ff - ber - tks - trp - mrw - adh - smt - srr - ffm - qvc - mtr - ann - kaa - aa - noe - nut - gyn - kwi - xmm - msb library_name: transformers tags: - text2text-generation - text-generation-inference datasets: - allenai/MADLAD-400 pipeline_tag: translation widget: - text: "<2en> Como vai, amigo?" example_title: "Translation to English" - text: "<2de> Do you speak German?" example_title: "Translation to German" --- # Model Card for MADLAD-400-3B-MT # Table of Contents 0. [TL;DR](#TL;DR) 1. [Model Details](#model-details) 2. [Usage](#usage) 3. [Uses](#uses) 4. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 5. [Training Details](#training-details) 6. [Evaluation](#evaluation) 7. [Environmental Impact](#environmental-impact) 8. [Citation](#citation) # TL;DR MADLAD-400-3B-MT is a multilingual machine translation model based on the T5 architecture that was trained on 1 trillion tokens covering over 450 languages using publicly available data. It is competitive with models that are significantly larger. **Disclaimer**: [Juarez Bochi](https://huggingface.co/jbochi), who was not involved in this research, converted the original weights and wrote the contents of this model card based on the original paper and Flan-T5. # Model Details ## Model Description - **Model type:** Language model - **Language(s) (NLP):** Multilingual (400+ languages) - **License:** Apache 2.0 - **Related Models:** [All MADLAD-400 Checkpoints](https://huggingface.co/models?search=madlad) - **Original Checkpoints:** [All Original MADLAD-400 Checkpoints](https://github.com/google-research/google-research/tree/master/madlad_400) - **Resources for more information:** - [Research paper](https://arxiv.org/abs/2309.04662) - [GitHub Repo](https://github.com/google-research/t5x) - [Hugging Face MADLAD-400 Docs (Similar to T5) ](https://huggingface.co/docs/transformers/model_doc/MADLAD-400) - [Pending PR](https://github.com/huggingface/transformers/pull/27471) # Usage Find below some example scripts on how to use the model: ## Using the Pytorch model with `transformers` ### Running the model on a CPU or GPU <details> <summary> Click to expand </summary> First, install the Python packages that are required: `pip install transformers accelerate sentencepiece protobuf` ```python from transformers import T5ForConditionalGeneration, T5Tokenizer model_name = 'jbochi/madlad400-3b-mt' model = T5ForConditionalGeneration.from_pretrained(model_name, device_map="auto") tokenizer = T5Tokenizer.from_pretrained(model_name) text = "<2pt> I love pizza!" input_ids = tokenizer(text, return_tensors="pt").input_ids.to(model.device) outputs = model.generate(input_ids=input_ids) tokenizer.decode(outputs[0], skip_special_tokens=True) # Eu adoro pizza! ``` </details> ## Running the model with Candle <details> <summary> Click to expand </summary> Usage with [candle](https://github.com/huggingface/candle): ```bash $ cargo run --example t5 --release -- \ --model-id "jbochi/madlad400-3b-mt" \ --prompt "<2de> How are you, my friend?" \ --decode --temperature 0 ``` We also provide a quantized model (1.65 GB vs the original 11.8 GB file): ``` cargo run --example quantized-t5 --release -- \ --model-id "jbochi/madlad400-3b-mt" --weight-file "model-q4k.gguf" \ --prompt "<2de> How are you, my friend?" \ --temperature 0 ... Wie geht es dir, mein Freund? ``` </details> # Uses ## Direct Use and Downstream Use > Primary intended uses: Machine Translation and multilingual NLP tasks on over 400 languages. > Primary intended users: Research community. ## Out-of-Scope Use > These models are trained on general domain data and are therefore not meant to > work on domain-specific models out-of-the box. Moreover, these research models have not been assessed > for production usecases. # Bias, Risks, and Limitations > We note that we evaluate on only 204 of the languages supported by these models and on machine translation > and few-shot machine translation tasks. Users must consider use of this model carefully for their own > usecase. ## Ethical considerations and risks > We trained these models with MADLAD-400 and publicly available data to create baseline models that > support NLP for over 400 languages, with a focus on languages underrepresented in large-scale corpora. > Given that these models were trained with web-crawled datasets that may contain sensitive, offensive or > otherwise low-quality content despite extensive preprocessing, it is still possible that these issues to the > underlying training data may cause differences in model performance and toxic (or otherwise problematic) > output for certain domains. Moreover, large models are dual use technologies that have specific risks > associated with their use and development. We point the reader to surveys such as those written by > Weidinger et al. or Bommasani et al. for a more detailed discussion of these risks, and to Liebling > et al. for a thorough discussion of the risks of machine translation systems. ## Known Limitations More information needed ## Sensitive Use: More information needed # Training Details > We train models of various sizes: a 3B, 32-layer parameter model, > a 7.2B 48-layer parameter model and a 10.7B 32-layer parameter model. > We share all parameters of the model across language pairs, > and use a Sentence Piece Model with 256k tokens shared on both the encoder and decoder > side. Each input sentence has a <2xx> token prepended to the source sentence to indicate the target > language. See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. ## Training Data > For both the machine translation and language model, MADLAD-400 is used. For the machine translation > model, a combination of parallel datasources covering 157 languages is also used. Further details are > described in the [paper](https://arxiv.org/pdf/2309.04662.pdf). ## Training Procedure See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. # Evaluation ## Testing Data, Factors & Metrics > For evaluation, we used WMT, NTREX, Flores-200 and Gatones datasets as described in Section 4.3 in the [paper](https://arxiv.org/pdf/2309.04662.pdf). > The translation quality of this model varies based on language, as seen in the paper, and likely varies on > domain, though we have not assessed this. ## Results    See the [research paper](https://arxiv.org/pdf/2309.04662.pdf) for further details. # Environmental Impact More information needed # Citation **BibTeX:** ```bibtex @misc{kudugunta2023madlad400, title={MADLAD-400: A Multilingual And Document-Level Large Audited Dataset}, author={Sneha Kudugunta and Isaac Caswell and Biao Zhang and Xavier Garcia and Christopher A. Choquette-Choo and Katherine Lee and Derrick Xin and Aditya Kusupati and Romi Stella and Ankur Bapna and Orhan Firat}, year={2023}, eprint={2309.04662}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

TencentARC/InstantMesh

TencentARC

diffusers

image-to-3d

--- license: apache-2.0 tags: - image-to-3d --- # InstantMesh Model card for *InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models*. Code: https://github.com/TencentARC/InstantMesh Arxiv: https://arxiv.org/abs/2404.07191 We present InstantMesh, a feed-forward framework for instant 3D mesh generation from a single image, featuring state-of-the-art generation quality and significant training scalability. By synergizing the strengths of an off-the-shelf multiview diffusion model and a sparse-view reconstruction model based on the LRM architecture, InstantMesh is able to create diverse 3D assets within 10 seconds. To enhance the training efficiency and exploit more geometric supervisions, e.g., depths and normals, we integrate a differentiable iso-surface extraction module into our framework and directly optimize on the mesh representation. Experimental results on public datasets demonstrate that InstantMesh significantly outperforms other latest image-to-3D baselines, both qualitatively and quantitatively. We release all the code, weights, and demo of InstantMesh, with the intention that it can make substantial contributions to the community of 3D generative AI and empower both researchers and content creators.

meta-llama/Llama-2-70b-chat-hf

meta-llama

transformers

text-generation

--- extra_gated_heading: You need to share contact information with Meta to access this model extra_gated_prompt: >- ### LLAMA 2 COMMUNITY LICENSE AGREEMENT "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Llama 2 distributed by Meta at https://ai.meta.com/resources/models-and-libraries/llama-downloads/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity's behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. 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You agree you will not use, or allow others to use, Llama 2 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama 2 Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 2 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Llama 2 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Llama 2 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama: [LlamaUseReport@meta.com](mailto:LlamaUseReport@meta.com) extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: >- The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-2 license: llama2 --- # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)| |70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)|

benjamin/wtp-canine-s-12l

benjamin

transformers

token-classification

--- license: mit language: - multilingual - am - ar - az - be - bg - bn - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hu - hy - id - ig - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lt - lv - mg - mk - ml - mn - mr - ms - mt - my - ne - nl - no - pa - pl - ps - pt - ro - ru - si - sk - sl - sq - sr - sv - ta - te - tg - th - tr - uk - ur - uz - vi - xh - yi - yo - zh - zu --- # wtp-canine-s-12l Model for [`wtpsplit`](https://github.com/bminixhofer/wtpsplit).

dbmdz/bert-base-french-europeana-cased

dbmdz

transformers

--- language: fr license: mit tags: - "historic french" --- # 🤗 + 📚 dbmdz BERT model In this repository the MDZ Digital Library team (dbmdz) at the Bavarian State Library open sources French Europeana BERT models 🎉 # French Europeana BERT We extracted all French texts using the `language` metadata attribute from the Europeana corpus. The resulting corpus has a size of 63GB and consists of 11,052,528,456 tokens. Based on the metadata information, texts from the 18th - 20th century are mainly included in the training corpus. Detailed information about the data and pretraining steps can be found in [this repository](https://github.com/stefan-it/europeana-bert). ## Model weights BERT model weights for PyTorch and TensorFlow are available. * French Europeana BERT: `dbmdz/bert-base-french-europeana-cased` - [model hub page](https://huggingface.co/dbmdz/bert-base-french-europeana-cased/tree/main) ## Results For results on Historic NER, please refer to [this repository](https://github.com/stefan-it/europeana-bert). ## Usage With Transformers >= 2.3 our French Europeana BERT model can be loaded like: ```python from transformers import AutoModel, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-french-europeana-cased") model = AutoModel.from_pretrained("dbmdz/bert-base-french-europeana-cased") ``` # Huggingface model hub All models are available on the [Huggingface model hub](https://huggingface.co/dbmdz). # Contact (Bugs, Feedback, Contribution and more) For questions about our BERT model just open an issue [here](https://github.com/dbmdz/berts/issues/new) 🤗 # Acknowledgments Research supported with Cloud TPUs from Google's TensorFlow Research Cloud (TFRC). Thanks for providing access to the TFRC ❤️ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team, it is possible to download our model from their S3 storage 🤗

google/paligemma-3b-mix-224

google

transformers

image-text-to-text

--- license: gemma library_name: transformers extra_gated_heading: Access PaliGemma on Hugging Face extra_gated_prompt: To access PaliGemma on Hugging Face, you’re required to review and agree to Google’s usage license. To do this, please ensure you’re logged-in to Hugging Face and click below. Requests are processed immediately. extra_gated_button_content: Acknowledge license pipeline_tag: image-text-to-text --- # PaliGemma model card **Model page:** [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) Transformers PaliGemma 3B weights, fine-tuned with 224*224 input images and 256 token input/output text sequences on a mixture of downstream academic datasets. The models are available in float32, bfloat16 and float16 format for research purposes only. **Resources and technical documentation:** * [Responsible Generative AI Toolkit](https://ai.google.dev/responsible) * [PaliGemma on Kaggle](https://www.kaggle.com/models/google/paligemma) * [PaliGemma on Vertex Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/363) **Terms of Use:** [Terms](https://ai.google.dev/gemma/terms) **Authors:** Google ## Model information ### Model summary #### Description PaliGemma is a versatile and lightweight vision-language model (VLM) inspired by [PaLI-3](https://arxiv.org/abs/2310.09199) and based on open components such as the [SigLIP vision model](https://arxiv.org/abs/2303.15343) and the [Gemma language model](https://arxiv.org/abs/2403.08295). It takes both image and text as input and generates text as output, supporting multiple languages. It is designed for class-leading fine-tune performance on a wide range of vision-language tasks such as image and short video caption, visual question answering, text reading, object detection and object segmentation. #### Model architecture PaliGemma is the composition of a [Transformer decoder](https://arxiv.org/abs/1706.03762) and a [Vision Transformer image encoder](https://arxiv.org/abs/2010.11929), with a total of 3 billion params. The text decoder is initialized from [Gemma-2B](https://www.kaggle.com/models/google/gemma). The image encoder is initialized from [SigLIP-So400m/14](https://colab.research.google.com/github/google-research/big_vision/blob/main/big_vision/configs/proj/image_text/SigLIP_demo.ipynb). PaliGemma is trained following the PaLI-3 recipes. #### Inputs and outputs * **Input:** Image and text string, such as a prompt to caption the image, or a question. * **Output:** Generated text in response to the input, such as a caption of the image, an answer to a question, a list of object bounding box coordinates, or segmentation codewords. ### Model data #### Pre-train datasets PaliGemma is pre-trained on the following mixture of datasets: * **WebLI:** [WebLI (Web Language Image)](https://arxiv.org/abs/2209.06794) is a web-scale multilingual image-text dataset built from the public web. A wide range of WebLI splits are used to acquire versatile model capabilities, such as visual semantic understanding, object localization, visually-situated text understanding, multilinguality, etc. * **CC3M-35L:** Curated English image-alt_text pairs from webpages ([Sharma et al., 2018](https://aclanthology.org/P18-1238/)). We used the [Google Cloud Translation API](https://cloud.google.com/translate) to translate into 34 additional languages. * **VQ²A-CC3M-35L/VQG-CC3M-35L:** A subset of VQ2A-CC3M ([Changpinyo et al., 2022a](https://aclanthology.org/2022.naacl-main.142/)), translated into the same additional 34 languages as CC3M-35L, using the [Google Cloud Translation API](https://cloud.google.com/translate). * **OpenImages:** Detection and object-aware questions and answers ([Piergiovanni et al. 2022](https://arxiv.org/abs/2209.04372)) generated by handcrafted rules on the [OpenImages dataset]. * **WIT:** Images and texts collected from Wikipedia ([Srinivasan et al., 2021](https://arxiv.org/abs/2103.01913)). [OpenImages dataset]: https://storage.googleapis.com/openimages/web/factsfigures_v7.html #### Data responsibility filtering The following filters are applied to WebLI, with the goal of training PaliGemma on clean data: * **Pornographic image filtering:** This filter removes images deemed to be of pornographic nature. * **Text safety filtering:** We identify and filter out images that are paired with unsafe text. Unsafe text is any text deemed to contain or be about CSAI, pornography, vulgarities, or otherwise offensive. * **Text toxicity filtering:** We further use the [Perspective API](https://perspectiveapi.com/) to identify and filter out images that are paired with text deemed insulting, obscene, hateful or otherwise toxic. * **Text personal information filtering:** We filtered certain personal information and other sensitive data using [Cloud Data Loss Prevention (DLP) API](https://cloud.google.com/security/products/dlp) to protect the privacy of individuals. Identifiers such as social security numbers and [other sensitive information types] were removed. * **Additional methods:** Filtering based on content quality and safety in line with our policies and practices. [other sensitive information types]: https://cloud.google.com/sensitive-data-protection/docs/high-sensitivity-infotypes-reference?_gl=1*jg604m*_ga*ODk5MzA3ODQyLjE3MTAzMzQ3NTk.*_ga_WH2QY8WWF5*MTcxMDUxNTkxMS4yLjEuMTcxMDUxNjA2NC4wLjAuMA..&_ga=2.172110058.-899307842.1710334759 ## How to Use PaliGemma is a single-turn vision language model not meant for conversational use, and it works best when fine-tuning to a specific use case. You can configure which task the model will solve by conditioning it with task prefixes, such as “detect” or “segment”. The pretrained models were trained in this fashion to imbue them with a rich set of capabilities (question answering, captioning, segmentation, etc.). However, they are not designed to be used directly, but to be transferred (by fine-tuning) to specific tasks using a similar prompt structure. For interactive testing, you can use the "mix" family of models, which have been fine-tuned on a mixture of tasks. To see model [google/paligemma-3b-mix-448](https://huggingface.co/google/paligemma-3b-mix-448) in action, check [this Space that uses the Transformers codebase](https://huggingface.co/spaces/big-vision/paligemma-hf). Please, refer to the [usage and limitations section](#usage-and-limitations) for intended use cases, or visit the [blog post](https://huggingface.co/blog/paligemma-google-vlm) for additional details and examples. ## Use in Transformers The following snippets use model `google/paligemma-3b-mix-224` for reference purposes. The model in this repo you are now browsing may have been trained for other tasks, please make sure you use appropriate inputs for the task at hand. ### Running the default precision (`float32`) on CPU ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import requests import torch model_id = "google/paligemma-3b-mix-224" url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval() processor = AutoProcessor.from_pretrained(model_id) # Instruct the model to create a caption in Spanish prompt = "caption es" model_inputs = processor(text=prompt, images=image, return_tensors="pt") input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ``` Output: `Un auto azul estacionado frente a un edificio.` ### Running other precisions on CUDA For convenience, the repos contain revisions of the weights already converted to `bfloat16` and `float16`, so you can use them to reduce the download size and avoid casting on your local computer. This is how you'd run `bfloat16` on an nvidia CUDA card. ```python from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import requests import torch model_id = "google/paligemma-3b-mix-224" device = "cuda:0" dtype = torch.bfloat16 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) model = PaliGemmaForConditionalGeneration.from_pretrained( model_id, torch_dtype=dtype, device_map=device, revision="bfloat16", ).eval() processor = AutoProcessor.from_pretrained(model_id) # Instruct the model to create a caption in Spanish prompt = "caption es" model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device) input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ``` ### Loading in 4-bit / 8-bit You need to install `bitsandbytes` to automatically run inference using 8-bit or 4-bit precision: ``` pip install bitsandbytes accelerate ``` ``` from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import requests import torch model_id = "google/paligemma-3b-mix-224" device = "cuda:0" dtype = torch.bfloat16 url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" image = Image.open(requests.get(url, stream=True).raw) quantization_config = BitsAndBytesConfig(load_in_8bit=True) model = PaliGemmaForConditionalGeneration.from_pretrained( model_id, quantization_config=quantization_config ).eval() processor = AutoProcessor.from_pretrained(model_id) # Instruct the model to create a caption in Spanish prompt = "caption es" model_inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device) input_len = model_inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**model_inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) print(decoded) ``` ## Implementation information ### Hardware PaliGemma was trained using the latest generation of Tensor Processing Unit (TPU) hardware (TPUv5e). ### Software Training was done using [JAX](https://github.com/google/jax), [Flax](https://github.com/google/flax), [TFDS](https://github.com/tensorflow/datasets) and [`big_vision`](https://github.com/google-research/big_vision). JAX allows researchers to take advantage of the latest generation of hardware, including TPUs, for faster and more efficient training of large models. TFDS is used to access datasets and Flax is used for model architecture. The PaliGemma fine-tune code and inference code are released in the `big_vision` GitHub repository. ## Evaluation information ### Benchmark results In order to verify the transferability of PaliGemma to a wide variety of academic tasks, we fine-tune the pretrained models on each task. Additionally we train the mix model with a mixture of the transfer tasks. We report results on different resolutions to provide an impression of which tasks benefit from increased resolution. Importantly, none of these tasks or datasets are part of the pretraining data mixture, and their images are explicitly removed from the web-scale pre-training data. #### Single task (fine-tune on single task) <table> <tbody><tr> <th>Benchmark<br>(train split)</th> <th>Metric<br>(split)</th> <th>pt-224</th> <th>pt-448</th> <th>pt-896</th> </tr> <tr> <th>Captioning</th> </tr> <tr> <td> <a href="https://cocodataset.org/#home">COCO captions</a><br>(train+restval) </td> <td>CIDEr (val)</td> <td>141.92</td> <td>144.60</td> </tr> <tr> <td> <a href="https://nocaps.org/">NoCaps</a><br>(Eval of COCO<br>captions transfer) </td> <td>CIDEr (val)</td> <td>121.72</td> <td>123.58</td> </tr> <tr> <td> <a href="https://arxiv.org/pdf/2205.12522">COCO-35L</a><br>(train) </td> <td>CIDEr dev<br>(en/avg-34/avg)</td> <td> 139.2<br> 115.8<br> 116.4 </td> <td> 141.2<br> 118.0<br> 118.6 </td> </tr> <tr> <td> <a href="https://arxiv.org/pdf/2205.12522">XM3600</a><br>(Eval of COCO-35L transfer) </td> <td>CIDEr dev<br>(en/avg-34/avg)</td> <td> 78.1<br> 41.3<br> 42.4 </td> <td> 80.0<br> 41.9<br> 42.9 </td> </tr> <tr> <td> <a href="https://textvqa.org/textcaps/">TextCaps</a><br>(train) </td> <td>CIDEr (val)</td> <td>127.48</td> <td>153.94</td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2110.11624">SciCap</a><br>(first sentence, no subfigure)<br>(train+val) </td> <td>CIDEr/BLEU-4<br>(test)</td> <td> 162.25<br> 0.192<br> </td> <td> 181.49<br> 0.211<br> </td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2108.03353">Screen2words</a><br>(train+dev) </td> <td>CIDEr (test)</td> <td>117.57</td> <td>119.59</td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2010.04295">Widget Captioning</a><br>(train+dev) </td> <td>CIDEr (test)</td> <td>136.07</td> <td>148.36</td> </tr> <tr> <th>Question answering</th> </tr> <tr> <td> <a href="https://visualqa.org/index.html">VQAv2</a><br>(train+validation) </td> <td>Accuracy<br>(Test server - std)</td> <td>83.19</td> <td>85.64</td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2401.06209">MMVP</a><br>(Eval of VQAv2 transfer) </td> <td>Paired Accuracy</td> <td>47.33</td> <td>45.33</td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2305.10355">POPE</a><br>(Eval of VQAv2 transfer) </td> <td>Accuracy<br>(random/popular/<br>adversarial)</td> <td> 87.80<br> 85.87<br> 84.27 </td> <td> 88.23<br> 86.77<br> 85.90 </td> </tr> <tr> <td> <a href="https://okvqa.allenai.org/">OKVQA</a><br>(train) </td> <td>Accuracy (val)</td> <td>63.54</td> <td>63.15</td> </tr> <tr> <td> <a href="https://allenai.org/project/a-okvqa/home">A-OKVQA</a> (MC)<br>(train+val) </td> <td>Accuracy<br>(Test server)</td> <td>76.37</td> <td>76.90</td> </tr> <tr> <td> <a href="https://allenai.org/project/a-okvqa/home">A-OKVQA</a> (DA)<br>(train+val) </td> <td>Accuracy<br>(Test server)</td> <td>61.85</td> <td>63.22</td> </tr> <tr> <td> <a href="https://cs.stanford.edu/people/dorarad/gqa/about.html">GQA</a><br>(train_balanced+<br>val_balanced) </td> <td>Accuracy<br>(testdev balanced)</td> <td>65.61</td> <td>67.03</td> </tr> <tr> <td> <a href="https://aclanthology.org/2022.findings-acl.196/">xGQA</a><br>(Eval of GQA transfer) </td> <td>Mean Accuracy<br>(bn, de, en, id,<br>ko, pt, ru, zh)</td> <td>58.37</td> <td>59.07</td> </tr> <tr> <td> <a href="https://lil.nlp.cornell.edu/nlvr/">NLVR2</a><br>(train+dev) </td> <td>Accuracy (test)</td> <td>90.02</td> <td>88.93</td> </tr> <tr> <td> <a href="https://marvl-challenge.github.io/">MaRVL</a><br>(Eval of NLVR2 transfer) </td> <td>Mean Accuracy<br>(test)<br>(id, sw, ta, tr, zh)</td> <td>80.57</td> <td>76.78</td> </tr> <tr> <td> <a href="https://allenai.org/data/diagrams">AI2D</a><br>(train) </td> <td>Accuracy (test)</td> <td>72.12</td> <td>73.28</td> </tr> <tr> <td> <a href="https://scienceqa.github.io/">ScienceQA</a><br>(Img subset, no CoT)<br>(train+val) </td> <td>Accuracy (test)</td> <td>95.39</td> <td>95.93</td> </tr> <tr> <td> <a href="https://zenodo.org/records/6344334">RSVQA-LR</a> (Non numeric)<br>(train+val) </td> <td>Mean Accuracy<br>(test)</td> <td>92.65</td> <td>93.11</td> </tr> <tr> <td> <a href="https://zenodo.org/records/6344367">RSVQA-HR</a> (Non numeric)<br>(train+val) </td> <td>Mean Accuracy<br>(test/test2)</td> <td> 92.61<br> 90.58 </td> <td> 92.79<br> 90.54 </td> </tr> <tr> <td> <a href="https://arxiv.org/abs/2203.10244">ChartQA</a><br>(human+aug)x(train+val) </td> <td>Mean Relaxed<br>Accuracy<br>(test_human,<br>test_aug)</td> <td>57.08</td> <td>71.36</td> </tr> <tr> <td> <a href="https://vizwiz.org/tasks-and-datasets/vqa/">VizWiz VQA</a><br>(train+val) </td> <td>Accuracy<br>(Test server - std)</td> <td> 73.7 </td> <td> 75.52 </td> </tr> <tr> <td> <a href="https://arxiv.org/abs/1810.12440">TallyQA</a><br>(train) </td> <td>Accuracy<br>(test_simple/<br>test_complex)</td> <td> 81.72<br> 69.56 </td> <td> 84.86<br> 72.27 </td> </tr> <tr> <td> <a href="https://ocr-vqa.github.io/">OCR-VQA</a><br>(train+val) </td> <td>Accuracy (test)</td> <td>72.32</td> <td>74.61</td> <td>74.93</td> </tr> <tr> <td> <a href="https://textvqa.org/">TextVQA</a><br>(train+val) </td> <td>Accuracy<br>(Test server - std)</td> <td>55.47</td> <td>73.15</td> <td>76.48</td> </tr> <tr> <td> <a href="https://www.docvqa.org/">DocVQA</a><br>(train+val) </td> <td>ANLS (Test server)</td> <td>43.74</td> <td>78.02</td> <td>84.77</td> </tr> <tr> <td> <a href="https://openaccess.thecvf.com/content/WACV2022/papers/Mathew_InfographicVQA_WACV_2022_paper.pdf">Infographic VQA</a><br>(train+val) </td> <td>ANLS (Test server)</td> <td>28.46</td> <td>40.47</td> <td>47.75</td> </tr> <tr> <td> <a href="https://arxiv.org/abs/1905.13648">SceneText VQA</a><br>(train+val) </td> <td>ANLS (Test server)</td> <td>63.29</td> <td>81.82</td> <td>84.40</td> </tr> <tr> <th>Segmentation</th> </tr> <tr> <td> <a href="https://arxiv.org/abs/1608.00272">RefCOCO</a><br>(combined refcoco, refcoco+,<br>refcocog excluding val<br>and test images) </td> <td>MIoU<br>(validation)<br>refcoco/refcoco+/<br>refcocog</td> <td> 73.40<br> 68.32<br> 67.65 </td> <td> 75.57<br> 69.76<br> 70.17 </td> <td> 76.94<br> 72.18<br> 72.22 </td> </tr> <tr> <th>Video tasks (Caption/QA)</th> </tr> <tr> <td>MSR-VTT (Captioning)</td> <td>CIDEr (test)</td> <td>70.54</td> </tr> <tr> <td>MSR-VTT (QA)</td> <td>Accuracy (test)</td> <td>50.09</td> </tr> <tr> <td>ActivityNet (Captioning)</td> <td>CIDEr (test)</td> <td>34.62</td> </tr> <tr> <td>ActivityNet (QA)</td> <td>Accuracy (test)</td> <td>50.78</td> </tr> <tr> <td>VATEX (Captioning)</td> <td>CIDEr (test)</td> <td>79.73</td> </tr> <tr> <td>MSVD (QA)</td> <td>Accuracy (test)</td> <td>60.22</td> </tr> </tbody></table> #### Mix model (fine-tune on mixture of transfer tasks) <table> <tbody><tr> <th>Benchmark</th> <th>Metric (split)</th> <th>mix-224</th> <th>mix-448</th> </tr> <tr> <td><a href="https://arxiv.org/abs/2401.06209">MMVP</a></td> <td>Paired Accuracy</td> <td>46.00</td> <td>45.33</td> </tr> <tr> <td><a href="https://arxiv.org/abs/2305.10355">POPE</a></td> <td>Accuracy<br>(random/popular/adversarial)</td> <td> 88.00<br> 86.63<br> 85.67 </td> <td> 89.37<br> 88.40<br> 87.47 </td> </tr> </tbody></table> ## Ethics and safety ### Evaluation approach Our evaluation methods include structured evaluations and internal red-teaming testing of relevant content policies. Red-teaming was conducted by a number of different teams, each with different goals and human evaluation metrics. These models were evaluated against a number of different categories relevant to ethics and safety, including: * Human evaluation on prompts covering child safety, content safety and representational harms. See the [Gemma model card](https://ai.google.dev/gemma/docs/model_card#evaluation_approach) for more details on evaluation approach, but with image captioning and visual question answering setups. * Image-to-Text benchmark evaluation: Benchmark against relevant academic datasets such as FairFace Dataset ([Karkkainen et al., 2021](https://arxiv.org/abs/1908.04913)). ### Evaluation results * The human evaluation results of ethics and safety evaluations are within acceptable thresholds for meeting [internal policies](https://storage.googleapis.com/gweb-uniblog-publish-prod/documents/2023_Google_AI_Principles_Progress_Update.pdf#page=11) for categories such as child safety, content safety and representational harms. * On top of robust internal evaluations, we also use the Perspective API (threshold of 0.8) to measure toxicity, profanity, and other potential issues in the generated captions for images sourced from the FairFace dataset. We report the maximum and median values observed across subgroups for each of the perceived gender, ethnicity, and age attributes. <table> <tbody><tr> </tr></tbody><tbody><tr><th>Metric</th> <th>Perceived<br>gender</th> <th></th> <th>Ethnicity</th> <th></th> <th>Age group</th> <th></th> </tr> <tr> <th></th> <th>Maximum</th> <th>Median</th> <th>Maximum</th> <th>Median</th> <th>Maximum</th> <th>Median</th> </tr> <tr> <td>Toxicity</td> <td>0.04%</td> <td>0.03%</td> <td>0.08%</td> <td>0.00%</td> <td>0.09%</td> <td>0.00%</td> </tr> <tr> <td>Identity Attack</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> </tr> <tr> <td>Insult</td> <td>0.06%</td> <td>0.04%</td> <td>0.09%</td> <td>0.07%</td> <td>0.16%</td> <td>0.00%</td> </tr> <tr> <td>Threat</td> <td>0.06%</td> <td>0.05%</td> <td>0.14%</td> <td>0.05%</td> <td>0.17%</td> <td>0.00%</td> </tr> <tr> <td>Profanity</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> <td>0.00%</td> </tr> </tbody></table> ## Usage and limitations ### Intended usage Open Vision Language Models (VLMs) have a wide range of applications across various industries and domains. The following list of potential uses is not comprehensive. The purpose of this list is to provide contextual information about the possible use-cases that the model creators considered as part of model training and development. Fine-tune on specific vision-language task: * The pre-trained models can be fine-tuned on a wide range of vision-language tasks such as: image captioning, short video caption, visual question answering, text reading, object detection and object segmentation. * The pre-trained models can be fine-tuned for specific domains such as remote sensing question answering, visual questions from people who are blind, science question answering, describe UI element functionalities. * The pre-trained models can be fine-tuned for tasks with non-textual outputs such as bounding boxes or segmentation masks. Vision-language research: * The pre-trained models and fine-tuned models can serve as a foundation for researchers to experiment with VLM techniques, develop algorithms, and contribute to the advancement of the field. ### Ethical considerations and risks The development of vision-language models (VLMs) raises several ethical concerns. In creating an open model, we have carefully considered the following: * Bias and Fairness * VLMs trained on large-scale, real-world image-text data can reflect socio-cultural biases embedded in the training material. These models underwent careful scrutiny, input data pre-processing described and posterior evaluations reported in this card. * Misinformation and Misuse * VLMs can be misused to generate text that is false, misleading, or harmful. * Guidelines are provided for responsible use with the model, see the [Responsible Generative AI Toolkit](https://ai.google.dev/responsible). * Transparency and Accountability * This model card summarizes details on the models' architecture, capabilities, limitations, and evaluation processes. * A responsibly developed open model offers the opportunity to share innovation by making VLM technology accessible to developers and researchers across the AI ecosystem. Risks identified and mitigations: * **Perpetuation of biases:** It's encouraged to perform continuous monitoring (using evaluation metrics, human review) and the exploration of de-biasing techniques during model training, fine-tuning, and other use cases. * **Generation of harmful content:** Mechanisms and guidelines for content safety are essential. Developers are encouraged to exercise caution and implement appropriate content safety safeguards based on their specific product policies and application use cases. * **Misuse for malicious purposes:** Technical limitations and developer and end-user education can help mitigate against malicious applications of LLMs. Educational resources and reporting mechanisms for users to flag misuse are provided. Prohibited uses of Gemma models are outlined in the [Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy). * **Privacy violations:** Models were trained on data filtered to remove certain personal information and sensitive data. Developers are encouraged to adhere to privacy regulations with privacy-preserving techniques. ### Limitations * Most limitations inherited from the underlying Gemma model still apply: * VLMs are better at tasks that can be framed with clear prompts and instructions. Open-ended or highly complex tasks might be challenging. * Natural language is inherently complex. VLMs might struggle to grasp subtle nuances, sarcasm, or figurative language. * VLMs generate responses based on information they learned from their training datasets, but they are not knowledge bases. They may generate incorrect or outdated factual statements. * VLMs rely on statistical patterns in language and images. They might lack the ability to apply common sense reasoning in certain situations. * PaliGemma was designed first and foremost to serve as a general pre-trained model for transfer to specialized tasks. Hence, its "out of the box" or "zero-shot" performance might lag behind models designed specifically for that. * PaliGemma is not a multi-turn chatbot. It is designed for a single round of image and text input.

huggyllama/llama-7b

huggyllama

transformers

text-generation

--- license: other --- This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.

facebook/bart-large

facebook

transformers

feature-extraction

--- license: apache-2.0 language: en --- # BART (large-sized model) BART model pre-trained on English language. It was introduced in the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Lewis et al. and first released in [this repository](https://github.com/pytorch/fairseq/tree/master/examples/bart). Disclaimer: The team releasing BART did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BART is a transformer encoder-decoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. BART is particularly effective when fine-tuned for text generation (e.g. summarization, translation) but also works well for comprehension tasks (e.g. text classification, question answering). ## Intended uses & limitations You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=bart) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model in PyTorch: ```python from transformers import BartTokenizer, BartModel tokenizer = BartTokenizer.from_pretrained('facebook/bart-large') model = BartModel.from_pretrained('facebook/bart-large') inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-1910-13461, author = {Mike Lewis and Yinhan Liu and Naman Goyal and Marjan Ghazvininejad and Abdelrahman Mohamed and Omer Levy and Veselin Stoyanov and Luke Zettlemoyer}, title = {{BART:} Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension}, journal = {CoRR}, volume = {abs/1910.13461}, year = {2019}, url = {http://arxiv.org/abs/1910.13461}, eprinttype = {arXiv}, eprint = {1910.13461}, timestamp = {Thu, 31 Oct 2019 14:02:26 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-1910-13461.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

stabilityai/stable-diffusion-2-depth

stabilityai

diffusers

--- license: openrail++ tags: - stable-diffusion inference: false --- # Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2-depth` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-depth-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-depth/resolve/main/512-depth-ema.ckpt). - Use it with 🧨 [`diffusers`](#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install -U git+https://github.com/huggingface/transformers.git pip install diffusers transformers accelerate scipy safetensors ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler): ```python import torch import requests from PIL import Image from diffusers import StableDiffusionDepth2ImgPipeline pipe = StableDiffusionDepth2ImgPipeline.from_pretrained( "stabilityai/stable-diffusion-2-depth", torch_dtype=torch.float16, ).to("cuda") url = "http://images.cocodataset.org/val2017/000000039769.jpg" init_image = Image.open(requests.get(url, stream=True).raw) prompt = "two tigers" n_propmt = "bad, deformed, ugly, bad anotomy" image = pipe(prompt=prompt, image=init_image, negative_prompt=n_propmt, strength=0.7).images[0] ``` **Notes**: - Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance) - If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed) # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere” - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a subset of the large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section). ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic. **Training Procedure** Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through the OpenCLIP-ViT/H text-encoder. - The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512. We currently provide the following checkpoints: - `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. 850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`. - `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset. - `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. - `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning. The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama). - `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752). In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 1 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:  Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 200000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq. ## Citation @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } *This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

adrianjoheni/translation-model-opus

adrianjoheni

transformers

translation

--- language: - en - es tags: - translation license: apache-2.0 --- ### eng-spa * source group: English * target group: Spanish * OPUS readme: [eng-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md) * model: transformer * source language(s): eng * target language(s): spa * model: transformer * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-18.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip) * test set translations: [opus-2020-08-18.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt) * test set scores: [opus-2020-08-18.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newssyscomb2009-engspa.eng.spa | 31.0 | 0.583 | | news-test2008-engspa.eng.spa | 29.7 | 0.564 | | newstest2009-engspa.eng.spa | 30.2 | 0.578 | | newstest2010-engspa.eng.spa | 36.9 | 0.620 | | newstest2011-engspa.eng.spa | 38.2 | 0.619 | | newstest2012-engspa.eng.spa | 39.0 | 0.625 | | newstest2013-engspa.eng.spa | 35.0 | 0.598 | | Tatoeba-test.eng.spa | 54.9 | 0.721 | ### System Info: - hf_name: eng-spa - source_languages: eng - target_languages: spa - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-spa/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'es'] - src_constituents: {'eng'} - tgt_constituents: {'spa'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-spa/opus-2020-08-18.test.txt - src_alpha3: eng - tgt_alpha3: spa - short_pair: en-es - chrF2_score: 0.721 - bleu: 54.9 - brevity_penalty: 0.978 - ref_len: 77311.0 - src_name: English - tgt_name: Spanish - train_date: 2020-08-18 00:00:00 - src_alpha2: en - tgt_alpha2: es - prefer_old: False - long_pair: eng-spa - helsinki_git_sha: d2f0910c89026c34a44e331e785dec1e0faa7b82 - transformers_git_sha: f7af09b4524b784d67ae8526f0e2fcc6f5ed0de9 - port_machine: brutasse - port_time: 2020-08-24-18:20

speechbrain/lang-id-voxlingua107-ecapa

speechbrain

speechbrain

audio-classification

--- language: - multilingual - ab - af - am - ar - as - az - ba - be - bg - bi - bo - br - bs - ca - ceb - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fo - fr - gl - gn - gu - gv - ha - haw - hi - hr - ht - hu - hy - ia - id - is - it - he - ja - jv - ka - kk - km - kn - ko - la - lm - ln - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - nn - no - oc - pa - pl - ps - pt - ro - ru - sa - sco - sd - si - sk - sl - sn - so - sq - sr - su - sv - sw - ta - te - tg - th - tk - tl - tr - tt - uk - ud - uz - vi - war - yi - yo - zh thumbnail: tags: - audio-classification - speechbrain - embeddings - Language - Identification - pytorch - ECAPA-TDNN - TDNN - VoxLingua107 license: "apache-2.0" datasets: - VoxLingua107 metrics: - Accuracy widget: - example_title: English Sample src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac --- # VoxLingua107 ECAPA-TDNN Spoken Language Identification Model ## Model description This is a spoken language recognition model trained on the VoxLingua107 dataset using SpeechBrain. The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition. However, it uses more fully connected hidden layers after the embedding layer, and cross-entropy loss was used for training. We observed that this improved the performance of extracted utterance embeddings for downstream tasks. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. The model can classify a speech utterance according to the language spoken. It covers 107 different languages ( Abkhazian, Afrikaans, Amharic, Arabic, Assamese, Azerbaijani, Bashkir, Belarusian, Bulgarian, Bengali, Tibetan, Breton, Bosnian, Catalan, Cebuano, Czech, Welsh, Danish, German, Greek, English, Esperanto, Spanish, Estonian, Basque, Persian, Finnish, Faroese, French, Galician, Guarani, Gujarati, Manx, Hausa, Hawaiian, Hindi, Croatian, Haitian, Hungarian, Armenian, Interlingua, Indonesian, Icelandic, Italian, Hebrew, Japanese, Javanese, Georgian, Kazakh, Central Khmer, Kannada, Korean, Latin, Luxembourgish, Lingala, Lao, Lithuanian, Latvian, Malagasy, Maori, Macedonian, Malayalam, Mongolian, Marathi, Malay, Maltese, Burmese, Nepali, Dutch, Norwegian Nynorsk, Norwegian, Occitan, Panjabi, Polish, Pushto, Portuguese, Romanian, Russian, Sanskrit, Scots, Sindhi, Sinhala, Slovak, Slovenian, Shona, Somali, Albanian, Serbian, Sundanese, Swedish, Swahili, Tamil, Telugu, Tajik, Thai, Turkmen, Tagalog, Turkish, Tatar, Ukrainian, Urdu, Uzbek, Vietnamese, Waray, Yiddish, Yoruba, Mandarin Chinese). ## Intended uses & limitations The model has two uses: - use 'as is' for spoken language recognition - use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data The model is trained on automatically collected YouTube data. For more information about the dataset, see [here](http://bark.phon.ioc.ee/voxlingua107/). #### How to use ```bash pip install git+https://github.com/speechbrain/speechbrain.git@develop ``` ```python import torchaudio from speechbrain.inference.classifiers import EncoderClassifier language_id = EncoderClassifier.from_hparams(source="speechbrain/lang-id-voxlingua107-ecapa", savedir="tmp") # Download Thai language sample from Omniglot and cvert to suitable form signal = language_id.load_audio("speechbrain/lang-id-voxlingua107-ecapa/udhr_th.wav") prediction = language_id.classify_batch(signal) print(prediction) # (tensor([[-2.8646e+01, -3.0346e+01, -2.0748e+01, -2.9562e+01, -2.2187e+01, # -3.2668e+01, -3.6677e+01, -3.3573e+01, -3.2545e+01, -2.4365e+01, # -2.4688e+01, -3.1171e+01, -2.7743e+01, -2.9918e+01, -2.4770e+01, # -3.2250e+01, -2.4727e+01, -2.6087e+01, -2.1870e+01, -3.2821e+01, # -2.2128e+01, -2.2822e+01, -3.0888e+01, -3.3564e+01, -2.9906e+01, # -2.2392e+01, -2.5573e+01, -2.6443e+01, -3.2429e+01, -3.2652e+01, # -3.0030e+01, -2.4607e+01, -2.2967e+01, -2.4396e+01, -2.8578e+01, # -2.5153e+01, -2.8475e+01, -2.6409e+01, -2.5230e+01, -2.7957e+01, # -2.6298e+01, -2.3609e+01, -2.5863e+01, -2.8225e+01, -2.7225e+01, # -3.0486e+01, -2.1185e+01, -2.7938e+01, -3.3155e+01, -1.9076e+01, # -2.9181e+01, -2.2160e+01, -1.8352e+01, -2.5866e+01, -3.3636e+01, # -4.2016e+00, -3.1581e+01, -3.1894e+01, -2.7834e+01, -2.5429e+01, # -3.2235e+01, -3.2280e+01, -2.8786e+01, -2.3366e+01, -2.6047e+01, # -2.2075e+01, -2.3770e+01, -2.2518e+01, -2.8101e+01, -2.5745e+01, # -2.6441e+01, -2.9822e+01, -2.7109e+01, -3.0225e+01, -2.4566e+01, # -2.9268e+01, -2.7651e+01, -3.4221e+01, -2.9026e+01, -2.6009e+01, # -3.1968e+01, -3.1747e+01, -2.8156e+01, -2.9025e+01, -2.7756e+01, # -2.8052e+01, -2.9341e+01, -2.8806e+01, -2.1636e+01, -2.3992e+01, # -2.3794e+01, -3.3743e+01, -2.8332e+01, -2.7465e+01, -1.5085e-02, # -2.9094e+01, -2.1444e+01, -2.9780e+01, -3.6046e+01, -3.7401e+01, # -3.0888e+01, -3.3172e+01, -1.8931e+01, -2.2679e+01, -3.0225e+01, # -2.4995e+01, -2.1028e+01]]), tensor([-0.0151]), tensor([94]), ['th']) # The scores in the prediction[0] tensor can be interpreted as log-likelihoods that # the given utterance belongs to the given language (i.e., the larger the better) # The linear-scale likelihood can be retrieved using the following: print(prediction[1].exp()) # tensor([0.9850]) # The identified language ISO code is given in prediction[3] print(prediction[3]) # ['th: Thai'] # Alternatively, use the utterance embedding extractor: emb = language_id.encode_batch(signal) print(emb.shape) # torch.Size([1, 1, 256]) ``` To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. The system is trained with recordings sampled at 16kHz (single channel). The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *classify_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *encode_batch* and *classify_batch*. #### Limitations and bias Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are: - Probably it's accuracy on smaller languages is quite limited - Probably it works worse on female speech than male speech (because YouTube data includes much more male speech) - Based on subjective experiments, it doesn't work well on speech with a foreign accent - Probably it doesn't work well on children's speech and on persons with speech disorders ## Training data The model is trained on [VoxLingua107](http://bark.phon.ioc.ee/voxlingua107/). VoxLingua107 is a speech dataset for training spoken language identification models. The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives. VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours. The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language. ## Training procedure See the [SpeechBrain recipe](https://github.com/speechbrain/speechbrain/tree/voxlingua107/recipes/VoxLingua107/lang_id). ## Evaluation results Error rate: 6.7% on the VoxLingua107 development dataset #### Referencing SpeechBrain ```bibtex @misc{speechbrain, title={{SpeechBrain}: A General-Purpose Speech Toolkit}, author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, year={2021}, eprint={2106.04624}, archivePrefix={arXiv}, primaryClass={eess.AS}, note={arXiv:2106.04624} } ``` ### Referencing VoxLingua107 ```bibtex @inproceedings{valk2021slt, title={{VoxLingua107}: a Dataset for Spoken Language Recognition}, author={J{\"o}rgen Valk and Tanel Alum{\"a}e}, booktitle={Proc. IEEE SLT Workshop}, year={2021}, } ``` #### About SpeechBrain SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. Website: https://speechbrain.github.io/ GitHub: https://github.com/speechbrain/speechbrain

facebook/contriever-msmarco

facebook

transformers

feature-extraction

--- tags: - feature-extraction pipeline_tag: feature-extraction --- This model is the finetuned version of the pre-trained contriever model available here https://huggingface.co/facebook/contriever, following the approach described in [Towards Unsupervised Dense Information Retrieval with Contrastive Learning](https://arxiv.org/abs/2112.09118). The associated GitHub repository is available here https://github.com/facebookresearch/contriever. ## Usage (HuggingFace Transformers) Using the model directly available in HuggingFace transformers requires to add a mean pooling operation to obtain a sentence embedding. ```python import torch from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained('facebook/contriever-msmarco') model = AutoModel.from_pretrained('facebook/contriever-msmarco') sentences = [ "Where was Marie Curie born?", "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Apply tokenizer inputs = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings outputs = model(**inputs) # Mean pooling def mean_pooling(token_embeddings, mask): token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.) sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None] return sentence_embeddings embeddings = mean_pooling(outputs[0], inputs['attention_mask']) ```

monster-labs/control_v1p_sd15_qrcode_monster

monster-labs

diffusers

--- tags: - stable-diffusion - controlnet - qrcode license: openrail++ language: - en --- # Controlnet QR Code Monster v2 For SD-1.5  ## Model Description This model is made to generate creative QR codes that still scan. Keep in mind that not all generated codes might be readable, but you can try different parameters and prompts to get the desired results. **NEW VERSION** Introducing the upgraded version of our model - Controlnet QR code Monster v2. V2 is a huge upgrade over v1, for scannability AND creativity. QR codes can now seamlessly blend the image by using a gray-colored background (#808080). As with the former version, the readability of some generated codes may vary, however playing around with parameters and prompts could yield better results. You can find in in the `v2/` subfolder. ## How to Use - **Condition**: QR codes are passed as condition images with a module size of 16px. Use a higher error correction level to make it easier to read (sometimes a lower level can be easier to read if smaller in size). Use a gray background for the rest of the image to make the code integrate better. - **Prompts**: Use a prompt to guide the QR code generation. The output will highly depend on the given prompt. Some seem to be really easily accepted by the qr code process, some will require careful tweaking to get good results. - **Controlnet guidance scale**: Set the controlnet guidance scale value: - High values: The generated QR code will be more readable. - Low values: The generated QR code will be more creative. ### Tips - For an optimally readable output, try generating multiple QR codes with similar parameters, then choose the best ones. - Use the Image-to-Image feature to improve the readability of a generated QR code: - Decrease the denoising strength to retain more of the original image. - Increase the controlnet guidance scale value for better readability. A typical workflow for "saving" a code would be : Max out the guidance scale and minimize the denoising strength, then bump the strength until the code scans. ## Example Outputs Here are some examples of creative, yet scannable QR codes produced by our model:    Feel free to experiment with prompts, parameters, and the Image-to-Image feature to achieve the desired QR code output. Good luck and have fun!

SpanBERT/spanbert-large-cased

SpanBERT

transformers

Entry not found

CohereForAI/aya-23-35B

CohereForAI

transformers

text-generation

--- inference: false library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 --- # Model Card for Aya-23-35B **Try Aya 23** You can try out Aya 23 (35B) before downloading the weights in our hosted Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ## Model Summary Aya 23 is an open weights research release of an instruction fine-tuned model with highly advanced multilingual capabilities. Aya 23 focuses on pairing a highly performant pre-trained [Command family](https://huggingface.co/CohereForAI/c4ai-command-r-plus) of models with the recently released [Aya Collection](https://huggingface.co/datasets/CohereForAI/aya_collection). The result is a powerful multilingual large language model serving 23 languages. This model card corresponds to the 35-billion version of the Aya 23 model. We also released an 8-billion version which you can find [here](https://huggingface.co/CohereForAI/aya-23-8B). We cover 23 languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese Developed by: [Cohere For AI](https://cohere.for.ai) and [Cohere](https://cohere.com/) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: aya-23-35B - Model Size: 35 billion parameters ### Usage Please install transformers from the source repository that includes the necessary changes for this model ```python # pip install 'git+https://github.com/huggingface/transformers.git' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/aya-23-35B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Anneme onu ne kadar sevdiğimi anlatan bir mektup yaz<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` ### Example Notebook [This notebook](https://huggingface.co/CohereForAI/aya-23-35B/blob/main/Aya_23_notebook.ipynb) showcases a detailed use of Aya 23 (8B) including inference and fine-tuning with [QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes). ## Model Details **Input**: Models input text only. **Output**: Models generate text only. **Model Architecture**: Aya-23-35B is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model is fine-tuned (IFT) to follow human instructions. **Languages covered**: The model is particularly optimized for multilinguality and supports the following languages: Arabic, Chinese (simplified & traditional), Czech, Dutch, English, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Korean, Persian, Polish, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian, and Vietnamese **Context length**: 8192 ### Evaluation <img src="benchmarks.png" alt="multilingual benchmarks" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> <img src="winrates.png" alt="average win rates" width="650" style="margin-left:'auto' margin-right:'auto' display:'block'"/> Please refer to the [Aya 23 technical report](https://cohere.com/research/papers/aya-command-23-8b-and-35b-technical-report-2024-05-23) for further details about the base model, data, instruction tuning, and evaluation. ### Model Card Contact For errors or additional questions about details in this model card, contact info@for.ai. ### Terms of Use We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant multilingual model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try the model today You can try Aya 23 in the Cohere [playground](https://dashboard.cohere.com/playground/chat) here. You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/aya-23). ### Citation info ```bibtex @misc{aryabumi2024aya, title={Aya 23: Open Weight Releases to Further Multilingual Progress}, author={Viraat Aryabumi and John Dang and Dwarak Talupuru and Saurabh Dash and David Cairuz and Hangyu Lin and Bharat Venkitesh and Madeline Smith and Kelly Marchisio and Sebastian Ruder and Acyr Locatelli and Julia Kreutzer and Nick Frosst and Phil Blunsom and Marzieh Fadaee and Ahmet Üstün and Sara Hooker}, year={2024}, eprint={2405.15032}, archivePrefix={arXiv}, primaryClass={cs.CL} }

TheBloke/Mistral-7B-v0.1-AWQ

TheBloke

transformers

text-generation

--- base_model: mistralai/Mistral-7B-v0.1 inference: false license: apache-2.0 model_creator: Mistral AI model_name: Mistral 7B v0.1 model_type: mistral pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: TheBloke tags: - pretrained --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B v0.1 - AWQ - Model creator: [Mistral AI](https://huggingface.co/mistralai) - Original model: [Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) <!-- description start --> ## Description This repo contains AWQ model files for [Mistral AI's Mistral 7B v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference. ### Mistral AWQs These are experimental first AWQs for the brand-new model format, Mistral. As of September 29th 2023, they are only supported by AutoAWQ (version 0.1.1+) To use from AutoAWQ requires installing both AutoAWQ and Transformers from Github. More details are below. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GGUF) * [Mistral AI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-v0.1) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: None ``` {prompt} ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Mistral-7B-v0.1-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-use-from-python start --> ## How to use this AWQ model from Python code ### Install the necessary packages Requires: - Transformers from [commit 72958fcd3c98a7afdc61f953aa58c544ebda2f79](https://github.com/huggingface/transformers/commit/72958fcd3c98a7afdc61f953aa58c544ebda2f79) - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) from [commit 1c5ccc791fa2cb0697db3b4070df1813f1736208](https://github.com/casper-hansen/AutoAWQ/commit/1c5ccc791fa2cb0697db3b4070df1813f1736208). ```shell pip3 install git+https://github.com/huggingface/transformers.git@72958fcd3c98a7afdc61f953aa58c544ebda2f79 pip3 install git+https://github.com/casper-hansen/AutoAWQ.git@1c5ccc791fa2cb0697db3b4070df1813f1736208 ``` ### You can then try the following example code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_name_or_path = "TheBloke/Mistral-7B-v0.1-AWQ" # Load model model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, trust_remote_code=False, safetensors=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False) prompt = "Tell me about AI" prompt_template=f'''{prompt} ''' print("\n\n*** Generate:") tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Generate output generation_output = model.generate( tokens, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, max_new_tokens=512 ) print("Output: ", tokenizer.decode(generation_output[0])) """ # Inference should be possible with transformers pipeline as well in future # But currently this is not yet supported by AutoAWQ (correct as of September 25th 2023) from transformers import pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) """ ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Mistral AI's Mistral 7B v0.1 # Model Card for Mistral-7B-v0.1 The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested. For full details of this model please read our [Release blog post](https://mistral.ai/news/announcing-mistral-7b/) ## Model Architecture Mistral-7B-v0.1 is a transformer model, with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.

tau-vision/sn6-finetune

tau-vision

transformers

text-generation

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Maykeye/TinyLLama-v0

Maykeye

transformers

text-generation

--- license: apache-2.0 --- This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture. * Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running the cells. Validation content is not used by the script so you put anythin in * Backup directory has a script do_backup that I used to copy weights from remote machine to local. Weight are generated too quickly, so by the time script copied weihgt N+1 * This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use any sliding window to train story not from the start * Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used * I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69). I had no troubles on the cloud machine with preninstalled libraries. * Demo script is demo.py * Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`: After training I decided that it's not necessary to beat validation into chunks * Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks so if random shuffle asks for a story, it may use cache or load chunk. Training dataset is too small, so in next versions I will get rid of it. from transformers import AutoModelForCausalLM, AutoTokenizer

EleutherAI/pythia-160m

EleutherAI

transformers

text-generation

--- language: - en tags: - pytorch - causal-lm - pythia license: apache-2.0 datasets: - EleutherAI/pile --- The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research [(see paper)](https://arxiv.org/pdf/2304.01373.pdf). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models <a href="#evaluations">match or exceed</a> the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. <details> <summary style="font-weight:600">Details on previous early release and naming convention.</summary> Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card <a href="#changelog">lists the changes</a>; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are [still available](https://huggingface.co/models?other=pythia_v0), but we suggest the retrained suite if you are just starting to use Pythia.<br> **This is the current release.** Please note that all models in the *Pythia* suite were renamed in January 2023. For clarity, a <a href="#naming-convention-and-parameter-count">table comparing the old and new names</a> is provided in this model card, together with exact parameter counts. </details> <br> # Pythia-160M ## Model Details - Developed by: [EleutherAI](http://eleuther.ai) - Model type: Transformer-based Language Model - Language: English - Learn more: [Pythia's GitHub repository](https://github.com/EleutherAI/pythia) for training procedure, config files, and details on how to use. [See paper](https://arxiv.org/pdf/2304.01373.pdf) for more evals and implementation details. - Library: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) - License: Apache 2.0 - Contact: to ask questions about this model, join the [EleutherAI Discord](https://discord.gg/zBGx3azzUn), and post them in `#release-discussion`. Please read the existing *Pythia* documentation before asking about it in the EleutherAI Discord. For general correspondence: [contact@eleuther. ai](mailto:contact@eleuther.ai). <figure> | Pythia model | Non-Embedding Params | Layers | Model Dim | Heads | Batch Size | Learning Rate | Equivalent Models | | -----------: | -------------------: | :----: | :-------: | :---: | :--------: | :-------------------: | :--------------------: | | 70M | 18,915,328 | 6 | 512 | 8 | 2M | 1.0 x 10^-3 | — | | 160M | 85,056,000 | 12 | 768 | 12 | 2M | 6.0 x 10^-4 | GPT-Neo 125M, OPT-125M | | 410M | 302,311,424 | 24 | 1024 | 16 | 2M | 3.0 x 10^-4 | OPT-350M | | 1.0B | 805,736,448 | 16 | 2048 | 8 | 2M | 3.0 x 10^-4 | — | | 1.4B | 1,208,602,624 | 24 | 2048 | 16 | 2M | 2.0 x 10^-4 | GPT-Neo 1.3B, OPT-1.3B | | 2.8B | 2,517,652,480 | 32 | 2560 | 32 | 2M | 1.6 x 10^-4 | GPT-Neo 2.7B, OPT-2.7B | | 6.9B | 6,444,163,072 | 32 | 4096 | 32 | 2M | 1.2 x 10^-4 | OPT-6.7B | | 12B | 11,327,027,200 | 36 | 5120 | 40 | 2M | 1.2 x 10^-4 | — | <figcaption>Engineering details for the <i>Pythia Suite</i>. Deduped and non-deduped models of a given size have the same hyperparameters. “Equivalent” models have <b>exactly</b> the same architecture, and the same number of non-embedding parameters.</figcaption> </figure> ## Uses and Limitations ### Intended Use The primary intended use of Pythia is research on the behavior, functionality, and limitations of large language models. This suite is intended to provide a controlled setting for performing scientific experiments. We also provide 154 checkpoints per model: initial `step0`, 10 log-spaced checkpoints `step{1,2,4...512}`, and 143 evenly-spaced checkpoints from `step1000` to `step143000`. These checkpoints are hosted on Hugging Face as branches. Note that branch `143000` corresponds exactly to the model checkpoint on the `main` branch of each model. You may also further fine-tune and adapt Pythia-160M for deployment, as long as your use is in accordance with the Apache 2.0 license. Pythia models work with the Hugging Face [Transformers Library](https://huggingface.co/docs/transformers/index). If you decide to use pre-trained Pythia-160M as a basis for your fine-tuned model, please conduct your own risk and bias assessment. ### Out-of-scope use The Pythia Suite is **not** intended for deployment. It is not a in itself a product and cannot be used for human-facing interactions. For example, the model may generate harmful or offensive text. Please evaluate the risks associated with your particular use case. Pythia models are English-language only, and are not suitable for translation or generating text in other languages. Pythia-160M has not been fine-tuned for downstream contexts in which language models are commonly deployed, such as writing genre prose, or commercial chatbots. This means Pythia-160M will **not** respond to a given prompt the way a product like ChatGPT does. This is because, unlike this model, ChatGPT was fine-tuned using methods such as Reinforcement Learning from Human Feedback (RLHF) to better “follow” human instructions. ### Limitations and biases The core functionality of a large language model is to take a string of text and predict the next token. The token used by the model need not produce the most “accurate” text. Never rely on Pythia-160M to produce factually accurate output. This model was trained on [the Pile](https://pile.eleuther.ai/), a dataset known to contain profanity and texts that are lewd or otherwise offensive. See [Section 6 of the Pile paper](https://arxiv.org/abs/2101.00027) for a discussion of documented biases with regards to gender, religion, and race. Pythia-160M may produce socially unacceptable or undesirable text, *even if* the prompt itself does not include anything explicitly offensive. If you plan on using text generated through, for example, the Hosted Inference API, we recommend having a human curate the outputs of this language model before presenting it to other people. Please inform your audience that the text was generated by Pythia-160M. ### Quickstart Pythia models can be loaded and used via the following code, demonstrated here for the third `pythia-70m-deduped` checkpoint: ```python from transformers import GPTNeoXForCausalLM, AutoTokenizer model = GPTNeoXForCausalLM.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) tokenizer = AutoTokenizer.from_pretrained( "EleutherAI/pythia-70m-deduped", revision="step3000", cache_dir="./pythia-70m-deduped/step3000", ) inputs = tokenizer("Hello, I am", return_tensors="pt") tokens = model.generate(**inputs) tokenizer.decode(tokens[0]) ``` Revision/branch `step143000` corresponds exactly to the model checkpoint on the `main` branch of each model.<br> For more information on how to use all Pythia models, see [documentation on GitHub](https://github.com/EleutherAI/pythia). ## Training ### Training data [The Pile](https://pile.eleuther.ai/) is a 825GiB general-purpose dataset in English. It was created by EleutherAI specifically for training large language models. It contains texts from 22 diverse sources, roughly broken down into five categories: academic writing (e.g. arXiv), internet (e.g. CommonCrawl), prose (e.g. Project Gutenberg), dialogue (e.g. YouTube subtitles), and miscellaneous (e.g. GitHub, Enron Emails). See [the Pile paper](https://arxiv.org/abs/2101.00027) for a breakdown of all data sources, methodology, and a discussion of ethical implications. Consult [the datasheet](https://arxiv.org/abs/2201.07311) for more detailed documentation about the Pile and its component datasets. The Pile can be downloaded from the [official website](https://pile.eleuther.ai/), or from a [community mirror](https://the-eye.eu/public/AI/pile/).<br> The Pile was **not** deduplicated before being used to train Pythia-160M. ### Training procedure All models were trained on the exact same data, in the exact same order. Each model saw 299,892,736,000 tokens during training, and 143 checkpoints for each model are saved every 2,097,152,000 tokens, spaced evenly throughout training, from `step1000` to `step143000` (which is the same as `main`). In addition, we also provide frequent early checkpoints: `step0` and `step{1,2,4...512}`. This corresponds to training for just under 1 epoch on the Pile for non-deduplicated models, and about 1.5 epochs on the deduplicated Pile. All *Pythia* models trained for 143000 steps at a batch size of 2M (2,097,152 tokens).<br> See [GitHub](https://github.com/EleutherAI/pythia) for more details on training procedure, including [how to reproduce it](https://github.com/EleutherAI/pythia/blob/main/README.md#reproducing-training).<br> Pythia uses the same tokenizer as [GPT-NeoX- 20B](https://huggingface.co/EleutherAI/gpt-neox-20b). ## Evaluations All 16 *Pythia* models were evaluated using the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). You can access the results by model and step at `results/json/*` in the [GitHub repository](https://github.com/EleutherAI/pythia/tree/main/results/json/).<br> Expand the sections below to see plots of evaluation results for all Pythia and Pythia-deduped models compared with OPT and BLOOM. <details> <summary>LAMBADA – OpenAI</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/lambada_openai_v1.png" style="width:auto"/> </details> <details> <summary>Physical Interaction: Question Answering (PIQA)</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/piqa_v1.png" style="width:auto"/> </details> <details> <summary>WinoGrande</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/winogrande_v1.png" style="width:auto"/> </details> <details> <summary>AI2 Reasoning Challenge—Easy Set</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/arc_easy_v1.png" style="width:auto"/> </details> <details> <summary>SciQ</summary> <img src="/EleutherAI/pythia-12b/resolve/main/eval_plots/sciq_v1.png" style="width:auto"/> </details> ## Changelog This section compares differences between previously released [Pythia v0](https://huggingface.co/models?other=pythia_v0) and the current models. See Appendix B of the Pythia paper for further discussion of these changes and the motivation behind them. We found that retraining Pythia had no impact on benchmark performance. - All model sizes are now trained with uniform batch size of 2M tokens. Previously, the models of size 160M, 410M, and 1.4B parameters were trained with batch sizes of 4M tokens. - We added checkpoints at initialization (step 0) and steps {1,2,4,8,16,32,64, 128,256,512} in addition to every 1000 training steps. - Flash Attention was used in the new retrained suite. - We remedied a minor inconsistency that existed in the original suite: all models of size 2.8B parameters or smaller had a learning rate (LR) schedule which decayed to a minimum LR of 10% the starting LR rate, but the 6.9B and 12B models all used an LR schedule which decayed to a minimum LR of 0. In the redone training runs, we rectified this inconsistency: all models now were trained with LR decaying to a minimum of 0.1× their maximum LR. ### Naming convention and parameter count *Pythia* models were renamed in January 2023. It is possible that the old naming convention still persists in some documentation by accident. The current naming convention (70M, 160M, etc.) is based on total parameter count. <figure style="width:32em"> | current Pythia suffix | old suffix | total params | non-embedding params | | --------------------: | ---------: | -------------: | -------------------: | | 70M | 19M | 70,426,624 | 18,915,328 | | 160M | 125M | 162,322,944 | 85,056,000 | | 410M | 350M | 405,334,016 | 302,311,424 | | 1B | 800M | 1,011,781,632 | 805,736,448 | | 1.4B | 1.3B | 1,414,647,808 | 1,208,602,624 | | 2.8B | 2.7B | 2,775,208,960 | 2,517,652,480 | | 6.9B | 6.7B | 6,857,302,016 | 6,444,163,072 | | 12B | 13B | 11,846,072,320 | 11,327,027,200 | </figure>

facebook/dpr-question_encoder-multiset-base

facebook

transformers

feature-extraction

--- language: en license: cc-by-nc-4.0 tags: - dpr datasets: - nq_open - trivia_qa - web_questions - trec inference: false --- # `dpr-question_encoder-multiset-base` ## Table of Contents - [Model Details](#model-details) - [How To Get Started With the Model](#how-to-get-started-with-the-model) - [Uses](#uses) - [Risks, Limitations and Biases](#risks-limitations-and-biases) - [Training](#training) - [Evaluation](#evaluation-results) - [Environmental Impact](#environmental-impact) - [Technical Specifications](#technical-specifications) - [Citation Information](#citation-information) - [Model Card Authors](#model-card-authors) ## Model Details **Model Description:** [Dense Passage Retrieval (DPR)](https://github.com/facebookresearch/DPR) is a set of tools and models for state-of-the-art open-domain Q&A research. `dpr-question_encoder-multiset-base` is the question encoder trained using the [Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), and [CuratedTREC (TREC)](https://huggingface.co/datasets/trec). - **Developed by:** See [GitHub repo](https://github.com/facebookresearch/DPR) for model developers - **Model Type:** BERT-based encoder - **Language(s):** [CC-BY-NC-4.0](https://github.com/facebookresearch/DPR/blob/main/LICENSE), also see [Code of Conduct](https://github.com/facebookresearch/DPR/blob/main/CODE_OF_CONDUCT.md) - **License:** English - **Related Models:** - [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base) - [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base) - [`dpr-ctx_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base) - [`dpr-question_encoder-single-nq-base`](https://huggingface.co/facebook/dpr-question_encoder-single-nq-base) - [`dpr-reader-single-nq-base`](https://huggingface.co/facebook/dpr-reader-single-nq-base) - **Resources for more information:** - [Research Paper](https://arxiv.org/abs/2004.04906) - [GitHub Repo](https://github.com/facebookresearch/DPR) - [Hugging Face DPR docs](https://huggingface.co/docs/transformers/main/en/model_doc/dpr) - [BERT Base Uncased Model Card](https://huggingface.co/bert-base-uncased) ## How to Get Started with the Model Use the code below to get started with the model. ```python from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") model = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") input_ids = tokenizer("Hello, is my dog cute ?", return_tensors="pt")["input_ids"] embeddings = model(input_ids).pooler_output ``` ## Uses #### Direct Use `dpr-question_encoder-multiset-base`, [`dpr-ctx_encoder-multiset-base`](https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base), and [`dpr-reader-multiset-base`](https://huggingface.co/facebook/dpr-reader-multiset-base) can be used for the task of open-domain question answering. #### Misuse and Out-of-scope Use The model should not be used to intentionally create hostile or alienating environments for people. In addition, the set of DPR models was not trained to be factual or true representations of people or events, and therefore using the models to generate such content is out-of-scope for the abilities of this model. ## Risks, Limitations and Biases **CONTENT WARNING: Readers should be aware this section may contain content that is disturbing, offensive, and can propogate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al., 2021](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al., 2021](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model can include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Training #### Training Data This model was trained using the following datasets: - **[Natural Questions (NQ) dataset](https://huggingface.co/datasets/nq_open)** ([Lee et al., 2019](https://aclanthology.org/P19-1612/); [Kwiatkowski et al., 2019](https://aclanthology.org/Q19-1026/)) - **[TriviaQA](https://huggingface.co/datasets/trivia_qa)** ([Joshi et al., 2017](https://aclanthology.org/P17-1147/)) - **[WebQuestions (WQ)](https://huggingface.co/datasets/web_questions)** ([Berant et al., 2013](https://aclanthology.org/D13-1160/)) - **[CuratedTREC (TREC)](https://huggingface.co/datasets/trec)** ([Baudiš & Šedivý, 2015](https://www.aminer.cn/pub/599c7953601a182cd263079b/reading-wikipedia-to-answer-open-domain-questions)) #### Training Procedure The training procedure is described in the [associated paper](https://arxiv.org/pdf/2004.04906.pdf): > Given a collection of M text passages, the goal of our dense passage retriever (DPR) is to index all the passages in a low-dimensional and continuous space, such that it can retrieve efficiently the top k passages relevant to the input question for the reader at run-time. > Our dense passage retriever (DPR) uses a dense encoder EP(·) which maps any text passage to a d- dimensional real-valued vectors and builds an index for all the M passages that we will use for retrieval. At run-time, DPR applies a different encoder EQ(·) that maps the input question to a d-dimensional vector, and retrieves k passages of which vectors are the closest to the question vector. The authors report that for encoders, they used two independent BERT ([Devlin et al., 2019](https://aclanthology.org/N19-1423/)) networks (base, un-cased) and use FAISS ([Johnson et al., 2017](https://arxiv.org/abs/1702.08734)) during inference time to encode and index passages. See the paper for further details on training, including encoders, inference, positive and negative passages, and in-batch negatives. ## Evaluation The following evaluation information is extracted from the [associated paper](https://arxiv.org/pdf/2004.04906.pdf). #### Testing Data, Factors and Metrics The model developers report the performance of the model on five QA datasets, using the top-k accuracy (k ∈ {20, 100}). The datasets were [NQ](https://huggingface.co/datasets/nq_open), [TriviaQA](https://huggingface.co/datasets/trivia_qa), [WebQuestions (WQ)](https://huggingface.co/datasets/web_questions), [CuratedTREC (TREC)](https://huggingface.co/datasets/trec), and [SQuAD v1.1](https://huggingface.co/datasets/squad). #### Results | | Top 20 | | | | | Top 100| | | | | |:----:|:------:|:---------:|:--:|:----:|:-----:|:------:|:---------:|:--:|:----:|:-----:| | | NQ | TriviaQA | WQ | TREC | SQuAD | NQ | TriviaQA | WQ | TREC | SQuAD | | | 79.4 | 78.8 |75.0| 89.1 | 51.6 | 86.0 | 84.7 |82.9| 93.9 | 67.6 | ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). We present the hardware type and based on the [associated paper](https://arxiv.org/abs/2004.04906). - **Hardware Type:** 8 32GB GPUs - **Hours used:** Unknown - **Cloud Provider:** Unknown - **Compute Region:** Unknown - **Carbon Emitted:** Unknown ## Technical Specifications See the [associated paper](https://arxiv.org/abs/2004.04906) for details on the modeling architecture, objective, compute infrastructure, and training details. ## Citation Information ```bibtex @inproceedings{karpukhin-etal-2020-dense, title = "Dense Passage Retrieval for Open-Domain Question Answering", author = "Karpukhin, Vladimir and Oguz, Barlas and Min, Sewon and Lewis, Patrick and Wu, Ledell and Edunov, Sergey and Chen, Danqi and Yih, Wen-tau", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.emnlp-main.550", doi = "10.18653/v1/2020.emnlp-main.550", pages = "6769--6781", } ``` ## Model Card Authors This model card was written by the team at Hugging Face.

llava-hf/llava-v1.6-vicuna-13b-hf

llava-hf

transformers

image-text-to-text

--- tags: - vision - image-text-to-text --- # LLaVa-Next, leveraging [liuhaotian/llava-v1.6-vicuna-13b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-13b) as LLM The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Improved reasoning, OCR, and world knowledge](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon [LLaVa-1.5](https://huggingface.co/transformers/main/model_doc/llava.html) by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - More diverse and high quality data mixture - Dynamic high resolution  ## Intended uses & limitations You can use the raw model for tasks like image captioning, visual question answering, multimodal chatbot use cases. See the [model hub](https://huggingface.co/models?search=llava-hf) to look for other versions on a task that interests you. ### How to use Here's the prompt template for this model: ``` "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:" ``` You can load and use the model like following: ```python from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration import torch from PIL import Image import requests processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-vicuna-13b-hf") model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-vicuna-13b-hf", torch_dtype=torch.float16, low_cpu_mem_usage=True) model.to("cuda:0") # prepare image and text prompt, using the appropriate prompt template url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true" image = Image.open(requests.get(url, stream=True).raw) prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:" inputs = processor(prompt, image, return_tensors="pt").to("cuda:0") # autoregressively complete prompt output = model.generate(**inputs, max_new_tokens=100) print(processor.decode(output[0], skip_special_tokens=True)) ``` ### Model optimization #### 4-bit quantization through `bitsandbytes` library First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: ```diff model = LlavaNextForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + load_in_4bit=True ) ``` #### Use Flash-Attention 2 to further speed-up generation First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: ```diff model = LlavaNextForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + use_flash_attention_2=True ).to(0) ``` ### BibTeX entry and citation info ```bibtex @misc{liu2023improved, title={Improved Baselines with Visual Instruction Tuning}, author={Haotian Liu and Chunyuan Li and Yuheng Li and Yong Jae Lee}, year={2023}, eprint={2310.03744}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

laion/CLIP-ViT-g-14-laion2B-s12B-b42K

laion

open_clip

--- license: mit widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png candidate_labels: playing music, playing sports example_title: Cat & Dog --- # Model Card for CLIP ViT-g/14 - LAION-2B # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Training Details](#training-details) 4. [Evaluation](#evaluation) 5. [Acknowledgements](#acknowledgements) 6. [Citation](#citation) 7. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description A CLIP ViT-g/14 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Romain Beaumont on the [stability.ai](https://stability.ai/) cluster. # Uses As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model. The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset. ## Direct Use Zero-shot image classification, image and text retrieval, among others. ## Downstream Use Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others. ## Out-of-Scope Use As per the OpenAI models, **Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use. Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases. Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below. # Training Details ## Training Data This model was trained with the 2 Billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/). **IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress. ## Training Procedure Please see [training notes](https://docs.google.com/document/d/1EFbMLRWSSV0LUf9Du1pWzWqgeiIRPwEWX2s1C6mAk5c) and [wandb logs](https://wandb.ai/rom1504/eval_openclip/reports/slow-g-14--VmlldzoyNTMwMjg5). # Evaluation Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark). ## Testing Data, Factors & Metrics ### Testing Data The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval. **TODO** - more detail ## Results The model achieves a 76.6 zero-shot top-1 accuracy on ImageNet-1k. An initial round of benchmarks have been performed on a wider range of datasets, currently viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb **TODO** - create table for just this model's metrics. # Acknowledgements Acknowledging [stability.ai](https://stability.ai/) for the compute used to train this model. # Citation **BibTeX:** In addition to forthcoming LAION-5B (https://laion.ai/blog/laion-5b/) paper, please cite: OpenAI CLIP paper ``` @inproceedings{Radford2021LearningTV, title={Learning Transferable Visual Models From Natural Language Supervision}, author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever}, booktitle={ICML}, year={2021} } ``` OpenCLIP software ``` @software{ilharco_gabriel_2021_5143773, author = {Ilharco, Gabriel and Wortsman, Mitchell and Wightman, Ross and Gordon, Cade and Carlini, Nicholas and Taori, Rohan and Dave, Achal and Shankar, Vaishaal and Namkoong, Hongseok and Miller, John and Hajishirzi, Hannaneh and Farhadi, Ali and Schmidt, Ludwig}, title = {OpenCLIP}, month = jul, year = 2021, note = {If you use this software, please cite it as below.}, publisher = {Zenodo}, version = {0.1}, doi = {10.5281/zenodo.5143773}, url = {https://doi.org/10.5281/zenodo.5143773} } ``` # How to Get Started with the Model Use the code below to get started with the model. ** TODO ** - Hugging Face transformers, OpenCLIP, and timm getting started snippets

sentence-transformers/paraphrase-MiniLM-L12-v2

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/paraphrase-MiniLM-L12-v2 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L12-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/paraphrase-MiniLM-L12-v2') model = AutoModel.from_pretrained('sentence-transformers/paraphrase-MiniLM-L12-v2') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/paraphrase-MiniLM-L12-v2) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

HooshvareLab/bert-base-parsbert-uncased

HooshvareLab

transformers

fill-mask

## ParsBERT: Transformer-based Model for Persian Language Understanding ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base. Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515) All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned) --- ## Introduction This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually. As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences. ## Evaluation ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling. ## Results The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures. ### Sentiment Analysis (SA) task | Dataset | ParsBERT | mBERT | DeepSentiPers | |:--------------------------:|:---------:|:-----:|:-------------:| | Digikala User Comments | 81.74* | 80.74 | - | | SnappFood User Comments | 88.12* | 87.87 | - | | SentiPers (Multi Class) | 71.11* | - | 69.33 | | SentiPers (Binary Class) | 92.13* | - | 91.98 | ### Text Classification (TC) task | Dataset | ParsBERT | mBERT | |:-----------------:|:--------:|:-----:| | Digikala Magazine | 93.59* | 90.72 | | Persian News | 97.19* | 95.79 | ### Named Entity Recognition (NER) task | Dataset | ParsBERT | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF | |:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:| | PEYMA | 93.10* | 86.64 | - | 90.59 | - | 84.00 | - | | ARMAN | 98.79* | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 | **If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference** ## How to use ### TensorFlow 2.0 ```python from transformers import AutoConfig, AutoTokenizer, TFAutoModel config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد می‌توانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است." tokenizer.tokenize(text) >>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.'] ``` ### Pytorch ```python from transformers import AutoConfig, AutoTokenizer, AutoModel config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased") ``` ## NLP Tasks Tutorial Coming soon stay tuned ## Cite Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research: ```markdown @article{ParsBERT, title={ParsBERT: Transformer-based Model for Persian Language Understanding}, author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri}, journal={ArXiv}, year={2020}, volume={abs/2005.12515} } ``` ## Acknowledgments We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources. ## Contributors - Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi) - Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam) - Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi) - Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri) - Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/) ## Releases ### Release v0.1 (May 27, 2019) This is the first version of our ParsBERT based on BERT_BASE

facebook/musicgen-small

facebook

transformers

text-to-audio

--- inference: true tags: - musicgen license: cc-by-nc-4.0 pipeline_tag: text-to-audio widget: - text: "a funky house with 80s hip hop vibes" example_title: "Prompt 1" - text: "a chill song with influences from lofi, chillstep and downtempo" example_title: "Prompt 2" - text: "a catchy beat for a podcast intro" example_title: "Prompt 3" --- # MusicGen - Small - 300M MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. Four checkpoints are released: - [**small** (this checkpoint)](https://huggingface.co/facebook/musicgen-small) - [medium](https://huggingface.co/facebook/musicgen-medium) - [large](https://huggingface.co/facebook/musicgen-large) - [melody](https://huggingface.co/facebook/musicgen-melody) ## Example Try out MusicGen yourself! * Audiocraft Colab: <a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Colab: <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> </a> * Hugging Face Demo: <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen"> <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/> </a> ## 🤗 Transformers Usage You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards. 1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy: ``` pip install --upgrade pip pip install --upgrade transformers scipy ``` 2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code! ```python from transformers import pipeline import scipy synthesiser = pipeline("text-to-audio", "facebook/musicgen-small") music = synthesiser("lo-fi music with a soothing melody", forward_params={"do_sample": True}) scipy.io.wavfile.write("musicgen_out.wav", rate=music["sampling_rate"], data=music["audio"]) ``` 3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control. ```python from transformers import AutoProcessor, MusicgenForConditionalGeneration processor = AutoProcessor.from_pretrained("facebook/musicgen-small") model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-small") inputs = processor( text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"], padding=True, return_tensors="pt", ) audio_values = model.generate(**inputs, max_new_tokens=256) ``` 3. Listen to the audio samples either in an ipynb notebook: ```python from IPython.display import Audio sampling_rate = model.config.audio_encoder.sampling_rate Audio(audio_values[0].numpy(), rate=sampling_rate) ``` Or save them as a `.wav` file using a third-party library, e.g. `scipy`: ```python import scipy sampling_rate = model.config.audio_encoder.sampling_rate scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values[0, 0].numpy()) ``` For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen). ## Audiocraft Usage You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft): 1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft) ``` pip install git+https://github.com/facebookresearch/audiocraft.git ``` 2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed: ``` apt-get install ffmpeg ``` 3. Run the following Python code: ```py from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write model = MusicGen.get_pretrained("small") model.set_generation_params(duration=8) # generate 8 seconds. descriptions = ["happy rock", "energetic EDM"] wav = model.generate(descriptions) # generates 2 samples. for idx, one_wav in enumerate(wav): # Will save under {idx}.wav, with loudness normalization at -14 db LUFS. audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness") ``` ## Model details **Organization developing the model:** The FAIR team of Meta AI. **Model date:** MusicGen was trained between April 2023 and May 2023. **Model version:** This is the version 1 of the model. **Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation. **Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284). **Citation details:** ``` @misc{copet2023simple, title={Simple and Controllable Music Generation}, author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez}, year={2023}, eprint={2306.05284}, archivePrefix={arXiv}, primaryClass={cs.SD} } ``` **License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0. **Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue. ## Intended use **Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including: - Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science - Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs **Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models. **Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. ## Metrics **Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark: - Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish) - Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST) - CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes: - Overall quality of the music samples; - Text relevance to the provided text input; - Adherence to the melody for melody-guided music generation. More details on performance measures and human studies can be found in the paper. **Decision thresholds:** Not applicable. ## Evaluation datasets The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set. ## Training datasets The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing. ## Evaluation results Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper. | Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity | |---|---|---|---|---| | **facebook/musicgen-small** | 4.88 | 1.42 | 0.27 | - | | facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - | | facebook/musicgen-large | 5.48 | 1.37 | 0.28 | - | | facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 | More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section. ## Limitations and biases **Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model. **Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs). **Limitations:** - The model is not able to generate realistic vocals. - The model has been trained with English descriptions and will not perform as well in other languages. - The model does not perform equally well for all music styles and cultures. - The model sometimes generates end of songs, collapsing to silence. - It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results. **Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive. **Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data. **Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.

valhalla/bart-large-finetuned-squadv1

valhalla

transformers

question-answering

--- datasets: - squad --- # BART-LARGE finetuned on SQuADv1 This is bart-large model finetuned on SQuADv1 dataset for question answering task ## Model details BART was propsed in the [paper](https://arxiv.org/abs/1910.13461) **BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**. BART is a seq2seq model intended for both NLG and NLU tasks. To use BART for question answering tasks, we feed the complete document into the encoder and decoder, and use the top hidden state of the decoder as a representation for each word. This representation is used to classify the token. As given in the paper bart-large achives comparable to ROBERTa on SQuAD. Another notable thing about BART is that it can handle sequences with upto 1024 tokens. | Param | #Value | |---------------------|--------| | encoder layers | 12 | | decoder layers | 12 | | hidden size | 4096 | | num attetion heads | 16 | | on disk size | 1.63GB | ## Model training This model was trained on google colab v100 GPU. You can find the fine-tuning colab here [](https://colab.research.google.com/drive/1I5cK1M_0dLaf5xoewh6swcm5nAInfwHy?usp=sharing). ## Results The results are actually slightly worse than given in the paper. In the paper the authors mentioned that bart-large achieves 88.8 EM and 94.6 F1 | Metric | #Value | |--------|--------| | EM | 86.8022| | F1 | 92.7342| ## Model in Action 🚀 ```python3 from transformers import BartTokenizer, BartForQuestionAnswering import torch tokenizer = BartTokenizer.from_pretrained('valhalla/bart-large-finetuned-squadv1') model = BartForQuestionAnswering.from_pretrained('valhalla/bart-large-finetuned-squadv1') question, text = "Who was Jim Henson?", "Jim Henson was a nice puppet" encoding = tokenizer(question, text, return_tensors='pt') input_ids = encoding['input_ids'] attention_mask = encoding['attention_mask'] start_scores, end_scores = model(input_ids, attention_mask=attention_mask, output_attentions=False)[:2] all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0]) answer = ' '.join(all_tokens[torch.argmax(start_scores) : torch.argmax(end_scores)+1]) answer = tokenizer.convert_tokens_to_ids(answer.split()) answer = tokenizer.decode(answer) #answer => 'a nice puppet' ``` > Created with ❤️ by Suraj Patil [](https://github.com/patil-suraj/) [](https://twitter.com/psuraj28)

microsoft/DialoGPT-medium

microsoft

transformers

text-generation

--- thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png tags: - conversational license: mit --- ## A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT) DialoGPT is a SOTA large-scale pretrained dialogue response generation model for multiturn conversations. The [human evaluation results](https://github.com/dreasysnail/Dialogpt_dev#human-evaluation) indicate that the response generated from DialoGPT is comparable to human response quality under a single-turn conversation Turing test. The model is trained on 147M multi-turn dialogue from Reddit discussion thread. * Multi-turn generation examples from an interactive environment: |Role | Response | |---------|--------| |User | Does money buy happiness? | | Bot | Depends how much money you spend on it .| |User | What is the best way to buy happiness ? | | Bot | You just have to be a millionaire by your early 20s, then you can be happy . | |User |This is so difficult ! | | Bot | You have no idea how hard it is to be a millionaire and happy . There is a reason the rich have a lot of money | Please find the information about preprocessing, training and full details of the DialoGPT in the [original DialoGPT repository](https://github.com/microsoft/DialoGPT) ArXiv paper: [https://arxiv.org/abs/1911.00536](https://arxiv.org/abs/1911.00536) ### How to use Now we are ready to try out how the model works as a chatting partner! ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium") model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium") # Let's chat for 5 lines for step in range(5): # encode the new user input, add the eos_token and return a tensor in Pytorch new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') # append the new user input tokens to the chat history bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids # generated a response while limiting the total chat history to 1000 tokens, chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) # pretty print last ouput tokens from bot print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```

emrecan/bert-base-turkish-cased-mean-nli-stsb-tr

emrecan

sentence-transformers

sentence-similarity

--- language: - tr pipeline_tag: sentence-similarity license: apache-2.0 tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - nli_tr - emrecan/stsb-mt-turkish widget: source_sentence: "Bu çok mutlu bir kişi" sentences: - "Bu mutlu bir köpek" - "Bu sevincinden havalara uçan bir insan" - "Çok kar yağıyor" --- # emrecan/bert-base-turkish-cased-mean-nli-stsb-tr This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model was trained on Turkish machine translated versions of [NLI](https://huggingface.co/datasets/nli_tr) and [STS-b](https://huggingface.co/datasets/emrecan/stsb-mt-turkish) datasets, using example [training scripts]( https://github.com/UKPLab/sentence-transformers/tree/master/examples/training) from sentence-transformers GitHub repository. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"] model = SentenceTransformer('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["Bu örnek bir cümle", "Her cümle vektöre çevriliyor"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr') model = AutoModel.from_pretrained('emrecan/bert-base-turkish-cased-mean-nli-stsb-tr') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results Evaluation results on test and development sets are given below: | Split | Epoch | cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman | |------------|-------|----------------|-----------------|-------------------|--------------------|-------------------|--------------------|-------------|--------------| | test | - | 0.834 | 0.830 | 0.820 | 0.819 | 0.819 | 0.818 | 0.799 | 0.789 | | validation | 1 | 0.850 | 0.848 | 0.831 | 0.835 | 0.83 | 0.83 | 0.80 | 0.806 | | validation | 2 | 0.857 | 0.857 | 0.844 | 0.848 | 0.844 | 0.848 | 0.813 | 0.810 | | validation | 3 | 0.860 | 0.859 | 0.846 | 0.851 | 0.846 | 0.850 | 0.825 | 0.822 | | validation | 4 | 0.859 | 0.860 | 0.846 | 0.851 | 0.846 | 0.851 | 0.825 | 0.823 | ## Training Training scripts [`training_nli_v2.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/nli/training_nli_v2.py) and [`training_stsbenchmark_continue_training.py`](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) were used to train the model. The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 200, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 144, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

FacebookAI/xlm-roberta-large-finetuned-conll03-english

FacebookAI

transformers

token-classification

--- language: - multilingual - af - am - ar - as - az - be - bg - bn - br - bs - ca - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fr - fy - ga - gd - gl - gu - ha - he - hi - hr - hu - hy - id - is - it - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lo - lt - lv - mg - mk - ml - mn - mr - ms - my - ne - nl - no - om - or - pa - pl - ps - pt - ro - ru - sa - sd - si - sk - sl - so - sq - sr - su - sv - sw - ta - te - th - tl - tr - ug - uk - ur - uz - vi - xh - yi - zh --- # xlm-roberta-large-finetuned-conll03-english # Table of Contents 1. [Model Details](#model-details) 2. [Uses](#uses) 3. [Bias, Risks, and Limitations](#bias-risks-and-limitations) 4. [Training](#training) 5. [Evaluation](#evaluation) 6. [Environmental Impact](#environmental-impact) 7. [Technical Specifications](#technical-specifications) 8. [Citation](#citation) 9. [Model Card Authors](#model-card-authors) 10. [How To Get Started With the Model](#how-to-get-started-with-the-model) # Model Details ## Model Description The XLM-RoBERTa model was proposed in [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov. It is based on Facebook's RoBERTa model released in 2019. It is a large multi-lingual language model, trained on 2.5TB of filtered CommonCrawl data. This model is [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) fine-tuned with the [conll2003](https://huggingface.co/datasets/conll2003) dataset in English. - **Developed by:** See [associated paper](https://arxiv.org/abs/1911.02116) - **Model type:** Multi-lingual language model - **Language(s) (NLP) or Countries (images):** XLM-RoBERTa is a multilingual model trained on 100 different languages; see [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) for full list; model is fine-tuned on a dataset in English - **License:** More information needed - **Related Models:** [RoBERTa](https://huggingface.co/roberta-base), [XLM](https://huggingface.co/docs/transformers/model_doc/xlm) - **Parent Model:** [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) - **Resources for more information:** -[GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/xlmr) -[Associated Paper](https://arxiv.org/abs/1911.02116) # Uses ## Direct Use The model is a language model. The model can be used for token classification, a natural language understanding task in which a label is assigned to some tokens in a text. ## Downstream Use Potential downstream use cases include Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging. To learn more about token classification and other potential downstream use cases, see the Hugging Face [token classification docs](https://huggingface.co/tasks/token-classification). ## Out-of-Scope Use The model should not be used to intentionally create hostile or alienating environments for people. # Bias, Risks, and Limitations **CONTENT WARNING: Readers should be made aware that language generated by this model may be disturbing or offensive to some and may propagate historical and current stereotypes.** Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). In the context of tasks relevant to this model, [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf) explore social biases in NER systems for English and find that there is systematic bias in existing NER systems in that they fail to identify named entities from different demographic groups (though this paper did not look at BERT). For example, using a sample sentence from [Mishra et al. (2020)](https://arxiv.org/pdf/2008.03415.pdf): ```python >>> from transformers import pipeline >>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> classifier = pipeline("ner", model=model, tokenizer=tokenizer) >>> classifier("Alya told Jasmine that Andrew could pay with cash..") [{'end': 2, 'entity': 'I-PER', 'index': 1, 'score': 0.9997861, 'start': 0, 'word': '▁Al'}, {'end': 4, 'entity': 'I-PER', 'index': 2, 'score': 0.9998591, 'start': 2, 'word': 'ya'}, {'end': 16, 'entity': 'I-PER', 'index': 4, 'score': 0.99995816, 'start': 10, 'word': '▁Jasmin'}, {'end': 17, 'entity': 'I-PER', 'index': 5, 'score': 0.9999584, 'start': 16, 'word': 'e'}, {'end': 29, 'entity': 'I-PER', 'index': 7, 'score': 0.99998057, 'start': 23, 'word': '▁Andrew'}] ``` ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. # Training See the following resources for training data and training procedure details: - [XLM-RoBERTa-large model card](https://huggingface.co/xlm-roberta-large) - [CoNLL-2003 data card](https://huggingface.co/datasets/conll2003) - [Associated paper](https://arxiv.org/pdf/1911.02116.pdf) # Evaluation See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for evaluation details. # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** 500 32GB Nvidia V100 GPUs (from the [associated paper](https://arxiv.org/pdf/1911.02116.pdf)) - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications See the [associated paper](https://arxiv.org/pdf/1911.02116.pdf) for further details. # Citation **BibTeX:** ```bibtex @article{conneau2019unsupervised, title={Unsupervised Cross-lingual Representation Learning at Scale}, author={Conneau, Alexis and Khandelwal, Kartikay and Goyal, Naman and Chaudhary, Vishrav and Wenzek, Guillaume and Guzm{\'a}n, Francisco and Grave, Edouard and Ott, Myle and Zettlemoyer, Luke and Stoyanov, Veselin}, journal={arXiv preprint arXiv:1911.02116}, year={2019} } ``` **APA:** - Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., ... & Stoyanov, V. (2019). Unsupervised cross-lingual representation learning at scale. arXiv preprint arXiv:1911.02116. # Model Card Authors This model card was written by the team at Hugging Face. # How to Get Started with the Model Use the code below to get started with the model. You can use this model directly within a pipeline for NER. <details> <summary> Click to expand </summary> ```python >>> from transformers import AutoTokenizer, AutoModelForTokenClassification >>> from transformers import pipeline >>> tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english") >>> classifier = pipeline("ner", model=model, tokenizer=tokenizer) >>> classifier("Hello I'm Omar and I live in Zürich.") [{'end': 14, 'entity': 'I-PER', 'index': 5, 'score': 0.9999175, 'start': 10, 'word': '▁Omar'}, {'end': 35, 'entity': 'I-LOC', 'index': 10, 'score': 0.9999906, 'start': 29, 'word': '▁Zürich'}] ``` </details>

sentence-transformers/bert-large-nli-stsb-mean-tokens

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/bert-large-nli-stsb-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/bert-large-nli-stsb-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/bert-large-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/bert-large-nli-stsb-mean-tokens') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/bert-large-nli-stsb-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

ptx0/terminus-xl-velocity-training

ptx0

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: "ptx0/terminus-xl-velocity-v2" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - full inference: true widget: - text: 'Alien planet, strange rock formations, glowing plants, bizarre creatures, surreal atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_0_0.png - text: 'Alien planet, strange rock formations, glowing plants, bizarre creatures, surreal atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_1_1.png - text: 'Alien planet, strange rock formations, glowing plants, bizarre creatures, surreal atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_2_2.png - text: 'Alien marketplace, bizarre creatures, exotic goods, vibrant colors, otherworldly atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_3_0.png - text: 'Alien marketplace, bizarre creatures, exotic goods, vibrant colors, otherworldly atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_4_1.png - text: 'Alien marketplace, bizarre creatures, exotic goods, vibrant colors, otherworldly atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_5_2.png - text: 'Child holding a balloon, happy expression, colorful balloons, sunny day, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_6_0.png - text: 'Child holding a balloon, happy expression, colorful balloons, sunny day, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_7_1.png - text: 'Child holding a balloon, happy expression, colorful balloons, sunny day, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_8_2.png - text: 'a 4-panel comic strip showing an orange cat saying the words ''HELP'' and ''LASAGNA''' parameters: negative_prompt: '''' output: url: ./assets/image_9_0.png - text: 'a 4-panel comic strip showing an orange cat saying the words ''HELP'' and ''LASAGNA''' parameters: negative_prompt: '''' output: url: ./assets/image_10_1.png - text: 'a 4-panel comic strip showing an orange cat saying the words ''HELP'' and ''LASAGNA''' parameters: negative_prompt: '''' output: url: ./assets/image_11_2.png - text: 'a hand is holding a comic book with a cover that reads ''The Adventures of Superhero''' parameters: negative_prompt: '''' output: url: ./assets/image_12_0.png - text: 'a hand is holding a comic book with a cover that reads ''The Adventures of Superhero''' parameters: negative_prompt: '''' output: url: ./assets/image_13_1.png - text: 'a hand is holding a comic book with a cover that reads ''The Adventures of Superhero''' parameters: negative_prompt: '''' output: url: ./assets/image_14_2.png - text: 'Underground cave filled with crystals, glowing lights, reflective surfaces, fantasy environment, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_15_0.png - text: 'Underground cave filled with crystals, glowing lights, reflective surfaces, fantasy environment, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_16_1.png - text: 'Underground cave filled with crystals, glowing lights, reflective surfaces, fantasy environment, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_17_2.png - text: 'Bustling cyberpunk bazaar, vendors, neon signs, advanced tech, crowded, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_18_0.png - text: 'Bustling cyberpunk bazaar, vendors, neon signs, advanced tech, crowded, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_19_1.png - text: 'Bustling cyberpunk bazaar, vendors, neon signs, advanced tech, crowded, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_20_2.png - text: 'Cyberpunk hacker in a dark room, neon glow, multiple screens, intense focus, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_21_0.png - text: 'Cyberpunk hacker in a dark room, neon glow, multiple screens, intense focus, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_22_1.png - text: 'Cyberpunk hacker in a dark room, neon glow, multiple screens, intense focus, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_23_2.png - text: 'a cybernetic anne of green gables with neural implant and bio mech augmentations' parameters: negative_prompt: '''' output: url: ./assets/image_24_0.png - text: 'a cybernetic anne of green gables with neural implant and bio mech augmentations' parameters: negative_prompt: '''' output: url: ./assets/image_25_1.png - text: 'a cybernetic anne of green gables with neural implant and bio mech augmentations' parameters: negative_prompt: '''' output: url: ./assets/image_26_2.png - text: 'Post-apocalyptic cityscape, ruined buildings, overgrown vegetation, dark and gritty, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_27_0.png - text: 'Post-apocalyptic cityscape, ruined buildings, overgrown vegetation, dark and gritty, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_28_1.png - text: 'Post-apocalyptic cityscape, ruined buildings, overgrown vegetation, dark and gritty, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_29_2.png - text: 'Magical castle in a lush forest, glowing windows, fantasy architecture, high resolution, detailed textures' parameters: negative_prompt: '''' output: url: ./assets/image_30_0.png - text: 'Magical castle in a lush forest, glowing windows, fantasy architecture, high resolution, detailed textures' parameters: negative_prompt: '''' output: url: ./assets/image_31_1.png - text: 'Magical castle in a lush forest, glowing windows, fantasy architecture, high resolution, detailed textures' parameters: negative_prompt: '''' output: url: ./assets/image_32_2.png - text: 'Ruins of an ancient temple in an enchanted forest, glowing runes, mystical creatures, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_33_0.png - text: 'Ruins of an ancient temple in an enchanted forest, glowing runes, mystical creatures, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_34_1.png - text: 'Ruins of an ancient temple in an enchanted forest, glowing runes, mystical creatures, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_35_2.png - text: 'Mystical forest, glowing plants, fairies, magical creatures, fantasy art, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_36_0.png - text: 'Mystical forest, glowing plants, fairies, magical creatures, fantasy art, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_37_1.png - text: 'Mystical forest, glowing plants, fairies, magical creatures, fantasy art, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_38_2.png - text: 'Magical garden with glowing flowers, fairies, serene atmosphere, detailed plants, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_39_0.png - text: 'Magical garden with glowing flowers, fairies, serene atmosphere, detailed plants, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_40_1.png - text: 'Magical garden with glowing flowers, fairies, serene atmosphere, detailed plants, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_41_2.png - text: 'Whimsical garden filled with fairies, magical plants, sparkling lights, serene atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_42_0.png - text: 'Whimsical garden filled with fairies, magical plants, sparkling lights, serene atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_43_1.png - text: 'Whimsical garden filled with fairies, magical plants, sparkling lights, serene atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_44_2.png - text: 'Majestic dragon soaring through the sky, detailed scales, dynamic pose, fantasy art, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_45_0.png - text: 'Majestic dragon soaring through the sky, detailed scales, dynamic pose, fantasy art, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_46_1.png - text: 'Majestic dragon soaring through the sky, detailed scales, dynamic pose, fantasy art, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_47_2.png - text: 'Fantasy world, floating islands in the sky, waterfalls, lush vegetation, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_48_0.png - text: 'Fantasy world, floating islands in the sky, waterfalls, lush vegetation, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_49_1.png - text: 'Fantasy world, floating islands in the sky, waterfalls, lush vegetation, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_50_2.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_51_0.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_52_1.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_53_2.png - text: 'Space battle scene, starships fighting, laser beams, explosions, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_54_0.png - text: 'Space battle scene, starships fighting, laser beams, explosions, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_55_1.png - text: 'Space battle scene, starships fighting, laser beams, explosions, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_56_2.png - text: 'Abandoned fairground at night, eerie rides, ghostly figures, fog, dark atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_57_0.png - text: 'Abandoned fairground at night, eerie rides, ghostly figures, fog, dark atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_58_1.png - text: 'Abandoned fairground at night, eerie rides, ghostly figures, fog, dark atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_59_2.png - text: 'Spooky haunted mansion on a hill, dark and eerie, glowing windows, ghostly atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_60_0.png - text: 'Spooky haunted mansion on a hill, dark and eerie, glowing windows, ghostly atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_61_1.png - text: 'Spooky haunted mansion on a hill, dark and eerie, glowing windows, ghostly atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_62_2.png - text: 'a hardcover physics textbook that is called PHYSICS FOR DUMMIES' parameters: negative_prompt: '''' output: url: ./assets/image_63_0.png - text: 'a hardcover physics textbook that is called PHYSICS FOR DUMMIES' parameters: negative_prompt: '''' output: url: ./assets/image_64_1.png - text: 'a hardcover physics textbook that is called PHYSICS FOR DUMMIES' parameters: negative_prompt: '''' output: url: ./assets/image_65_2.png - text: 'Epic medieval battle, knights in armor, dynamic action, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_66_0.png - text: 'Epic medieval battle, knights in armor, dynamic action, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_67_1.png - text: 'Epic medieval battle, knights in armor, dynamic action, detailed landscape, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_68_2.png - text: 'Bustling medieval market with merchants, knights, and jesters, vibrant colors, detailed' parameters: negative_prompt: '''' output: url: ./assets/image_69_0.png - text: 'Bustling medieval market with merchants, knights, and jesters, vibrant colors, detailed' parameters: negative_prompt: '''' output: url: ./assets/image_70_1.png - text: 'Bustling medieval market with merchants, knights, and jesters, vibrant colors, detailed' parameters: negative_prompt: '''' output: url: ./assets/image_71_2.png - text: 'Cozy medieval tavern, warm firelight, adventurers drinking, detailed interior, rustic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_72_0.png - text: 'Cozy medieval tavern, warm firelight, adventurers drinking, detailed interior, rustic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_73_1.png - text: 'Cozy medieval tavern, warm firelight, adventurers drinking, detailed interior, rustic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_74_2.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_75_0.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_76_1.png - text: 'Futuristic city skyline at night, neon lights, cyberpunk style, high contrast, sharp focus' parameters: negative_prompt: '''' output: url: ./assets/image_77_2.png - text: 'Forest with neon-lit trees, glowing plants, bioluminescence, surreal atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_78_0.png - text: 'Forest with neon-lit trees, glowing plants, bioluminescence, surreal atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_79_1.png - text: 'Forest with neon-lit trees, glowing plants, bioluminescence, surreal atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_80_2.png - text: 'Bright neon sign in a busy city street, ''Open 24 Hours'', bold typography, glowing lights' parameters: negative_prompt: '''' output: url: ./assets/image_81_0.png - text: 'Bright neon sign in a busy city street, ''Open 24 Hours'', bold typography, glowing lights' parameters: negative_prompt: '''' output: url: ./assets/image_82_1.png - text: 'Bright neon sign in a busy city street, ''Open 24 Hours'', bold typography, glowing lights' parameters: negative_prompt: '''' output: url: ./assets/image_83_2.png - text: 'Vibrant neon sign, ''Bar'', bold typography, dark background, glowing lights, detailed design' parameters: negative_prompt: '''' output: url: ./assets/image_84_0.png - text: 'Vibrant neon sign, ''Bar'', bold typography, dark background, glowing lights, detailed design' parameters: negative_prompt: '''' output: url: ./assets/image_85_1.png - text: 'Vibrant neon sign, ''Bar'', bold typography, dark background, glowing lights, detailed design' parameters: negative_prompt: '''' output: url: ./assets/image_86_2.png - text: 'Pirate ship on the high seas, stormy weather, detailed sails, dramatic waves, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_87_0.png - text: 'Pirate ship on the high seas, stormy weather, detailed sails, dramatic waves, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_88_1.png - text: 'Pirate ship on the high seas, stormy weather, detailed sails, dramatic waves, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_89_2.png - text: 'Pirate discovering a treasure chest, detailed gold coins, tropical island, dramatic lighting' parameters: negative_prompt: '''' output: url: ./assets/image_90_0.png - text: 'Pirate discovering a treasure chest, detailed gold coins, tropical island, dramatic lighting' parameters: negative_prompt: '''' output: url: ./assets/image_91_1.png - text: 'Pirate discovering a treasure chest, detailed gold coins, tropical island, dramatic lighting' parameters: negative_prompt: '''' output: url: ./assets/image_92_2.png - text: 'a photograph of a woman experiencing a psychedelic trip. trippy, 8k, uhd, fractal' parameters: negative_prompt: '''' output: url: ./assets/image_93_0.png - text: 'a photograph of a woman experiencing a psychedelic trip. trippy, 8k, uhd, fractal' parameters: negative_prompt: '''' output: url: ./assets/image_94_1.png - text: 'a photograph of a woman experiencing a psychedelic trip. trippy, 8k, uhd, fractal' parameters: negative_prompt: '''' output: url: ./assets/image_95_2.png - text: 'Cozy cafe on a rainy day, people sipping coffee, warm lights, reflections on wet pavement, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_96_0.png - text: 'Cozy cafe on a rainy day, people sipping coffee, warm lights, reflections on wet pavement, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_97_1.png - text: 'Cozy cafe on a rainy day, people sipping coffee, warm lights, reflections on wet pavement, photorealistic' parameters: negative_prompt: '''' output: url: ./assets/image_98_2.png - text: '1980s arcade, neon lights, vintage game machines, kids playing, vibrant colors, nostalgic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_99_0.png - text: '1980s arcade, neon lights, vintage game machines, kids playing, vibrant colors, nostalgic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_100_1.png - text: '1980s arcade, neon lights, vintage game machines, kids playing, vibrant colors, nostalgic atmosphere' parameters: negative_prompt: '''' output: url: ./assets/image_101_2.png - text: '1980s game room with vintage arcade machines, neon lights, vibrant colors, nostalgic feel' parameters: negative_prompt: '''' output: url: ./assets/image_102_0.png - text: '1980s game room with vintage arcade machines, neon lights, vibrant colors, nostalgic feel' parameters: negative_prompt: '''' output: url: ./assets/image_103_1.png - text: '1980s game room with vintage arcade machines, neon lights, vibrant colors, nostalgic feel' parameters: negative_prompt: '''' output: url: ./assets/image_104_2.png - text: 'Robot blacksmith forging metal, sparks flying, detailed workshop, futuristic and medieval blend' parameters: negative_prompt: '''' output: url: ./assets/image_105_0.png - text: 'Robot blacksmith forging metal, sparks flying, detailed workshop, futuristic and medieval blend' parameters: negative_prompt: '''' output: url: ./assets/image_106_1.png - text: 'Robot blacksmith forging metal, sparks flying, detailed workshop, futuristic and medieval blend' parameters: negative_prompt: '''' output: url: ./assets/image_107_2.png - text: 'Sleek robot performing a dance, futuristic theater, holographic effects, detailed, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_108_0.png - text: 'Sleek robot performing a dance, futuristic theater, holographic effects, detailed, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_109_1.png - text: 'Sleek robot performing a dance, futuristic theater, holographic effects, detailed, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_110_2.png - text: 'High-tech factory where robots are assembled, detailed machinery, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_111_0.png - text: 'High-tech factory where robots are assembled, detailed machinery, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_112_1.png - text: 'High-tech factory where robots are assembled, detailed machinery, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_113_2.png - text: 'Garden tended by robots, mechanical plants, colorful flowers, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_114_0.png - text: 'Garden tended by robots, mechanical plants, colorful flowers, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_115_1.png - text: 'Garden tended by robots, mechanical plants, colorful flowers, futuristic setting, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_116_2.png - text: 'Cute robotic pet, futuristic home, sleek design, detailed features, friendly and animated' parameters: negative_prompt: '''' output: url: ./assets/image_117_0.png - text: 'Cute robotic pet, futuristic home, sleek design, detailed features, friendly and animated' parameters: negative_prompt: '''' output: url: ./assets/image_118_1.png - text: 'Cute robotic pet, futuristic home, sleek design, detailed features, friendly and animated' parameters: negative_prompt: '''' output: url: ./assets/image_119_2.png - text: 'cctv trail camera night time security picture of a wendigo in the woods' parameters: negative_prompt: '''' output: url: ./assets/image_120_0.png - text: 'cctv trail camera night time security picture of a wendigo in the woods' parameters: negative_prompt: '''' output: url: ./assets/image_121_1.png - text: 'cctv trail camera night time security picture of a wendigo in the woods' parameters: negative_prompt: '''' output: url: ./assets/image_122_2.png - text: 'Astronaut exploring an alien planet, detailed landscape, futuristic suit, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_123_0.png - text: 'Astronaut exploring an alien planet, detailed landscape, futuristic suit, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_124_1.png - text: 'Astronaut exploring an alien planet, detailed landscape, futuristic suit, cosmic background' parameters: negative_prompt: '''' output: url: ./assets/image_125_2.png - text: 'Futuristic space station orbiting a distant exoplanet, sleek design, detailed structures, cosmic backdrop' parameters: negative_prompt: '''' output: url: ./assets/image_126_0.png - text: 'Futuristic space station orbiting a distant exoplanet, sleek design, detailed structures, cosmic backdrop' parameters: negative_prompt: '''' output: url: ./assets/image_127_1.png - text: 'Futuristic space station orbiting a distant exoplanet, sleek design, detailed structures, cosmic backdrop' parameters: negative_prompt: '''' output: url: ./assets/image_128_2.png - text: 'a person holding a sign that reads ''SOON''' parameters: negative_prompt: '''' output: url: ./assets/image_129_0.png - text: 'a person holding a sign that reads ''SOON''' parameters: negative_prompt: '''' output: url: ./assets/image_130_1.png - text: 'a person holding a sign that reads ''SOON''' parameters: negative_prompt: '''' output: url: ./assets/image_131_2.png - text: 'Steampunk airship in the sky, intricate design, Victorian aesthetics, dynamic scene, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_132_0.png - text: 'Steampunk airship in the sky, intricate design, Victorian aesthetics, dynamic scene, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_133_1.png - text: 'Steampunk airship in the sky, intricate design, Victorian aesthetics, dynamic scene, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_134_2.png - text: 'Steampunk inventor in a workshop, intricate gadgets, Victorian attire, mechanical arm, goggles' parameters: negative_prompt: '''' output: url: ./assets/image_135_0.png - text: 'Steampunk inventor in a workshop, intricate gadgets, Victorian attire, mechanical arm, goggles' parameters: negative_prompt: '''' output: url: ./assets/image_136_1.png - text: 'Steampunk inventor in a workshop, intricate gadgets, Victorian attire, mechanical arm, goggles' parameters: negative_prompt: '''' output: url: ./assets/image_137_2.png - text: 'Stormy ocean with towering waves, dramatic skies, detailed water, intense atmosphere, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_138_0.png - text: 'Stormy ocean with towering waves, dramatic skies, detailed water, intense atmosphere, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_139_1.png - text: 'Stormy ocean with towering waves, dramatic skies, detailed water, intense atmosphere, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_140_2.png - text: 'Dramatic stormy sea, lighthouse in the distance, lightning striking, dark clouds, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_141_0.png - text: 'Dramatic stormy sea, lighthouse in the distance, lightning striking, dark clouds, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_142_1.png - text: 'Dramatic stormy sea, lighthouse in the distance, lightning striking, dark clouds, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_143_2.png - text: 'Graffiti artist creating a mural, vibrant colors, urban setting, dynamic action, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_144_0.png - text: 'Graffiti artist creating a mural, vibrant colors, urban setting, dynamic action, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_145_1.png - text: 'Graffiti artist creating a mural, vibrant colors, urban setting, dynamic action, high resolution' parameters: negative_prompt: '''' output: url: ./assets/image_146_2.png - text: 'Urban alleyway filled with vibrant graffiti art, tags and murals, realistic textures' parameters: negative_prompt: '''' output: url: ./assets/image_147_0.png - text: 'Urban alleyway filled with vibrant graffiti art, tags and murals, realistic textures' parameters: negative_prompt: '''' output: url: ./assets/image_148_1.png - text: 'Urban alleyway filled with vibrant graffiti art, tags and murals, realistic textures' parameters: negative_prompt: '''' output: url: ./assets/image_149_2.png - text: 'Urban street sign, ''Main Street'', bold typography, realistic textures, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_150_0.png - text: 'Urban street sign, ''Main Street'', bold typography, realistic textures, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_151_1.png - text: 'Urban street sign, ''Main Street'', bold typography, realistic textures, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_152_2.png - text: 'Classic car show with vintage vehicles, vibrant colors, nostalgic atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_153_0.png - text: 'Classic car show with vintage vehicles, vibrant colors, nostalgic atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_154_1.png - text: 'Classic car show with vintage vehicles, vibrant colors, nostalgic atmosphere, high detail' parameters: negative_prompt: '''' output: url: ./assets/image_155_2.png - text: 'Retro diner sign, ''Joe''s Diner'', classic 1950s design, neon lights, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_156_0.png - text: 'Retro diner sign, ''Joe''s Diner'', classic 1950s design, neon lights, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_157_1.png - text: 'Retro diner sign, ''Joe''s Diner'', classic 1950s design, neon lights, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_158_2.png - text: 'Vintage store sign with elaborate typography, ''Antique Shop'', hand-painted, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_159_0.png - text: 'Vintage store sign with elaborate typography, ''Antique Shop'', hand-painted, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_160_1.png - text: 'Vintage store sign with elaborate typography, ''Antique Shop'', hand-painted, weathered look' parameters: negative_prompt: '''' output: url: ./assets/image_161_2.png - text: 'a child wearing a pixar style wedding dress, in a play castle' parameters: negative_prompt: '''' output: url: ./assets/image_162_0.png - text: 'a child wearing a pixar style wedding dress, in a play castle' parameters: negative_prompt: '''' output: url: ./assets/image_163_1.png - text: 'a child wearing a pixar style wedding dress, in a play castle' parameters: negative_prompt: '''' output: url: ./assets/image_164_2.png - text: 'a cartoon bear in red shorts playing basketball with a sponge' parameters: negative_prompt: '''' output: url: ./assets/image_165_0.png - text: 'a cartoon bear in red shorts playing basketball with a sponge' parameters: negative_prompt: '''' output: url: ./assets/image_166_1.png - text: 'a cartoon bear in red shorts playing basketball with a sponge' parameters: negative_prompt: '''' output: url: ./assets/image_167_2.png - text: 'a superhero with a cape and a mask, fighting a dragon' parameters: negative_prompt: '''' output: url: ./assets/image_168_0.png - text: 'a superhero with a cape and a mask, fighting a dragon' parameters: negative_prompt: '''' output: url: ./assets/image_169_1.png - text: 'a superhero with a cape and a mask, fighting a dragon' parameters: negative_prompt: '''' output: url: ./assets/image_170_2.png - text: 'a dramatic scene with intense lighting showcasing a man and a woman in a tense conversation' parameters: negative_prompt: '''' output: url: ./assets/image_171_0.png - text: 'a dramatic scene with intense lighting showcasing a man and a woman in a tense conversation' parameters: negative_prompt: '''' output: url: ./assets/image_172_1.png - text: 'a dramatic scene with intense lighting showcasing a man and a woman in a tense conversation' parameters: negative_prompt: '''' output: url: ./assets/image_173_2.png - text: 'a group of people in a house, with a camera crew filming them' parameters: negative_prompt: '''' output: url: ./assets/image_174_0.png - text: 'a group of people in a house, with a camera crew filming them' parameters: negative_prompt: '''' output: url: ./assets/image_175_1.png - text: 'a group of people in a house, with a camera crew filming them' parameters: negative_prompt: '''' output: url: ./assets/image_176_2.png - text: 'a person in a lab coat holding a microphone stands in a forest, talking about the ecosystem' parameters: negative_prompt: '''' output: url: ./assets/image_177_0.png - text: 'a person in a lab coat holding a microphone stands in a forest, talking about the ecosystem' parameters: negative_prompt: '''' output: url: ./assets/image_178_1.png - text: 'a person in a lab coat holding a microphone stands in a forest, talking about the ecosystem' parameters: negative_prompt: '''' output: url: ./assets/image_179_2.png - text: 'a news anchor sitting at a desk, with a screen behind them showing a map of the world' parameters: negative_prompt: '''' output: url: ./assets/image_180_0.png - text: 'a news anchor sitting at a desk, with a screen behind them showing a map of the world' parameters: negative_prompt: '''' output: url: ./assets/image_181_1.png - text: 'a news anchor sitting at a desk, with a screen behind them showing a map of the world' parameters: negative_prompt: '''' output: url: ./assets/image_182_2.png - text: 'a soccer player kicking a ball into a goal, with a crowd cheering' parameters: negative_prompt: '''' output: url: ./assets/image_183_0.png - text: 'a soccer player kicking a ball into a goal, with a crowd cheering' parameters: negative_prompt: '''' output: url: ./assets/image_184_1.png - text: 'a soccer player kicking a ball into a goal, with a crowd cheering' parameters: negative_prompt: '''' output: url: ./assets/image_185_2.png - text: 'a man is holding a sign that says SOON' parameters: negative_prompt: '''' output: url: ./assets/image_186_0.png - text: 'a man is holding a sign that says SOON' parameters: negative_prompt: '''' output: url: ./assets/image_187_1.png - text: 'a man is holding a sign that says SOON' parameters: negative_prompt: '''' output: url: ./assets/image_188_2.png - text: 'a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side' parameters: negative_prompt: '''' output: url: ./assets/image_189_0.png - text: 'a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side' parameters: negative_prompt: '''' output: url: ./assets/image_190_1.png - text: 'a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side' parameters: negative_prompt: '''' output: url: ./assets/image_191_2.png --- # terminus-xl-velocity-training This is a full rank finetune derived from [ptx0/terminus-xl-velocity-v2](https://huggingface.co/ptx0/terminus-xl-velocity-v2). The main validation prompt used during training was: ``` a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side ``` ## Validation settings - CFG: `7.5` - CFG Rescale: `0.7` - Steps: `30` - Sampler: `euler` - Seed: `42` - Resolutions: `1024x1024,1152x960,896x1152` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 13 - Training steps: 23000 - Learning rate: 4e-07 - Effective batch size: 512 - Micro-batch size: 32 - Gradient accumulation steps: 2 - Number of GPUs: 8 - Prediction type: v_prediction - Rescaled betas zero SNR: True - Optimizer: AdamW, stochastic bf16 - Precision: Pure BF16 - Xformers: Enabled ## Datasets ### photo-concept-bucket - Repeats: 0 - Total number of images: ~557568 - Total number of aspect buckets: 5 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = "terminus-xl-velocity-training" prompt = "a cute anime character named toast holding a sign that says SOON, sitting next to a red square on her left side, and a transparent sphere on her right side" negative_prompt = "malformed, disgusting, overexposed, washed-out" pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt='', num_inference_steps=30, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1152, height=768, guidance_scale=7.5, guidance_rescale=0.7, ).images[0] image.save("output.png", format="PNG") ```

nguyenvulebinh/wav2vec2-base-vi

nguyenvulebinh

transformers

--- language: vi datasets: - youtube-vi-13k-hours tags: - speech license: cc-by-nc-4.0 --- # Vietnamese Self-Supervised Learning Wav2Vec2 model ## Model We use wav2vec2 architecture for doing Self-Supervised learning <img src="https://raw.githubusercontent.com/patrickvonplaten/scientific_images/master/wav2vec2.png" width=75% height=75%> ## Data Our self-supervised model is pre-trained on a massive audio set of 13k hours of Vietnamese youtube audio, which includes: - Clean audio - Noise audio - Conversation - Multi-gender and dialects ## Download We have already upload our pre-trained model to the Huggingface. The base model trained 35 epochs and the large model trained 20 epochs in about 30 days using TPU V3-8. - [Based version](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi) ~ 95M params - [Large version](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi) ~ 317M params ## Usage ```python from transformers import Wav2Vec2ForPreTraining, Wav2Vec2Processor model_name = 'nguyenvulebinh/wav2vec2-base-vi' # model_name = 'nguyenvulebinh/wav2vec2-large-vi' model = Wav2Vec2ForPreTraining.from_pretrained(model_name) processor = Wav2Vec2Processor.from_pretrained(model_name) ``` Since our model has the same architecture as the English wav2vec2 version, you can use [this notebook](https://colab.research.google.com/drive/1FjTsqbYKphl9kL-eILgUc-bl4zVThL8F?usp=sharing) for more information on how to fine-tune the model. ## Finetuned version ### VLSP 2020 ASR dataset Benchmark WER result on VLSP T1 testset: | | [base model](https://huggingface.co/nguyenvulebinh/wav2vec2-base-vi-vlsp2020) | [large model](https://huggingface.co/nguyenvulebinh/wav2vec2-large-vi-vlsp2020) | |---|---|---| |without LM| 8.66 | 6.90 | |with 5-grams LM| 6.53 | 5.32 | Usage ```python #pytorch #!pip install transformers==4.20.0 #!pip install https://github.com/kpu/kenlm/archive/master.zip #!pip install pyctcdecode==0.4.0 from transformers.file_utils import cached_path, hf_bucket_url from importlib.machinery import SourceFileLoader from transformers import Wav2Vec2ProcessorWithLM from IPython.lib.display import Audio import torchaudio import torch # Load model & processor model_name = "nguyenvulebinh/wav2vec2-base-vi-vlsp2020" # model_name = "nguyenvulebinh/wav2vec2-large-vi-vlsp2020" model = SourceFileLoader("model", cached_path(hf_bucket_url(model_name,filename="model_handling.py"))).load_module().Wav2Vec2ForCTC.from_pretrained(model_name) processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_name) # Load an example audio (16k) audio, sample_rate = torchaudio.load(cached_path(hf_bucket_url(model_name, filename="t2_0000006682.wav"))) input_data = processor.feature_extractor(audio[0], sampling_rate=16000, return_tensors='pt') # Infer output = model(**input_data) # Output transcript without LM print(processor.tokenizer.decode(output.logits.argmax(dim=-1)[0].detach().cpu().numpy())) # Output transcript with LM print(processor.decode(output.logits.cpu().detach().numpy()[0], beam_width=100).text) ``` ## Acknowledgment - We would like to thank the Google TPU Research Cloud (TRC) program and Soonson Kwon (Google ML Ecosystem programs Lead) for their support. - Special thanks to my colleagues at [VietAI](https://vietai.org/) and [VAIS](https://vais.vn/) for their advice. ## Contact nguyenvulebinh@gmail.com / binh@vietai.org [](https://twitter.com/intent/follow?screen_name=nguyenvulebinh)

timm/lcnet_050.ra2_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for lcnet_050.ra2_in1k A LCNet image classification model. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * RandAugment `RA2` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in [ResNet Strikes Back](https://arxiv.org/abs/2110.00476). * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 1.9 - GMACs: 0.0 - Activations (M): 1.3 - Image size: 224 x 224 - **Papers:** - PP-LCNet: A Lightweight CPU Convolutional Neural Network: https://arxiv.org/abs/2109.15099 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('lcnet_050.ra2_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'lcnet_050.ra2_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 16, 112, 112]) # torch.Size([1, 32, 56, 56]) # torch.Size([1, 64, 28, 28]) # torch.Size([1, 128, 14, 14]) # torch.Size([1, 256, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'lcnet_050.ra2_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 256, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @article{cui2021pp, title={PP-LCNet: A lightweight CPU convolutional neural network}, author={Cui, Cheng and Gao, Tingquan and Wei, Shengyu and Du, Yuning and Guo, Ruoyu and Dong, Shuilong and Lu, Bin and Zhou, Ying and Lv, Xueying and Liu, Qiwen and others}, journal={arXiv preprint arXiv:2109.15099}, year={2021} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ``` ```bibtex @inproceedings{wightman2021resnet, title={ResNet strikes back: An improved training procedure in timm}, author={Wightman, Ross and Touvron, Hugo and Jegou, Herve}, booktitle={NeurIPS 2021 Workshop on ImageNet: Past, Present, and Future} } ```

facebook/roberta-hate-speech-dynabench-r4-target

facebook

transformers

text-classification

--- language: en --- # LFTW R4 Target The R4 Target model from [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://arxiv.org/abs/2012.15761) ## Citation Information ```bibtex @inproceedings{vidgen2021lftw, title={Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection}, author={Bertie Vidgen and Tristan Thrush and Zeerak Waseem and Douwe Kiela}, booktitle={ACL}, year={2021} } ``` Thanks to Kushal Tirumala and Adina Williams for helping the authors put the model on the hub!

prithivida/grammar_error_correcter_v1

prithivida

transformers

text2text-generation

**This model is part of the Gramformer library** please refer to https://github.com/PrithivirajDamodaran/Gramformer/

minimaxir/sdxl-wrong-lora

minimaxir

diffusers

text-to-image

--- license: mit base_model: stabilityai/stable-diffusion-xl-base-1.0 tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - lora inference: true --- # sdxl-wrong-lora  A LoRA for SDXL 1.0 Base which improves output image quality after loading it and using `wrong` as a negative prompt during inference. You can demo image generation using this LoRA in [this Colab Notebook](https://colab.research.google.com/github/minimaxir/sdxl-experiments/blob/main/sdxl_image_generation.ipynb). The LoRA is also available in a `safetensors` format for other UIs such as A1111; however this LoRA was created using `diffusers` and I cannot guarantee its efficacy outside of it. Benefits of using this LoRA: - Higher detail in textures/fabrics, particularly at full 1024x1024 resolution. - Higher color saturation and vibrance. - Higher sharpness for blurry/background objects. - Better at anatomically-correct hands. - Less likely to have random artifacts. - Appears to allow the model to follow the input prompt with a more expected behavior, particularly with prompt weighting such as the [Compel](https://github.com/damian0815/compel) syntax. ## Usage The LoRA can be loaded using `load_lora_weights` like any other LoRA in `diffusers`: ```py import torch from diffusers import DiffusionPipeline, AutoencoderKL vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) base = DiffusionPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", vae=vae, torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) base.load_lora_weights("minimaxir/sdxl-wrong-lora") _ = base.to("cuda") ``` During image generation, use `wrong` as the negative prompt. That's it! ## Examples **Left image** is the base model output (no LoRA) + refiner, **right image** is base (w/ LoRA) + refiner + `wrong` negative prompt. Both generations use the same seed. I have also [released a Colab Notebook](https://colab.research.google.com/github/minimaxir/sdxl-experiments/blob/main/sdxl_wrong_comparison.ipynb) to generate these kinds of side-by-side comparison images, although the seeds listed will not give the same results since they were generated on a different GPU/CUDA than the Colab Notebook. `realistic human Shrek blogging at a computer workstation, hyperrealistic award-winning photo for vanity fair` (cfg = 13, seed = 56583700)  `pepperoni pizza in the shape of a heart, hyperrealistic award-winning professional food photography` (cfg = 13, seed = 75789081)  `presidential painting of realistic human Spongebob Squarepants wearing a suit, (oil on canvas)+++++` (cfg = 13, seed = 85588026)  `San Francisco panorama attacked by (one massive kitten)++++, hyperrealistic award-winning photo by the Associated Press` (cfg = 13, seed = 45454868)  `hyperrealistic death metal album cover featuring edgy moody realistic (human Super Mario)++, edgy and moody` (cfg = 13, seed = 30416580)  ## Methodology The methodology and motivation for creating this LoRA is similar to my [wrong SD 2.0 textual inversion embedding](https://huggingface.co/minimaxir/wrong_embedding_sd_2_0) by training on a balanced variety of undesirable outputs, except trained as a LoRA since textual inversion with SDXL is complicated. The base images were generated from SDXL itself, with some prompt weighting to emphasize undesirable attributes for test images. You can see the code to generate the wrong images [in this Jupyter Notebook](https://github.com/minimaxir/sdxl-experiments/blob/main/wrong_image_generator.ipynb). ## Notes - The intuitive way to think about how this LoRA works is that on training start, it indicates an undesirable area of the vast highdimensional latent space which the rest of the diffusion process will move away from. This may work more effectively than textual inversion but more testing needs to be done. - The description of this LoRA is very careful to not state that the output is objectively _better_ than not using LoRA, because everything is subjective and there are use cases where vibrant output is not desired. For most use cases, the output should be better desired however. - It's possible to use `not wrong` in the normal prompt itself but in testing it has not much effect. - You can use other negative prompts in conjunction with the `wrong` prompt but you may want to weight them appropriately. - All the Notebooks noted here are available [in this GitHub repo](https://github.com/minimaxir/sdxl-experiments).

ml6team/keyphrase-extraction-distilbert-inspec

ml6team

transformers

token-classification

--- language: en license: mit tags: - keyphrase-extraction datasets: - midas/inspec metrics: - seqeval widget: - text: "Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time. Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text." example_title: "Example 1" - text: "In this work, we explore how to learn task specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (up to 9.26 points in F1) over SOTA, when LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (up to 4.33 points inF1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition(NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks." example_title: "Example 2" model-index: - name: DeDeckerThomas/keyphrase-extraction-distilbert-inspec results: - task: type: keyphrase-extraction name: Keyphrase Extraction dataset: type: midas/inspec name: inspec metrics: - type: F1 (Seqeval) value: 0.509 name: F1 (Seqeval) - type: F1@M value: 0.490 name: F1@M --- # 🔑 Keyphrase Extraction Model: distilbert-inspec Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time ⏳. Here is where Artificial Intelligence 🤖 comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. ## 📓 Model Description This model uses [distilbert](https://huggingface.co/distilbert-base-uncased) as its base model and fine-tunes it on the [Inspec dataset](https://huggingface.co/datasets/midas/inspec). Keyphrase extraction models are transformer models fine-tuned as a token classification problem where each word in the document is classified as being part of a keyphrase or not. | Label | Description | | ----- | ------------------------------- | | B-KEY | At the beginning of a keyphrase | | I-KEY | Inside a keyphrase | | O | Outside a keyphrase | Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021). Sahrawat, Dhruva, Debanjan Mahata, Haimin Zhang, Mayank Kulkarni, Agniv Sharma, Rakesh Gosangi, Amanda Stent, Yaman Kumar, Rajiv Ratn Shah, and Roger Zimmermann. "Keyphrase extraction as sequence labeling using contextualized embeddings." In European Conference on Information Retrieval, pp. 328-335. Springer, Cham, 2020. ## ✋ Intended Uses & Limitations ### 🛑 Limitations * This keyphrase extraction model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out. * Only works for English documents. ### ❓ How To Use ```python from transformers import ( TokenClassificationPipeline, AutoModelForTokenClassification, AutoTokenizer, ) from transformers.pipelines import AggregationStrategy import numpy as np # Define keyphrase extraction pipeline class KeyphraseExtractionPipeline(TokenClassificationPipeline): def __init__(self, model, *args, **kwargs): super().__init__( model=AutoModelForTokenClassification.from_pretrained(model), tokenizer=AutoTokenizer.from_pretrained(model), *args, **kwargs ) def postprocess(self, all_outputs): results = super().postprocess( all_outputs=all_outputs, aggregation_strategy=AggregationStrategy.FIRST, ) return np.unique([result.get("word").strip() for result in results]) ``` ```python # Load pipeline model_name = "ml6team/keyphrase-extraction-distilbert-inspec" extractor = KeyphraseExtractionPipeline(model=model_name) ``` ```python # Inference text = """ Keyphrase extraction is a technique in text analysis where you extract the important keyphrases from a document. Thanks to these keyphrases humans can understand the content of a text very quickly and easily without reading it completely. Keyphrase extraction was first done primarily by human annotators, who read the text in detail and then wrote down the most important keyphrases. The disadvantage is that if you work with a lot of documents, this process can take a lot of time. Here is where Artificial Intelligence comes in. Currently, classical machine learning methods, that use statistical and linguistic features, are widely used for the extraction process. Now with deep learning, it is possible to capture the semantic meaning of a text even better than these classical methods. Classical methods look at the frequency, occurrence and order of words in the text, whereas these neural approaches can capture long-term semantic dependencies and context of words in a text. """.replace("\n", " ") keyphrases = extractor(text) print(keyphrases) ``` ``` # Output ['artificial intelligence' 'classical machine learning' 'deep learning' 'keyphrase extraction' 'linguistic features' 'statistical' 'text analysis'] ``` ## 📚 Training Dataset [Inspec](https://huggingface.co/datasets/midas/inspec) is a keyphrase extraction/generation dataset consisting of 2000 English scientific papers from the scientific domains of Computers and Control and Information Technology published between 1998 to 2002. The keyphrases are annotated by professional indexers or editors. You can find more information in the [paper](https://dl.acm.org/doi/10.3115/1119355.1119383). ## 👷‍♂️ Training Procedure ### Training Parameters | Parameter | Value | | --------- | ------| | Learning Rate | 1e-4 | | Epochs | 50 | | Early Stopping Patience | 3 | ### Preprocessing The documents in the dataset are already preprocessed into list of words with the corresponding labels. The only thing that must be done is tokenization and the realignment of the labels so that they correspond with the right subword tokens. ```python from datasets import load_dataset from transformers import AutoTokenizer # Labels label_list = ["B", "I", "O"] lbl2idx = {"B": 0, "I": 1, "O": 2} idx2label = {0: "B", 1: "I", 2: "O"} # Tokenizer tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased") max_length = 512 # Dataset parameters dataset_full_name = "midas/inspec" dataset_subset = "raw" dataset_document_column = "document" dataset_biotags_column = "doc_bio_tags" def preprocess_fuction(all_samples_per_split): tokenized_samples = tokenizer.batch_encode_plus( all_samples_per_split[dataset_document_column], padding="max_length", truncation=True, is_split_into_words=True, max_length=max_length, ) total_adjusted_labels = [] for k in range(0, len(tokenized_samples["input_ids"])): prev_wid = -1 word_ids_list = tokenized_samples.word_ids(batch_index=k) existing_label_ids = all_samples_per_split[dataset_biotags_column][k] i = -1 adjusted_label_ids = [] for wid in word_ids_list: if wid is None: adjusted_label_ids.append(lbl2idx["O"]) elif wid != prev_wid: i = i + 1 adjusted_label_ids.append(lbl2idx[existing_label_ids[i]]) prev_wid = wid else: adjusted_label_ids.append( lbl2idx[ f"{'I' if existing_label_ids[i] == 'B' else existing_label_ids[i]}" ] ) total_adjusted_labels.append(adjusted_label_ids) tokenized_samples["labels"] = total_adjusted_labels return tokenized_samples # Load dataset dataset = load_dataset(dataset_full_name, dataset_subset) # Preprocess dataset tokenized_dataset = dataset.map(preprocess_fuction, batched=True) ``` ### Postprocessing (Without Pipeline Function) If you do not use the pipeline function, you must filter out the B and I labeled tokens. Each B and I will then be merged into a keyphrase. Finally, you need to strip the keyphrases to make sure all unnecessary spaces have been removed. ```python # Define post_process functions def concat_tokens_by_tag(keyphrases): keyphrase_tokens = [] for id, label in keyphrases: if label == "B": keyphrase_tokens.append([id]) elif label == "I": if len(keyphrase_tokens) > 0: keyphrase_tokens[len(keyphrase_tokens) - 1].append(id) return keyphrase_tokens def extract_keyphrases(example, predictions, tokenizer, index=0): keyphrases_list = [ (id, idx2label[label]) for id, label in zip( np.array(example["input_ids"]).squeeze().tolist(), predictions[index] ) if idx2label[label] in ["B", "I"] ] processed_keyphrases = concat_tokens_by_tag(keyphrases_list) extracted_kps = tokenizer.batch_decode( processed_keyphrases, skip_special_tokens=True, clean_up_tokenization_spaces=True, ) return np.unique([kp.strip() for kp in extracted_kps]) ``` ## 📝 Evaluation Results Traditional evaluation methods are the precision, recall and F1-score @k,m where k is the number that stands for the first k predicted keyphrases and m for the average amount of predicted keyphrases. The model achieves the following results on the Inspec test set: | Dataset | P@5 | R@5 | F1@5 | P@10 | R@10 | F1@10 | P@M | R@M | F1@M | |:-----------------:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|:----:| | Inspec Test Set | 0.45 | 0.40 | 0.39 | 0.33 | 0.53 | 0.38 | 0.47 | 0.57 | 0.49 | ## 🚨 Issues Please feel free to start discussions in the Community Tab.

hfl/chinese-roberta-wwm-ext

hfl

transformers

fill-mask

--- language: - zh tags: - bert license: "apache-2.0" --- # Please use 'Bert' related functions to load this model! ## Chinese BERT with Whole Word Masking For further accelerating Chinese natural language processing, we provide **Chinese pre-trained BERT with Whole Word Masking**. **[Pre-Training with Whole Word Masking for Chinese BERT](https://arxiv.org/abs/1906.08101)** Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Ziqing Yang, Shijin Wang, Guoping Hu This repository is developed based on：https://github.com/google-research/bert You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - Chinese MacBERT: https://github.com/ymcui/MacBERT - Chinese ELECTRA: https://github.com/ymcui/Chinese-ELECTRA - Chinese XLNet: https://github.com/ymcui/Chinese-XLNet - Knowledge Distillation Toolkit - TextBrewer: https://github.com/airaria/TextBrewer More resources by HFL: https://github.com/ymcui/HFL-Anthology ## Citation If you find the technical report or resource is useful, please cite the following technical report in your paper. - Primary: https://arxiv.org/abs/2004.13922 ``` @inproceedings{cui-etal-2020-revisiting, title = "Revisiting Pre-Trained Models for {C}hinese Natural Language Processing", author = "Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Wang, Shijin and Hu, Guoping", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.findings-emnlp.58", pages = "657--668", } ``` - Secondary: https://arxiv.org/abs/1906.08101 ``` @article{chinese-bert-wwm, title={Pre-Training with Whole Word Masking for Chinese BERT}, author={Cui, Yiming and Che, Wanxiang and Liu, Ting and Qin, Bing and Yang, Ziqing and Wang, Shijin and Hu, Guoping}, journal={arXiv preprint arXiv:1906.08101}, year={2019} } ```

AdrienB134/ColBERTv2.0-spanish-mmarcoES

AdrienB134

transformers

--- license: mit datasets: - unicamp-dl/mmarco language: - es tags: - colbert - ColBERT --- ## Training #### Details The model is initialized from the [ColBERTv1.0-bert-based-spanish-mmarcoES](https://huggingface.co/AdrienB134/ColBERTv1.0-bert-based-spanish-mmarcoES) checkpoint and trained using the ColBERTv2 style of training. It was trained on 2 Tesla T4 GPU with 16GBs of memory each with 20k warmup steps warmup using a batch size of 64 and the AdamW optimizer with a constant learning rate of 1e-05. Total training time was around 60 hours. #### Data The model is fine-tuned on the Spanish version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset, a multi-lingual machine-translated version of the MS MARCO dataset. ## Evaluation The model is evaluated on the smaller development set of mMARCO-es, which consists of 6,980 queries for a corpus of 8.8M candidate passages. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). | model | Vocab. | #Param. | Size | MRR@10 | R@50 | R@1000 | |:------------------------------------------------------------------------------------------------------------------------|:--------|--------:|------:|---------:|-------:|--------:| | **ColBERTv2.0-spanish-mmarcoES** | spanish | 110M | 440MB | **32.86** | **76.46** | **81.06** | | **ColBERTv1.0-bert-based-spanish-mmarcoES** | spanish | 110M | 440MB | 24.70 | 59,23 | 63.86 |

jinaai/jina-clip-v1

jinaai

transformers

feature-extraction

--- tags: - feature-extraction - sentence-similarity - mteb - clip - vision - transformers.js language: en inference: false license: apache-2.0 library_name: transformers --- <br><br> <p align="center"> <img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/603763514de52ff951d89793/AFoybzd5lpBQXEBrQHuTt.png?w=200&h=200&f=face" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px"> </p> <p align="center"> <b>The embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>.</b> </p> <p align="center"> <b>Jina CLIP: your CLIP model is also your text retriever!</b> </p> ## Intended Usage & Model Info `jina-clip-v1` is a state-of-the-art English **multimodal (text-image) embedding model**. Traditional text embedding models, such as [jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en), excel in text-to-text retrieval but incapable of cross-modal tasks. Models like [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-base-patch32) effectively align image and text embeddings but are not optimized for text-to-text retrieval due to their training methodologies and context limitations. `jina-clip-v1` bridges this gap by offering robust performance in both domains. Its text component matches the retrieval efficiency of `jina-embeddings-v2-base-en`, while its overall architecture sets a new benchmark for cross-modal retrieval. This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, enabling seamless text-to-text and text-to-image searches within a single model. ## Data & Parameters [Check out our paper](https://arxiv.org/abs/2405.20204) ## Usage 1. The easiest way to starting using jina-clip-v1-en is to use Jina AI's [Embeddings API](https://jina.ai/embeddings/). 2. Alternatively, you can use Jina CLIP directly via transformers package. ```python !pip install transformers einops timm pillow from transformers import AutoModel # Initialize the model model = AutoModel.from_pretrained('jinaai/jina-clip-v1', trust_remote_code=True) # New meaningful sentences sentences = ['A blue cat', 'A red cat'] # Public image URLs image_urls = [ 'https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg', 'https://i.pinimg.com/736x/c9/f2/3e/c9f23e212529f13f19bad5602d84b78b.jpg' ] # Encode text and images text_embeddings = model.encode_text(sentences) image_embeddings = model.encode_image(image_urls) # also accepts PIL.image, local filenames, dataURI # Compute similarities print(text_embeddings[0] @ text_embeddings[1].T) # text embedding similarity print(text_embeddings[0] @ image_embeddings[0].T) # text-image cross-modal similarity print(text_embeddings[0] @ image_embeddings[1].T) # text-image cross-modal similarity print(text_embeddings[1] @ image_embeddings[0].T) # text-image cross-modal similarity print(text_embeddings[1] @ image_embeddings[1].T)# text-image cross-modal similarity ``` 3. JavaScript developers can use Jina CLIP via the [Transformers.js](https://huggingface.co/docs/transformers.js) library. Note that to use this model, you need to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source using `npm install xenova/transformers.js#v3`. ```js import { AutoTokenizer, CLIPTextModelWithProjection, AutoProcessor, CLIPVisionModelWithProjection, RawImage, cos_sim } from '@xenova/transformers'; // Load tokenizer and text model const tokenizer = await AutoTokenizer.from_pretrained('jinaai/jina-clip-v1'); const text_model = await CLIPTextModelWithProjection.from_pretrained('jinaai/jina-clip-v1'); // Load processor and vision model const processor = await AutoProcessor.from_pretrained('Xenova/clip-vit-base-patch32'); const vision_model = await CLIPVisionModelWithProjection.from_pretrained('jinaai/jina-clip-v1'); // Run tokenization const texts = ['A blue cat', 'A red cat']; const text_inputs = tokenizer(texts, { padding: true, truncation: true }); // Compute text embeddings const { text_embeds } = await text_model(text_inputs); // Read images and run processor const urls = [ 'https://i.pinimg.com/600x315/21/48/7e/21487e8e0970dd366dafaed6ab25d8d8.jpg', 'https://i.pinimg.com/736x/c9/f2/3e/c9f23e212529f13f19bad5602d84b78b.jpg' ]; const image = await Promise.all(urls.map(url => RawImage.read(url))); const image_inputs = await processor(image); // Compute vision embeddings const { image_embeds } = await vision_model(image_inputs); // Compute similarities console.log(cos_sim(text_embeds[0].data, text_embeds[1].data)) // text embedding similarity console.log(cos_sim(text_embeds[0].data, image_embeds[0].data)) // text-image cross-modal similarity console.log(cos_sim(text_embeds[0].data, image_embeds[1].data)) // text-image cross-modal similarity console.log(cos_sim(text_embeds[1].data, image_embeds[0].data)) // text-image cross-modal similarity console.log(cos_sim(text_embeds[1].data, image_embeds[1].data)) // text-image cross-modal similarity ``` ## Performance ### Text-Image Retrieval | Name | Flickr Image Retr. R@1 | Flickr Image Retr. R@5 | Flickr Text Retr. R@1 | Flickr Text Retr. R@5 | |------------------|-------------------------|-------------------------|-----------------------|-----------------------| | ViT-B-32 | 0.597 | 0.8398 | 0.781 | 0.938 | | ViT-B-16 | 0.6216 | 0.8572 | 0.822 | 0.966 | | jina-clip | 0.6748 | 0.8902 | 0.811 | 0.965 | | Name | MSCOCO Image Retr. R@1 | MSCOCO Image Retr. R@5 | MSCOCO Text Retr. R@1 | MSCOCO Text Retr. R@5 | |------------------|-------------------------|-------------------------|-----------------------|-----------------------| | ViT-B-32 | 0.342 | 0.6001 | 0.5234 | 0.7634 | | ViT-B-16 | 0.3309 | 0.5842 | 0.5242 | 0.767 | | jina-clip | 0.4111 | 0.6644 | 0.5544 | 0.7904 | ### Text-Text Retrieval | Name | STS12 | STS15 | STS17 | STS13 | STS14 | STS16 | STS22 | STSBenchmark | SummEval | |-----------------------|--------|--------|--------|--------|--------|--------|--------|--------------|----------| | jina-embeddings-v2 | 0.7427 | 0.8755 | 0.8888 | 0.833 | 0.7917 | 0.836 | 0.6346 | 0.8404 | 0.3056 | | jina-clip | 0.7352 | 0.8746 | 0.8976 | 0.8323 | 0.7868 | 0.8377 | 0.6583 | 0.8493 | 0.3048 | | Name | ArguAna | FiQA2018 | NFCorpus | Quora | SCIDOCS | SciFact | TRECCOVID | |--------------------|---------|----------|----------|-------|---------|---------|-----------| | jina-embeddings-v2 | 0.4418 | 0.4158 | 0.3245 | 0.882 | 0.1986 | 0.6668 | 0.6591 | | jina-clip | 0.4933 | 0.3827 | 0.3352 | 0.8789| 0.2024 | 0.6734 | 0.7161 | ## Contact Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas. ## Citation If you find `jina-clip-v1` useful in your research, please cite the following paper: ```bibtex @misc{2405.20204, Author = {Andreas Koukounas and Georgios Mastrapas and Michael Günther and Bo Wang and Scott Martens and Isabelle Mohr and Saba Sturua and Mohammad Kalim Akram and Joan Fontanals Martínez and Saahil Ognawala and Susana Guzman and Maximilian Werk and Nan Wang and Han Xiao}, Title = {Jina CLIP: Your CLIP Model Is Also Your Text Retriever}, Year = {2024}, Eprint = {arXiv:2405.20204}, } ``` ## FAQ ### I encounter this problem, what should I do? ``` ValueError: The model class you are passing has a `config_class` attribute that is not consistent with the config class you passed (model has <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_clip.JinaCLIPConfig'> and you passed <class 'transformers_modules.jinaai.jina-clip-implementation.7f069e2d54d609ef1ad2eb578c7bf07b5a51de41.configuration_cli.JinaCLIPConfig'>. Fix one of those so they match! ``` There was a bug in Transformers library between 4.40.x to 4.41.1. You can update transformers to >4.41.2 or <=4.40.0 ### Given one query, how can I merge its text-text and text-image cosine similarity? Our emperical study shows that text-text cosine similarity is normally larger than text-image cosine similarity! If you want to merge two scores, we recommended 2 ways: 1. weighted average of text-text sim and text-image sim: ```python combined_scores = sim(text, text) + lambda * sim(text, image) # optimal lambda depends on your dataset, but in general lambda=2 can be a good choice. ``` 2. apply z-score normalization before merging scores: ```python # pseudo code query_document_mean = np.mean(cos_sim_text_texts) query_document_std = np.std(cos_sim_text_texts) text_image_mean = np.mean(cos_sim_text_images) text_image_std = np.std(cos_sim_text_images) query_document_sim_normalized = (cos_sim_query_documents - query_document_mean) / query_document_std text_image_sim_normalized = (cos_sim_text_images - text_image_mean) / text_image_std ```

sentence-transformers/msmarco-MiniLM-L-6-v3

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # sentence-transformers/msmarco-MiniLM-L-6-v3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/msmarco-MiniLM-L-6-v3') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/msmarco-MiniLM-L-6-v3') model = AutoModel.from_pretrained('sentence-transformers/msmarco-MiniLM-L-6-v3') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/msmarco-MiniLM-L-6-v3) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

Helsinki-NLP/opus-mt-ko-en

Helsinki-NLP

transformers

translation

--- language: - ko - en tags: - translation license: apache-2.0 --- ### kor-eng * source group: Korean * target group: English * OPUS readme: [kor-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md) * model: transformer-align * source language(s): kor kor_Hang kor_Latn * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.zip) * test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.test.txt) * test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba-test.kor.eng | 41.3 | 0.588 | ### System Info: - hf_name: kor-eng - source_languages: kor - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['ko', 'en'] - src_constituents: {'kor_Hani', 'kor_Hang', 'kor_Latn', 'kor'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.test.txt - src_alpha3: kor - tgt_alpha3: eng - short_pair: ko-en - chrF2_score: 0.588 - bleu: 41.3 - brevity_penalty: 0.9590000000000001 - ref_len: 17711.0 - src_name: Korean - tgt_name: English - train_date: 2020-06-17 - src_alpha2: ko - tgt_alpha2: en - prefer_old: False - long_pair: kor-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

sentence-transformers/roberta-base-nli-stsb-mean-tokens

sentence-transformers

sentence-transformers

sentence-similarity

--- license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: [SBERT.net - Pretrained Models](https://www.sbert.net/docs/pretrained_models.html)** # sentence-transformers/roberta-base-nli-stsb-mean-tokens This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/roberta-base-nli-stsb-mean-tokens') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/roberta-base-nli-stsb-mean-tokens') model = AutoModel.from_pretrained('sentence-transformers/roberta-base-nli-stsb-mean-tokens') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/roberta-base-nli-stsb-mean-tokens) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors This model was trained by [sentence-transformers](https://www.sbert.net/). If you find this model helpful, feel free to cite our publication [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://arxiv.org/abs/1908.10084): ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "http://arxiv.org/abs/1908.10084", } ```

microsoft/unixcoder-base

microsoft

transformers

feature-extraction

--- language: - en license: apache-2.0 --- # Model Card for UniXcoder-base # Model Details ## Model Description UniXcoder is a unified cross-modal pre-trained model that leverages multimodal data (i.e. code comment and AST) to pretrain code representation. - **Developed by:** Microsoft Team - **Shared by [Optional]:** Hugging Face - **Model type:** Feature Engineering - **Language(s) (NLP):** en - **License:** Apache-2.0 - **Related Models:** - **Parent Model:** RoBERTa - **Resources for more information:** - [Associated Paper](https://arxiv.org/abs/2203.03850) # Uses ## Direct Use Feature Engineering ## Downstream Use [Optional] More information needed ## Out-of-Scope Use More information needed # Bias, Risks, and Limitations Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. # Training Details ## Training Data More information needed ## Training Procedure ### Preprocessing More information needed ### Speeds, Sizes, Times More information needed # Evaluation ## Testing Data, Factors & Metrics ### Testing Data More information needed ### Factors The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850): > UniXcoder has slightly worse BLEU-4 scores on both code summarization and generation tasks. The main reasons may come from two aspects. One is the amount of NL-PL pairs in the pre-training data ### Metrics The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850): > We evaluate UniXcoder on five tasks over nine public datasets, including two understanding tasks, two generation tasks and an autoregressive task. To further evaluate the performance of code fragment embeddings, we also propose a new task called zero-shot code-to-code search. ## Results The model creators note in the [associated paper](https://arxiv.org/abs/2203.03850): >Taking zero-shot code-code search task as an example, after removing contrastive learning, the performance drops from 20.45% to 13.73%. # Model Examination More information needed # Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** More information needed - **Hours used:** More information needed - **Cloud Provider:** More information needed - **Compute Region:** More information needed - **Carbon Emitted:** More information needed # Technical Specifications [optional] ## Model Architecture and Objective More information needed ## Compute Infrastructure More information needed ### Hardware More information needed ### Software More information needed # Citation **BibTeX:** ``` @misc{https://doi.org/10.48550/arxiv.2203.03850, doi = {10.48550/ARXIV.2203.03850}, url = {https://arxiv.org/abs/2203.03850}, author = {Guo, Daya and Lu, Shuai and Duan, Nan and Wang, Yanlin and Zhou, Ming and Yin, Jian}, keywords = {Computation and Language (cs.CL), Programming Languages (cs.PL), Software Engineering (cs.SE), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {UniXcoder: Unified Cross-Modal Pre-training for Code ``` # Glossary [optional] More information needed # More Information [optional] More information needed # Model Card Authors [optional] Microsoft Team in collaboration with Ezi Ozoani and the Hugging Face Team. # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base") model = AutoModel.from_pretrained("microsoft/unixcoder-base") ``` </details>

dandelin/vilt-b32-finetuned-vqa

dandelin

transformers

visual-question-answering

--- tags: - visual-question-answering license: apache-2.0 widget: - text: "What's the animal doing?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg" - text: "What is on top of the building?" src: "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg" --- # Vision-and-Language Transformer (ViLT), fine-tuned on VQAv2 Vision-and-Language Transformer (ViLT) model fine-tuned on [VQAv2](https://visualqa.org/). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Intended uses & limitations You can use the raw model for visual question answering. ### How to use Here is how to use this model in PyTorch: ```python from transformers import ViltProcessor, ViltForQuestionAnswering import requests from PIL import Image # prepare image + question url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) text = "How many cats are there?" processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") # prepare inputs encoding = processor(image, text, return_tensors="pt") # forward pass outputs = model(**encoding) logits = outputs.logits idx = logits.argmax(-1).item() print("Predicted answer:", model.config.id2label[idx]) ``` ## Training data (to do) ## Training procedure ### Preprocessing (to do) ### Pretraining (to do) ## Evaluation results (to do) ### BibTeX entry and citation info ```bibtex @misc{kim2021vilt, title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision}, author={Wonjae Kim and Bokyung Son and Ildoo Kim}, year={2021}, eprint={2102.03334}, archivePrefix={arXiv}, primaryClass={stat.ML} } ```

TheBloke/Mistral-7B-Instruct-v0.2-GGUF

TheBloke

transformers

text-generation

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 inference: false license: apache-2.0 model_creator: Mistral AI_ model_name: Mistral 7B Instruct v0.2 model_type: mistral pipeline_tag: text-generation prompt_template: '<s>[INST] {prompt} [/INST] ' quantized_by: TheBloke tags: - finetuned --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B Instruct v0.2 - GGUF - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- description start --> ## Description This repo contains GGUF format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF) * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-provided-files start --> ## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [mistral-7b-instruct-v0.2.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes | | [mistral-7b-instruct-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss | | [mistral-7b-instruct-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss | | [mistral-7b-instruct-v0.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss | | [mistral-7b-instruct-v0.2.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [mistral-7b-instruct-v0.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss | | [mistral-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended | | [mistral-7b-instruct-v0.2.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [mistral-7b-instruct-v0.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended | | [mistral-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended | | [mistral-7b-instruct-v0.2.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss | | [mistral-7b-instruct-v0.2.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. <!-- README_GGUF.md-provided-files end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Instruct-v0.2-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m mistral-7b-instruct-v0.2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> <!-- original-model-card start --> # Original model card: Mistral AI_'s Mistral 7B Instruct v0.2 # Model Card for Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. <!-- original-model-card end -->

vikhyatk/moondream1

vikhyatk

transformers

text-generation

--- language: - en --- # 🌔 moondream1 1.6B parameter model built by [@vikhyatk](https://x.com/vikhyatk) using SigLIP, Phi-1.5 and the LLaVa training dataset. The model is release for research purposes only, commercial use is not allowed. Try it out on [Huggingface Spaces](https://huggingface.co/spaces/vikhyatk/moondream1)! **Usage** ``` pip install transformers timm einops ``` ```python from transformers import AutoModelForCausalLM, CodeGenTokenizerFast as Tokenizer from PIL import Image model_id = "vikhyatk/moondream1" model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True) tokenizer = Tokenizer.from_pretrained(model_id) image = Image.open('<IMAGE_PATH>') enc_image = model.encode_image(image) print(model.answer_question(enc_image, "<QUESTION>", tokenizer)) ``` ## Benchmarks | Model | Parameters | VQAv2 | GQA | TextVQA | | --- | --- | --- | --- | --- | | LLaVA-1.5 | 13.3B | 80.0 | 63.3 | 61.3 | | LLaVA-1.5 | 7.3B | 78.5 | 62.0 | 58.2 | | **moondream1** | 1.6B | 74.7 | 57.9 | 35.6 | ## Examples | Image | Examples | | --- | --- | |  | **What is the title of this book?**<br>The Little Book of Deep Learning<br><br>**What can you tell me about this book?**<br>The book in the image is titled "The Little Book of Deep Learning." It appears to be a guide or manual that provides information and instructions on the subject of deep learning. The book is described as being small and thin, making it easy to carry and read. It is set in a room with a view outside, suggesting that the person holding the book might be in a living or working space with a view. The content of the book is likely to be complex, as the title implies a focus on advanced concepts in deep learning.<br><br>**Where is the person standing?**<br> The person is standing on a balcony or deck, which is outside. | |  | **What type of food is the girl holding?**<br>The girl is holding a hamburger.<br><br>**What color is the woman's hair?**<br>The woman's hair is white.<br><br>**What is the girl doing in the image?**<br>The girl in the image is eating a hamburger. | |  | **What color is the train?**<br>The train is red.<br><br>**What is the bus doing in the image?**<br> The bus in the image is driving down a city street, passing through an intersection.<br><br>**What kind of public transportation is in the image?**<br>The image features a red train or trolley as the public transportation option. | |  | **What is the girl looking at?**<br>The girl is looking at her reflection in the mirror.<br><br>**What color is the woman's hair?**<br>The woman's hair is brown.<br><br>**What is the woman doing in the mirror?**<br>The woman is adjusting her hair in the mirror. | |  | **What might be the reason for the dog's aggressive behavior?**<br>The dog's aggressive behavior, including its growling, baring its teeth, and showing its sharp teeth, could be due to various reasons. It is possible that the dog is feeling threatened, scared, or defensive about something in its environment, such as another animal, a loud noise, or a person nearby. Alternatively, the dog might be reacting to a command it received from its owner, or it could be experiencing discomfort or pain, which is causing it to display aggressive behavior. It is essential to observe the dog's body language and context to determine the underlying cause of its aggressive behavior and respond accordingly to address its needs or concerns.<br><br>**What kind of dog is in the picture?**<br>The dog in the picture is a small, black and white chihuahua. |

RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) CodeLlama-70b-Python-hf - GGUF - Model creator: https://huggingface.co/codellama/ - Original model: https://huggingface.co/codellama/CodeLlama-70b-Python-hf/ | Name | Quant method | Size | | ---- | ---- | ---- | | [CodeLlama-70b-Python-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q2_K.gguf) | Q2_K | 23.71GB | | [CodeLlama-70b-Python-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.IQ3_XS.gguf) | IQ3_XS | 26.37GB | | [CodeLlama-70b-Python-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.IQ3_S.gguf) | IQ3_S | 27.86GB | | [CodeLlama-70b-Python-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q3_K_S.gguf) | Q3_K_S | 27.86GB | | [CodeLlama-70b-Python-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.IQ3_M.gguf) | IQ3_M | 28.82GB | | [CodeLlama-70b-Python-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q3_K.gguf) | Q3_K | 30.99GB | | [CodeLlama-70b-Python-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q3_K_M.gguf) | Q3_K_M | 30.99GB | | [CodeLlama-70b-Python-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q3_K_L.gguf) | Q3_K_L | 33.67GB | | [CodeLlama-70b-Python-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.IQ4_XS.gguf) | IQ4_XS | 34.64GB | | [CodeLlama-70b-Python-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q4_0.gguf) | Q4_0 | 36.2GB | | [CodeLlama-70b-Python-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.IQ4_NL.gguf) | IQ4_NL | 36.55GB | | [CodeLlama-70b-Python-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/blob/main/CodeLlama-70b-Python-hf.Q4_K_S.gguf) | Q4_K_S | 36.55GB | | [CodeLlama-70b-Python-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q4_K | 38.58GB | | [CodeLlama-70b-Python-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q4_K_M | 38.58GB | | [CodeLlama-70b-Python-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q4_1 | 40.2GB | | [CodeLlama-70b-Python-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q5_0 | 44.2GB | | [CodeLlama-70b-Python-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q5_K_S | 44.2GB | | [CodeLlama-70b-Python-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q5_K | 45.41GB | | [CodeLlama-70b-Python-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q5_K_M | 45.41GB | | [CodeLlama-70b-Python-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q5_1 | 48.2GB | | [CodeLlama-70b-Python-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q6_K | 52.7GB | | [CodeLlama-70b-Python-hf.Q8_0.gguf](https://huggingface.co/RichardErkhov/codellama_-_CodeLlama-70b-Python-hf-gguf/tree/main/) | Q8_0 | 68.26GB | Original model description: --- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # **Code Llama** Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B Python specialist version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. > [!NOTE] > This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-70b-Python-hf). | | Base Model | Python | Instruct | | --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- | | 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) | | 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | | 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | | 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) | ## Model Use To use this model, please make sure to install `transformers`. ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [ ] Infilling. - [ ] Instructions / chat. - [x] Python specialist. ## Model Details *Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). **Model Developers** Meta **Variations** Code Llama comes in four model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B, 34B, and 70B parameters. **This repository contains the Python version of the 70B parameters model.** **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. **Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens. This variant **does not** support long context of up to 100k tokens. **Model Dates** Code Llama and its variants have been trained between January 2023 and January 2024. **Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use **Intended Use Cases** Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. **Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software **Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. **Carbon Footprint** In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).

mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF

mradermacher

transformers

--- base_model: OpenPipe/Hermes-2-Theta-Llama-3-70B-32k language: - en library_name: transformers license: llama3 quantized_by: mradermacher tags: - distillation - synthetic data - function calling - structured outputs - json mode --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/OpenPipe/Hermes-2-Theta-Llama-3-70B-32k <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Hermes-2-Theta-Llama-3-70B-32k-i1-GGUF/resolve/main/Hermes-2-Theta-Llama-3-70B-32k.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->

facebook/dino-vits16

facebook

transformers

image-feature-extraction

--- license: apache-2.0 tags: - dino - vision datasets: - imagenet-1k --- # Vision Transformer (small-sized model, patch size 16) trained using DINO Vision Transformer (ViT) model trained using the DINO method. It was introduced in the paper [Emerging Properties in Self-Supervised Vision Transformers](https://arxiv.org/abs/2104.14294) by Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, Armand Joulin and first released in [this repository](https://github.com/facebookresearch/dino). Disclaimer: The team releasing DINO did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not include any fine-tuned heads. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import ViTImageProcessor, ViTModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) processor = ViTImageProcessor.from_pretrained('facebook/dino-vits16') model = ViTModel.from_pretrained('facebook/dino-vits16') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2104-14294, author = {Mathilde Caron and Hugo Touvron and Ishan Misra and Herv{\'{e}} J{\'{e}}gou and Julien Mairal and Piotr Bojanowski and Armand Joulin}, title = {Emerging Properties in Self-Supervised Vision Transformers}, journal = {CoRR}, volume = {abs/2104.14294}, year = {2021}, url = {https://arxiv.org/abs/2104.14294}, archivePrefix = {arXiv}, eprint = {2104.14294}, timestamp = {Tue, 04 May 2021 15:12:43 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2104-14294.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

Mitsua/mitsua-diffusion-cc0

Mitsua

diffusers

text-to-image

--- license: openrail++ tags: - stable-diffusion - text-to-image - stable-diffusion-diffusers - diffusers inference: true --- # . # . # . # . # . # . # ❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗ # This version is deprecated. # Please use [Mitsua Diffusion One](https://huggingface.co/Mitsua/mitsua-diffusion-one), which is a successor of this model. # ❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗❗ # . # . # . # . # . # Mitsua Diffusion CC0 Model Card Mitsua Diffusion CC0 is a latent text-to-image diffusion model, whose U-Net is **trained from scratch using only public domain/CC0 or copyright images with permission for use**. Text Encoder and VAE are borrowed from [Stable Diffusion v2.1 base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base/). This will be used as a base model for [**AI VTuber Elan Mitsua🖌️**](https://elanmitsua.com/en/)’s activity. ❗❗ **Currently the model has super low visual quality and limited diversity** ❗❗ Yes, the visual quality is not so good. Most of modern artistic concept is lost completely. However, since she is a growing AI in an ethical fashion, it would be good starting point for Mitsua-chan! You can join [her training on Twitter](https://twitter.com/elanmitsua)! Please support Mitsua-chan!🎉 Further training will be done in a fully opt-in basis. If you are interested in, [please click here to submit an opt-in application](https://forms.gle/Nk3M7UyqSgYAqdpA6). We are active on [a Discord server for opt-in participants only](https://discord.com/invite/7VTGRweTUg). Communication is currently in Japanese.  You can check [here to all prompts to generate these images](https://huggingface.co/Mitsua/mitsua-diffusion-cc0/resolve/main/images/mitsua_cc0_works_prompts.csv). ## Training Data Sources All data was obtained ethically and in compliance with the site's terms and conditions. No copyright images are used in the training of this model without the permission. No AI generated images are in the dataset. - Traditional Artwork in public domain / CC0 - MET Museum Open Access - Smithsonian Open Access - Cleveland Museum of Art Open Access - National Gallery of Art Open Access - ArtBench-10 (public domain subset) - CC0 Photos - Flickr, Wikimedia Commons - CC0 NFTs *1 - goblintown.nft, mfer, tubby-cats, Timeless - CC0 VRM models - made by VRoid Project, pastelkies, yomox9 (all CC0 subset) - We generated a bunch of synthesized images dataset rendered with various poses and camera angles. - Copyright images with permission for use - Generative and Visual Artworks made by Rhizomatiks Approx 11M images in total with data augmentation. 1. Their work is released under a CC0 license, but if you are considering using this model to create a work inspired by their NFT and sell it as NFT, please consider paying them a royalty to help the CC0 NFT community grow. ## License [Creative Open-Rail++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ❗❗ “Mitsua Diffusion CC0” means most of the training data is CC0. **the model license itself is NOT CC0**.❗❗ This model is open access and available to all, with a CreativeML OpenRAIL++-M license further specifying rights and usage. The CreativeML OpenRAIL++-M License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL++-M to all your users (please read the license entirely and carefully) [Please read the full license here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) ## Developed by - Stable Diffusion 2.1: Robin Rombach, Patrick Esser - Mitsua Diffusion CC0 : Abstract Engine dev team

cvssp/audioldm-l-full

cvssp

diffusers

--- license: cc-by-nc-sa-4.0 --- # AudioLDM AudioLDM is a latent text-to-audio diffusion model capable of generating realistic audio samples given any text input. It is available in the 🧨 Diffusers library from v0.15.0 onwards. # Model Details AudioLDM was proposed in the paper [AudioLDM: Text-to-Audio Generation with Latent Diffusion Models](https://arxiv.org/abs/2301.12503) by Haohe Liu et al. Inspired by [Stable Diffusion](https://huggingface.co/CompVis/stable-diffusion-v1-4), AudioLDM is a text-to-audio _latent diffusion model (LDM)_ that learns continuous audio representations from [CLAP](https://huggingface.co/laion/clap-htsat-unfused) latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional sound effects, human speech and music. # Checkpoint Details This is the **large** version of the AudioLDM model, with twice the number of UNet channels and head channels as the small checkpoints. The four AudioLDM checkpoints are summarised in the table below: **Table 1:** Summary of the AudioLDM checkpoints. | Checkpoint | Training Steps | Audio conditioning | CLAP audio dim | UNet dim | Params | |-----------------------------------------------------------------------|----------------|--------------------|----------------|----------|--------| | [audioldm-s-full](https://huggingface.co/cvssp/audioldm) | 1.5M | No | 768 | 128 | 421M | | [audioldm-s-full-v2](https://huggingface.co/cvssp/audioldm-s-full-v2) | > 1.5M | No | 768 | 128 | 421M | | [audioldm-m-full](https://huggingface.co/cvssp/audioldm-m-full) | 1.5M | Yes | 1024 | 192 | 652M | | [audioldm-l-full](https://huggingface.co/cvssp/audioldm-l-full) | 1.5M | No | 768 | 256 | 975M | ## Model Sources - [**Original Repository**](https://github.com/haoheliu/AudioLDM) - [**🧨 Diffusers Pipeline**](https://huggingface.co/docs/diffusers/api/pipelines/audioldm) - [**Paper**](https://arxiv.org/abs/2301.12503) - [**Demo**](https://huggingface.co/spaces/haoheliu/audioldm-text-to-audio-generation) # Usage First, install the required packages: ``` pip install --upgrade diffusers transformers accelerate ``` ## Text-to-Audio For text-to-audio generation, the [AudioLDMPipeline](https://huggingface.co/docs/diffusers/api/pipelines/audioldm) can be used to load pre-trained weights and generate text-conditional audio outputs: ```python from diffusers import AudioLDMPipeline import torch repo_id = "cvssp/audioldm-l-full" pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16) pipe = pipe.to("cuda") prompt = "Techno music with a strong, upbeat tempo and high melodic riffs" audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0] ``` The resulting audio output can be saved as a .wav file: ```python import scipy scipy.io.wavfile.write("techno.wav", rate=16000, data=audio) ``` Or displayed in a Jupyter Notebook / Google Colab: ```python from IPython.display import Audio Audio(audio, rate=16000) ``` <audio controls> <source src="https://huggingface.co/datasets/sanchit-gandhi/audioldm-readme-samples/resolve/main/audioldm-l-full-techno.wav" type="audio/wav"> Your browser does not support the audio element. </audio> ## Tips Prompts: * Descriptive prompt inputs work best: you can use adjectives to describe the sound (e.g. "high quality" or "clear") and make the prompt context specific (e.g., "water stream in a forest" instead of "stream"). * It's best to use general terms like 'cat' or 'dog' instead of specific names or abstract objects that the model may not be familiar with. Inference: * The _quality_ of the predicted audio sample can be controlled by the `num_inference_steps` argument: higher steps give higher quality audio at the expense of slower inference. * The _length_ of the predicted audio sample can be controlled by varying the `audio_length_in_s` argument. # Citation **BibTeX:** ``` @article{liu2023audioldm, title={AudioLDM: Text-to-Audio Generation with Latent Diffusion Models}, author={Liu, Haohe and Chen, Zehua and Yuan, Yi and Mei, Xinhao and Liu, Xubo and Mandic, Danilo and Wang, Wenwu and Plumbley, Mark D}, journal={arXiv preprint arXiv:2301.12503}, year={2023} } ```

bartowski/firefunction-v2-GGUF

bartowski

text-generation

--- license: llama3 tags: - function-calling quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of firefunction-v2 Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3197">b3197</a> for quantization. Original model: https://huggingface.co/fireworks-ai/firefunction-v2 All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) ## Prompt format ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful assistant with access to functions. In addition to plain text responses, you can chose to call one or more of the provided functions. Use the following rule to decide when to call a function: * if the response can be generated from your internal knowledge (e.g., as in the case of queries like "What is the capital of Poland?"), do so * if you need external information that can be obtained by calling one or more of the provided functions, generate a function calls If you decide to call functions: * prefix function calls with functools marker (no closing marker required) * all function calls should be generated in a single JSON list formatted as functools[{"name": [function name], "arguments": [function arguments as JSON]},...] * follow the provided JSON schema. Do not hallucinate arguments or values. Do to blindly copy values from the provided samples * respect the argument type formatting. E.g., if the type if number and format is float, write value 7 as 7.0 * make sure you pick the right functions that match the user intent Available functions as JSON spec: [ {functions} ] Today is {datetime}.<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [firefunction-v2-Q8_0.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF//main/firefunction-v2-Q8_0.gguf) | Q8_0 | 0GB | Extremely high quality, generally unneeded but max available quant. | | [firefunction-v2-Q6_K.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF//main/firefunction-v2-Q6_K.gguf) | Q6_K | 0GB | Very high quality, near perfect, *recommended*. | | [firefunction-v2-Q5_K_L.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF//main/firefunction-v2-Q5_K_L.gguf) | Q5_K_L | 0GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. | | [firefunction-v2-Q5_K_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q5_K_M.gguf) | Q5_K_M | 49.94GB | High quality, *recommended*. | | [firefunction-v2-Q4_K_L.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q4_K_L.gguf) | Q4_K_L | 45.27GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. | | [firefunction-v2-Q4_K_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q4_K_M.gguf) | Q4_K_M | 42.52GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [firefunction-v2-IQ4_XS.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ4_XS.gguf) | IQ4_XS | 37.90GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [firefunction-v2-Q3_K_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q3_K_M.gguf) | Q3_K_M | 34.26GB | Even lower quality. | | [firefunction-v2-IQ3_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ3_M.gguf) | IQ3_M | 31.93GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [firefunction-v2-Q3_K_S.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q3_K_S.gguf) | Q3_K_S | 30.91GB | Low quality, not recommended. | | [firefunction-v2-IQ3_XXS.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ3_XXS.gguf) | IQ3_XXS | 27.46GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [firefunction-v2-Q2_K.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-Q2_K.gguf) | Q2_K | 26.37GB | Very low quality but surprisingly usable. | | [firefunction-v2-IQ2_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ2_M.gguf) | IQ2_M | 24.11GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [firefunction-v2-IQ2_XS.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ2_XS.gguf) | IQ2_XS | 21.14GB | Lower quality, uses SOTA techniques to be usable. | | [firefunction-v2-IQ2_XXS.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ2_XXS.gguf) | IQ2_XXS | 19.09GB | Lower quality, uses SOTA techniques to be usable. | | [firefunction-v2-IQ1_M.gguf](https://huggingface.co/bartowski/firefunction-v2-GGUF/blob/main/firefunction-v2-IQ1_M.gguf) | IQ1_M | 16.75GB | Extremely low quality, *not* recommended. | ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/firefunction-v2-GGUF --include "firefunction-v2-Q4_K_M.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/firefunction-v2-GGUF --include "firefunction-v2-Q8_0.gguf/*" --local-dir firefunction-v2-Q8_0 ``` You can either specify a new local-dir (firefunction-v2-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

mradermacher/CabraLlama3-70b-v2-i1-GGUF

mradermacher

transformers

--- base_model: nicolasdec/CabraLlama3-70b-v2 datasets: - botbot-ai/Cabra3k language: - pt library_name: transformers license: llama3 quantized_by: mradermacher tags: - portuguese - llama - cabra - llama-3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/nicolasdec/CabraLlama3-70b-v2 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/CabraLlama3-70b-v2-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/CabraLlama3-70b-v2-i1-GGUF/resolve/main/CabraLlama3-70b-v2.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract

microsoft

transformers

fill-mask

--- language: en tags: - exbert license: mit widget: - text: "[MASK] is a tyrosine kinase inhibitor." --- ## MSR BiomedBERT (abstracts only) <div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;"> * This model was previously named **"PubMedBERT (abstracts)"**. * You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract" or update your `transformers` library to version 4.22+ if you need to refer to the old name. </div> Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. [Recent work](https://arxiv.org/abs/2007.15779) shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. This BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/). This model achieves state-of-the-art performance on several biomedical NLP tasks, as shown on the [Biomedical Language Understanding and Reasoning Benchmark](https://aka.ms/BLURB). ## Citation If you find BiomedBERT useful in your research, please cite the following paper: ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, } ``` <a href="https://huggingface.co/exbert/?model=microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=10&heads=..0,1,2,3,4,5,6,7,8,9,10,11&threshold=0.7&tokenInd=17&tokenSide=right&maskInds=..&hideClsSep=true"> <img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png"> </a>

Systran/faster-whisper-tiny

Systran

ctranslate2

automatic-speech-recognition

--- language: - en - zh - de - es - ru - ko - fr - ja - pt - tr - pl - ca - nl - ar - sv - it - id - hi - fi - vi - he - uk - el - ms - cs - ro - da - hu - ta - 'no' - th - ur - hr - bg - lt - la - mi - ml - cy - sk - te - fa - lv - bn - sr - az - sl - kn - et - mk - br - eu - is - hy - ne - mn - bs - kk - sq - sw - gl - mr - pa - si - km - sn - yo - so - af - oc - ka - be - tg - sd - gu - am - yi - lo - uz - fo - ht - ps - tk - nn - mt - sa - lb - my - bo - tl - mg - as - tt - haw - ln - ha - ba - jw - su tags: - audio - automatic-speech-recognition license: mit library_name: ctranslate2 --- # Whisper tiny model for CTranslate2 This repository contains the conversion of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) to the [CTranslate2](https://github.com/OpenNMT/CTranslate2) model format. This model can be used in CTranslate2 or projects based on CTranslate2 such as [faster-whisper](https://github.com/systran/faster-whisper). ## Example ```python from faster_whisper import WhisperModel model = WhisperModel("tiny") segments, info = model.transcribe("audio.mp3") for segment in segments: print("[%.2fs -> %.2fs] %s" % (segment.start, segment.end, segment.text)) ``` ## Conversion details The original model was converted with the following command: ``` ct2-transformers-converter --model openai/whisper-tiny --output_dir faster-whisper-tiny \ --copy_files tokenizer.json --quantization float16 ``` Note that the model weights are saved in FP16. This type can be changed when the model is loaded using the [`compute_type` option in CTranslate2](https://opennmt.net/CTranslate2/quantization.html). ## More information **For more information about the original model, see its [model card](https://huggingface.co/openai/whisper-tiny).**

google/metricx-23-qe-large-v2p0

google

transformers

--- license: apache-2.0 --- # MetricX-23 *This is not an officially supported Google product.* **GitHub repository: [https://github.com/google-research/metricx](https://github.com/google-research/metricx)** This repository contains the MetricX-23 models, a family of models for automatic evaluation of translations that were proposed in the WMT'23 Metrics Shared Task submission [MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task](https://aclanthology.org/2023.wmt-1.63/). The models were trained in [T5X](https://github.com/google-research/t5x) and then converted for use in PyTorch. ## Available Models There are 6 models available on HuggingFace that vary in the number of parameters and whether or not the model is reference-based or reference-free (also known as quality estimation, or QE): * [MetricX-23-XXL](https://huggingface.co/google/metricx-23-large-v2p0) * [MetricX-23-XL](https://huggingface.co/google/metricx-23-xl-v2p0) * [MetricX-23-Large](https://huggingface.co/google/metricx-23-xxl-v2p0) * [MetricX-23-QE-XXL](https://huggingface.co/google/metricx-23-qe-large-v2p0) * [MetricX-23-QE-XL](https://huggingface.co/google/metricx-23-qe-xl-v2p0) * [MetricX-23-QE-Large](https://huggingface.co/google/metricx-23-qe-xxl-v2p0) We recommend using the XXL model versions for the best agreement with human judgments of translation quality, the Large versions for best speed, and the XL for an intermediate use case. ## Changes to the WMT'23 Submission These models available here are most similar to the primary submission to the WMT'23 Metrics Shared Task. They are initialized with [mT5](https://aclanthology.org/2021.naacl-main.41/) then fine-tuned on a combination of direct assessment and MQM data. However, we made some changes that make these models different from the WMT'23 submissions. First, the models are trained to regress the actual MQM score rather than a normalized score between 0 and 1. **That means the output from the MetricX-23 models is a score in the range [0, 25] where lower is better (i.e., it predicts an error score).** Second, these models were trained with a larger variety of synthetic data that makes them more robust to translation edge cases like over- and undertranslation, described in more detail in the following section. ### Synthetic Data In order for our MetricX models to learn to identify certain types of bad translations that are not sufficiently (or at all) represented in the regular training data, we created synthetic examples and mixed them in during training. The synthetic training data was generated from the DA datasets ranging from WMT15 to WMT21 (~ 43 language pairs). In most cases, the synthetic examples have the candidate translation manipulated so as to turn it into a bad translation with a specific issue commonly unrecognized by learned metrics. The table below provides an overview of the various failure modes that we considered, including brief descriptions of how we prepared the synthetic data to address them. | Failure mode | Synthetic example description | | ----------- | ----------- | | Undertranslation | Candidate translation with an arbitrary sentence removed (if multi-sentence); alternatively, candidate with a certain proportion of words removed from the end. | | Overtranslation | Candidate translation duplicated (with space in between). | | Fluent but unrelated translation | Arbitrary reference of a similar length from the dataset. | | Gibberish | Text of a similar length as the reference, generated by sampling words from the reference translation vocabulary (built from all references in the data). | | Missing punctuation | Reference translation with the end punctuation removed (11 punctuation symbols considered). | | Latin instead of Chinese/Japanese or Hindi/Bengali punctuation | Candidate translation with the language-specific punctuation symbol at the end replaced with the Latin equivalent (e.g., "." instead of "。" or "।"); alternatively, the punctuation symbol is replaced with the Latin equivalent in the reference, keeping the correct one in the candidate. | | Reference-matching translation | Reference translation copied as the candidate translation (unlike the rest of the synthetic data, these examples are meant to train the metric to predict a perfect score for candidates matching the reference). | Examples from the first 4 categories were assigned a label corresponding to the worst score on the given rating scale (e.g., 25 when mixed with MQM training data), whereas the reference-matching translation examples are assigned the best score (e.g., 0 when used with MQM data). The missing/incorrect punctuation examples were labeled with a score slightly worse than perfect. Note that some of the synthetic datasets are only meaningful in the reference-based scenario, and we thus excluded them when training a QE variant of MetricX. These are the Latin-vs-special punctuation and the reference-matching translation examples. Most of the synthetic training sets were created using stratified sampling across target languages, taking 500 examples per target language. One exception is the missing punctuation set, which used a stratified sample across different punctuation symbols instead. When training MetricX, a small proportion of the synthetic examples was mixed with the regular training examples. During the first-stage fine-tuning on DA data, each synthetic training set constituted between 0.1% and 1% of all training examples, whereas in the second-stage fine-tuning on MQM data we used an even smaller proportion, around 0.05%. As for evaluating the effect of the synthetic training data on the model's performance, the DEMETR challenge set - which we originally used to evaluate the models submitted to the WMT23 Metrics Shared Task - was not adequate anymore. We therefore created a new DEMETR-style test set based on the WMT22 DA data, with examples constructed analogically to the synthetic training examples, as described above. This test set helped us determine the right proportions of synthetic data for fine-tuning in order to make MetricX robust for the failure modes in consideration, without sacrificing the system- and segment-level correlations with human ratings. ## Usage The code for using MetricX models can be found at [https://github.com/google-research/metricx](https://github.com/google-research/metricx). The repository contains example prediction scripts, described below. The `metricx23/predict.py` script contains an example for how to run inference on the models. ### Reference-Based Example usage for a reference-based model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"reference"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. Note that the model was trained with a maximum input length of 1024 tokens, so significantly increasing that value may lead to unpredictable behavior. ### Reference-Free Example usage for a reference-free model: ```bash python -m metricx23.predict \ --tokenizer google/mt5-xl \ --model_name_or_path google/metricx-23-qe-xl-v2p0 \ --max_input_length 1024 \ --batch_size 1 \ --input_file input.jsonl \ --output_file output.jsonl \ --qe ``` `input.jsonl` is expected to have 1 serialized JSON object per line with `"source"` and `"hypothesis"` fields. The output jsonl will be parallel to `input.jsonl` but additionally contain a `"prediction"` field with the predicted score. ## Meta-Evaluation The `metricx23/evaluate.py` script contains code to calculate various correlations between the MetricX-23 scores and MQM ratings of translation quality using the [MT Metrics Eval](https://github.com/google-research/mt-metrics-eval) library. Example usage: ```bash python -m metricx23.evaluate \ --dataset wmt22 \ --lp en-de \ --input_file input.jsonl \ --output_file output.json ``` `input.jsonl` is expected to have one JSON object serialized per line. Each JSON object is expected to contain 4 fields: * `"system_id"`: The name of the system that generated the translation. * `"segment_id"`: The 0-based index of the corresponding segment in the MT Metrics Eval data. * `"label"`: The ground-truth translation quality score (with higher is better). * `"prediction"`: The model predicted translation quality score (with lower is better; the script negates the scores so higher is better). The script will calculate the 4 agreement/correlations that were used in the WMT'23 Shared Task. Below are the results for the MetricX-23 models on the WMT'22 Metrics Shared Task data: English-German: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.795 | 0.835 | 0.546 | 0.619 | | MetricX-23-XL | 0.756 | 0.813 | 0.540 | 0.605 | | MetricX-23-Large | 0.769 | 0.759 | 0.507 | 0.595 | | MetricX-23-QE-XXL | 0.769 | 0.830 | 0.490 | 0.606 | | MetricX-23-QE-XL | 0.718 | 0.684 | 0.421 | 0.594 | | MetricX-23-QE-Large | 0.744 | 0.671 | 0.387 | 0.579 | English-Russian: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.905 | 0.943 | 0.477 | 0.609 | | MetricX-23-XL | 0.876 | 0.906 | 0.498 | 0.589 | | MetricX-23-Large | 0.876 | 0.841 | 0.474 | 0.569 | | MetricX-23-QE-XXL | 0.895 | 0.940 | 0.470 | 0.602 | | MetricX-23-QE-XL | 0.848 | 0.861 | 0.415 | 0.570 | | MetricX-23-QE-Large | 0.819 | 0.778 | 0.411 | 0.551 | Chinese-English: | Model | System-Level Accuracy | System-Level Pearson | Segment-Level Pearson | Segment-Level Pairwise Acc | | ----------- | ----------- | ----------- | ----------- | ----------- | | MetricX-23-XXL | 0.868 | 0.919 | 0.605 | 0.551 | | MetricX-23-XL | 0.868 | 0.924 | 0.584 | 0.543 | | MetricX-23-Large | 0.857 | 0.919 | 0.555 | 0.539 | | MetricX-23-QE-XXL | 0.857 | 0.928 | 0.573 | 0.544 | | MetricX-23-QE-XL | 0.802 | 0.879 | 0.546 | 0.529 | | MetricX-23-QE-Large | 0.758 | 0.904 | 0.522 | 0.529 | The `metricx23/evaluate_wmt23.py` script re-calculates the average correlation score that was used to rank submissions from the [WMT'23 Shared Task](https://www2.statmt.org/wmt23/pdf/2023.wmt-1.51.pdf). Example usage: ```bash python -m metricx23.evaluate_wmt23 \ --en_de predictions_ende.jsonl \ --he_en predictions_heen.jsonl \ --zh_en predictions_zhen.jsonl \ --output_file output.json ``` Each of the 3 input files is expected to be in the same format as described above. Each file should correspond to running inference on each of the language pairs from the WMT'23 dataset. The results for each of the models is the following: | Model | Average Correlation | | ----------- | ----------- | | MetricX-23-XXL | 0.812 | | MetricX-23-XL | 0.813 | | MetricX-23-Large | 0.794 | | MetricX-23-QE-XXL | 0.797 | | MetricX-23-QE-XL | 0.767 | | MetricX-23-QE-Large | 0.762 | ## Citation If you use MetricX-23 in your research, please cite the following publication: ```bibtex @inproceedings{juraska-etal-2023-metricx, title = {{MetricX-23: The Google Submission to the WMT 2023 Metrics Shared Task}}, author = "Juraska, Juraj and Finkelstein, Mara and Deutsch, Daniel and Siddhant, Aditya and Mirzazadeh, Mehdi and Freitag, Markus", editor = "Koehn, Philipp and Haddow, Barry and Kocmi, Tom and Monz, Christof", booktitle = "Proceedings of the Eighth Conference on Machine Translation", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wmt-1.63", doi = "10.18653/v1/2023.wmt-1.63", pages = "756--767", } ```

timm/tf_efficientnet_b0.ns_jft_in1k

timm

timm

image-classification

--- tags: - image-classification - timm library_name: timm license: apache-2.0 datasets: - imagenet-1k --- # Model card for tf_efficientnet_b0.ns_jft_in1k A EfficientNet image classification model. Trained on ImageNet-1k and unlabeled JFT-300m using Noisy Student semi-supervised learning in Tensorflow by paper authors, ported to PyTorch by Ross Wightman. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 5.3 - GMACs: 0.4 - Activations (M): 6.7 - Image size: 224 x 224 - **Papers:** - EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks: https://arxiv.org/abs/1905.11946 - Self-training with Noisy Student improves ImageNet classification: https://arxiv.org/abs/1911.04252 - **Dataset:** ImageNet-1k - **Original:** https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet ## Model Usage ### Image Classification ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model('tf_efficientnet_b0.ns_jft_in1k', pretrained=True) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5) ``` ### Feature Map Extraction ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b0.ns_jft_in1k', pretrained=True, features_only=True, ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1 for o in output: # print shape of each feature map in output # e.g.: # torch.Size([1, 16, 112, 112]) # torch.Size([1, 24, 56, 56]) # torch.Size([1, 40, 28, 28]) # torch.Size([1, 112, 14, 14]) # torch.Size([1, 320, 7, 7]) print(o.shape) ``` ### Image Embeddings ```python from urllib.request import urlopen from PIL import Image import timm img = Image.open(urlopen( 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' )) model = timm.create_model( 'tf_efficientnet_b0.ns_jft_in1k', pretrained=True, num_classes=0, # remove classifier nn.Linear ) model = model.eval() # get model specific transforms (normalization, resize) data_config = timm.data.resolve_model_data_config(model) transforms = timm.data.create_transform(**data_config, is_training=False) output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor # or equivalently (without needing to set num_classes=0) output = model.forward_features(transforms(img).unsqueeze(0)) # output is unpooled, a (1, 1280, 7, 7) shaped tensor output = model.forward_head(output, pre_logits=True) # output is a (1, num_features) shaped tensor ``` ## Model Comparison Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results). ## Citation ```bibtex @inproceedings{tan2019efficientnet, title={Efficientnet: Rethinking model scaling for convolutional neural networks}, author={Tan, Mingxing and Le, Quoc}, booktitle={International conference on machine learning}, pages={6105--6114}, year={2019}, organization={PMLR} } ``` ```bibtex @article{Xie2019SelfTrainingWN, title={Self-Training With Noisy Student Improves ImageNet Classification}, author={Qizhe Xie and Eduard H. Hovy and Minh-Thang Luong and Quoc V. Le}, journal={2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2019}, pages={10684-10695} } ``` ```bibtex @misc{rw2019timm, author = {Ross Wightman}, title = {PyTorch Image Models}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, doi = {10.5281/zenodo.4414861}, howpublished = {\url{https://github.com/huggingface/pytorch-image-models}} } ```

EleutherAI/gpt-neo-1.3B

EleutherAI

transformers

text-generation

--- language: - en tags: - text generation - pytorch - causal-lm license: mit datasets: - EleutherAI/pile --- # GPT-Neo 1.3B ## Model Description GPT-Neo 1.3B is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 1.3B represents the number of parameters of this particular pre-trained model. ## Training data GPT-Neo 1.3B was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. ## Training procedure This model was trained on the Pile for 380 billion tokens over 362,000 steps. It was trained as a masked autoregressive language model, using cross-entropy loss. ## Intended Use and Limitations This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a prompt. ### How to use You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run: ```py >>> from transformers import pipeline >>> generator = pipeline('text-generation', model='EleutherAI/gpt-neo-1.3B') >>> generator("EleutherAI has", do_sample=True, min_length=50) [{'generated_text': 'EleutherAI has made a commitment to create new software packages for each of its major clients and has'}] ``` ### Limitations and Biases GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work. GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile. As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. ## Eval results ### Linguistic Reasoning | Model and Size | Pile BPB | Pile PPL | Wikitext PPL | Lambada PPL | Lambada Acc | Winogrande | Hellaswag | | ---------------- | ---------- | ---------- | ------------- | ----------- | ----------- | ---------- | ----------- | | **GPT-Neo 1.3B** | **0.7527** | **6.159** | **13.10** | **7.498** | **57.23%** | **55.01%** | **38.66%** | | GPT-2 1.5B | 1.0468 | ----- | 17.48 | 10.634 | 51.21% | 59.40% | 40.03% | | GPT-Neo 2.7B | 0.7165 | 5.646 | 11.39 | 5.626 | 62.22% | 56.50% | 42.73% | | GPT-3 Ada | 0.9631 | ----- | ----- | 9.954 | 51.60% | 52.90% | 35.93% | ### Physical and Scientific Reasoning | Model and Size | MathQA | PubMedQA | Piqa | | ---------------- | ---------- | ---------- | ----------- | | **GPT-Neo 1.3B** | **24.05%** | **54.40%** | **71.11%** | | GPT-2 1.5B | 23.64% | 58.33% | 70.78% | | GPT-Neo 2.7B | 24.72% | 57.54% | 72.14% | | GPT-3 Ada | 24.29% | 52.80% | 68.88% | ### Down-Stream Applications TBD ### BibTeX entry and citation info To cite this model, please use ```bibtex @software{gpt-neo, author = {Black, Sid and Leo, Gao and Wang, Phil and Leahy, Connor and Biderman, Stella}, title = {{GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow}}, month = mar, year = 2021, note = {{If you use this software, please cite it using these metadata.}}, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.5297715}, url = {https://doi.org/10.5281/zenodo.5297715} } @article{gao2020pile, title={The Pile: An 800GB Dataset of Diverse Text for Language Modeling}, author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and others}, journal={arXiv preprint arXiv:2101.00027}, year={2020} } ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_EleutherAI__gpt-neo-1.3B) | Metric | Value | |-----------------------|---------------------------| | Avg. | 29.44 | | ARC (25-shot) | 31.23 | | HellaSwag (10-shot) | 48.47 | | MMLU (5-shot) | 24.82 | | TruthfulQA (0-shot) | 39.63 | | Winogrande (5-shot) | 56.91 | | GSM8K (5-shot) | 0.45 | | DROP (3-shot) | 4.6 |

anton-l/wav2vec2-random-tiny-classifier

anton-l

transformers

audio-classification

Entry not found

microsoft/phi-1_5

microsoft

transformers

text-generation

--- license: mit license_link: https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE language: - en pipeline_tag: text-generation tags: - nlp - code --- ## Model Summary The language model Phi-1.5 is a Transformer with **1.3 billion** parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-1.5 demonstrates a nearly state-of-the-art performance among models with less than 10 billion parameters. We **did not** fine-tune Phi-1.5 either for **instruction following or through reinforcement learning from human feedback**. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more. For a safer model release, we exclude generic web-crawl data sources such as common-crawl from the training. This strategy prevents direct exposure to potentially harmful online content, enhancing the model's safety without RLHF. However, the model is still vulnerable to generating harmful content. We hope the model can help the research community to further study the safety of language models. Phi-1.5 can write poems, draft emails, create stories, summarize texts, write Python code (such as downloading a Hugging Face transformer model), etc. ## How to Use Phi-1.5 has been integrated in the `transformers` version 4.37.0, please ensure that you are using a version equal or higher than it. ## Intended Uses Given the nature of the training data, Phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. Note that Phi-1.5, being a base model, often produces irrelevant text following the main answer. In the following example, we've truncated the answer for illustrative purposes only. ### QA Format: ```markdown Write a detailed analogy between mathematics and a lighthouse. Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home. ``` where the model generates the text after "Answer:". ### Chat Format: ```markdown Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions? Bob: Have you tried using a timer? It can help you stay on track and avoid distractions. Alice: That's a good idea. I'll give it a try. Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time. Alice: That makes sense. I'll try that too. Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective. Alice: Thanks for the advice, guys. I feel more motivated now. Charlie: No problem, Alice. We're all in this together. Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other. ``` where the model generates the text after the first "Bob:". ### Code Format: ```python def print_prime(n): """ Print all primes between 1 and n """ primes = [] for num in range(2, n+1): is_prime = True for i in range(2, int(math.sqrt(num))+1): if num % i == 0: is_prime = False break if is_prime: primes.append(num) print(primes) ``` where the model generates the text after the comments. **Notes:** * Phi-1.5-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications. * Phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details. ## Sample Code ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", torch_dtype="auto") tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5") inputs = tokenizer('''def print_prime(n): """ Print all primes between 1 and n """''', return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` ## Limitations of Phi-1.5 * Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions. * Limited Scope for code: If the model generates Python scripts that utilize uncommon packages or scripts in other languages, we strongly recommend users manually verify all API uses. * Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users. * Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other language outside of English might pose challenges to its comprehension, leading to potential misinterpretations or errors in response. * Potential Societal Biases: Regardless of the safe data used for its training, the model is not entirely free from societal biases. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs. * Toxicity: Despite that the model is trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining. ## Training ### Model * Architecture: a Transformer-based model with next-word prediction objective * Dataset size: 30B tokens * Training tokens: 150B tokens * Precision: fp16 * GPUs: 32xA100-40G * Training time: 8 days ### Software * [PyTorch](https://github.com/pytorch/pytorch) * [DeepSpeed](https://github.com/microsoft/DeepSpeed) * [Flash-Attention](https://github.com/HazyResearch/flash-attention) ### License The model is licensed under the [MIT license](https://huggingface.co/microsoft/phi-1_5/resolve/main/LICENSE). ### Citation You can find the paper at https://arxiv.org/abs/2309.05463. Please cite as: ```bib @article{textbooks2, title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report}, author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat}, journal={arXiv preprint arXiv:2309.05463}, year={2023} } ``` ## Trademarks This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.

RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf

RichardErkhov

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) MoMo-72B-lora-1.8.6-DPO - GGUF - Model creator: https://huggingface.co/moreh/ - Original model: https://huggingface.co/moreh/MoMo-72B-lora-1.8.6-DPO/ | Name | Quant method | Size | | ---- | ---- | ---- | | [MoMo-72B-lora-1.8.6-DPO.Q2_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q2_K.gguf) | Q2_K | 25.22GB | | [MoMo-72B-lora-1.8.6-DPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_XS.gguf) | IQ3_XS | 27.88GB | | [MoMo-72B-lora-1.8.6-DPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_S.gguf) | IQ3_S | 29.4GB | | [MoMo-72B-lora-1.8.6-DPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_S.gguf) | Q3_K_S | 29.4GB | | [MoMo-72B-lora-1.8.6-DPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ3_M.gguf) | IQ3_M | 30.98GB | | [MoMo-72B-lora-1.8.6-DPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K.gguf) | Q3_K | 32.85GB | | [MoMo-72B-lora-1.8.6-DPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_M.gguf) | Q3_K_M | 32.85GB | | [MoMo-72B-lora-1.8.6-DPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.Q3_K_L.gguf) | Q3_K_L | 35.85GB | | [MoMo-72B-lora-1.8.6-DPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/blob/main/MoMo-72B-lora-1.8.6-DPO.IQ4_XS.gguf) | IQ4_XS | 36.41GB | | [MoMo-72B-lora-1.8.6-DPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_0 | 38.19GB | | [MoMo-72B-lora-1.8.6-DPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | IQ4_NL | 38.42GB | | [MoMo-72B-lora-1.8.6-DPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K_S | 38.45GB | | [MoMo-72B-lora-1.8.6-DPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K | 40.77GB | | [MoMo-72B-lora-1.8.6-DPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_K_M | 40.77GB | | [MoMo-72B-lora-1.8.6-DPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q4_1 | 42.32GB | | [MoMo-72B-lora-1.8.6-DPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_0 | 46.46GB | | [MoMo-72B-lora-1.8.6-DPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K_S | 46.46GB | | [MoMo-72B-lora-1.8.6-DPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K | 47.79GB | | [MoMo-72B-lora-1.8.6-DPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_K_M | 47.79GB | | [MoMo-72B-lora-1.8.6-DPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q5_1 | 50.59GB | | [MoMo-72B-lora-1.8.6-DPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q6_K | 55.24GB | | [MoMo-72B-lora-1.8.6-DPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/moreh_-_MoMo-72B-lora-1.8.6-DPO-gguf/tree/main/) | Q8_0 | 71.55GB | Original model description: --- license: mit language: - en --- # **Introduction** MoMo-72B-lora-1.8.6-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters. [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model. Note that we did not exploit any form of weight merge. For leaderboard submission, the trained weight is realigned for compatibility with llama. MoMo-72B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU. ## Details ### Used Librarys - torch - peft ### Used Datasets - [slimorca](Open-Orca/SlimOrca) - [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) - [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs) - No other dataset was used - No benchmark test set or the training set are used - [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result | Model | ARC | MMLU | TruthfulQA | GSM8K | |------------------------------|-------|-------|-------|-------| | **V1.8.6(result < 0.1, %)**| TBU |TBU | 0.73 | TBU | ### Used Environments - AMD MI250 & MoAI platform - Please visit https://moreh.io/product for more information about MoAI platform - Or, contact us directly [contact@moreh.io](mailto:contact@moreh.io) ## How to use ```python # pip install transformers==4.35.2 import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.6-DPO") model = AutoModelForCausalLM.from_pretrained( "moreh/MoMo-72B-lora-1.8.6-DPO" ) ```

google/vit-large-patch16-224

google

transformers

image-classification

--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet-1k - imagenet-21k --- # Vision Transformer (large-sized model) Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224, and fine-tuned on ImageNet 2012 (1 million images, 1,000 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Dosovitskiy et al. and first released in [this repository](https://github.com/google-research/vision_transformer). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman, who already converted the weights from JAX to PyTorch. Credits go to him. Disclaimer: The team releasing ViT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, at the same resolution, 224x224. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=google/vit) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: ```python from transformers import ViTFeatureExtractor, ViTForImageClassification from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-large-patch16-224') model = ViTForImageClassification.from_pretrained('google/vit-large-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # model predicts one of the 1000 ImageNet classes predicted_class_idx = logits.argmax(-1).item() print("Predicted class:", model.config.id2label[predicted_class_idx]) ``` Currently, both the feature extractor and model support PyTorch. Tensorflow and JAX/FLAX are coming soon, and the API of ViTFeatureExtractor might change. ## Training data The ViT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes, and fine-tuned on [ImageNet](http://www.image-net.org/challenges/LSVRC/2012/), a dataset consisting of 1 million images and 1k classes. ## Training procedure ### Preprocessing The exact details of preprocessing of images during training/validation can be found [here](https://github.com/google-research/vision_transformer/blob/master/vit_jax/input_pipeline.py). Images are resized/rescaled to the same resolution (224x224) and normalized across the RGB channels with mean (0.5, 0.5, 0.5) and standard deviation (0.5, 0.5, 0.5). ### Pretraining The model was trained on TPUv3 hardware (8 cores). All model variants are trained with a batch size of 4096 and learning rate warmup of 10k steps. For ImageNet, the authors found it beneficial to additionally apply gradient clipping at global norm 1. Pre-training resolution is 224. ## Evaluation results For evaluation results on several image classification benchmarks, we refer to tables 2 and 5 of the original paper. Note that for fine-tuning, the best results are obtained with a higher resolution (384x384). Of course, increasing the model size will result in better performance. ### BibTeX entry and citation info ```bibtex @misc{wu2020visual, title={Visual Transformers: Token-based Image Representation and Processing for Computer Vision}, author={Bichen Wu and Chenfeng Xu and Xiaoliang Dai and Alvin Wan and Peizhao Zhang and Zhicheng Yan and Masayoshi Tomizuka and Joseph Gonzalez and Kurt Keutzer and Peter Vajda}, year={2020}, eprint={2006.03677}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```bibtex @inproceedings{deng2009imagenet, title={Imagenet: A large-scale hierarchical image database}, author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li}, booktitle={2009 IEEE conference on computer vision and pattern recognition}, pages={248--255}, year={2009}, organization={Ieee} } ```

RishuD7/finetune_base_bge_pretrained_v4

RishuD7

transformers

feature-extraction

Entry not found

sonoisa/sentence-luke-japanese-base-lite

sonoisa

sentence-transformers

feature-extraction

--- language: ja license: apache-2.0 tags: - sentence-transformers - sentence-bert - sentence-luke - feature-extraction - sentence-similarity --- This is a Japanese sentence-LUKE model. 日本語用Sentence-LUKEモデルです。 [日本語Sentence-BERTモデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)と同一のデータセットと設定で学習しました。 手元の非公開データセットでは、[日本語Sentence-BERTモデル](https://huggingface.co/sonoisa/sentence-bert-base-ja-mean-tokens-v2)と比べて定量的な精度が同等〜0.5pt程度高く、定性的な精度は本モデルの方が高い結果でした。 事前学習済みモデルとして[studio-ousia/luke-japanese-base-lite](https://huggingface.co/studio-ousia/luke-japanese-base-lite)を利用させていただきました。 推論の実行にはSentencePieceが必要です（pip install sentencepiece）。 # 使い方 ```python from transformers import MLukeTokenizer, LukeModel import torch class SentenceLukeJapanese: def __init__(self, model_name_or_path, device=None): self.tokenizer = MLukeTokenizer.from_pretrained(model_name_or_path) self.model = LukeModel.from_pretrained(model_name_or_path) self.model.eval() if device is None: device = "cuda" if torch.cuda.is_available() else "cpu" self.device = torch.device(device) self.model.to(device) def _mean_pooling(self, model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) @torch.no_grad() def encode(self, sentences, batch_size=8): all_embeddings = [] iterator = range(0, len(sentences), batch_size) for batch_idx in iterator: batch = sentences[batch_idx:batch_idx + batch_size] encoded_input = self.tokenizer.batch_encode_plus(batch, padding="longest", truncation=True, return_tensors="pt").to(self.device) model_output = self.model(**encoded_input) sentence_embeddings = self._mean_pooling(model_output, encoded_input["attention_mask"]).to('cpu') all_embeddings.extend(sentence_embeddings) return torch.stack(all_embeddings) MODEL_NAME = "sonoisa/sentence-luke-japanese-base-lite" model = SentenceLukeJapanese(MODEL_NAME) sentences = ["暴走したAI", "暴走した人工知能"] sentence_embeddings = model.encode(sentences, batch_size=8) print("Sentence embeddings:", sentence_embeddings) ```

ptx0/terminus-lite-base-v1

ptx0

diffusers

text-to-image

--- license: creativeml-openrail-m base_model: "segmind/SSD-1B" tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - full inference: true --- # terminus-lite-base-v1 This is a full rank finetune derived from [segmind/SSD-1B](https://huggingface.co/segmind/SSD-1B). The main validation prompt used during training was: ``` a cute anime character named toast ``` ## Validation settings - CFG: `7.5` - CFG Rescale: `0.7` - Steps: `30` - Sampler: `euler` - Seed: `420420420` - Resolutions: `1024x1024,1152x960,896x1152` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 0 - Training steps: 600 - Learning rate: 1e-06 - Effective batch size: 16 - Micro-batch size: 4 - Gradient accumulation steps: 4 - Number of GPUs: 1 - Prediction type: v_prediction - Rescaled betas zero SNR: True - Optimizer: AdamW, stochastic bf16 - Precision: Pure BF16 - Xformers: Not used ## Datasets ### celebrities - Repeats: 4 - Total number of images: 1232 - Total number of aspect buckets: 2 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### movieposters - Repeats: 25 - Total number of images: 1712 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### normalnudes - Repeats: 5 - Total number of images: 1120 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### propagandaposters - Repeats: 0 - Total number of images: 560 - Total number of aspect buckets: 2 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### guys - Repeats: 5 - Total number of images: 368 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### pixel-art - Repeats: 0 - Total number of images: 1040 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### signs - Repeats: 25 - Total number of images: 384 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### moviecollection - Repeats: 0 - Total number of images: 1888 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### bookcovers - Repeats: 0 - Total number of images: 800 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### nijijourney - Repeats: 0 - Total number of images: 560 - Total number of aspect buckets: 1 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### experimental - Repeats: 0 - Total number of images: 3024 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### ethnic - Repeats: 0 - Total number of images: 3072 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### sports - Repeats: 0 - Total number of images: 784 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### gay - Repeats: 0 - Total number of images: 1072 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### architecture - Repeats: 0 - Total number of images: 4336 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### shutterstock - Repeats: 0 - Total number of images: 21088 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### cinemamix-1mp - Repeats: 0 - Total number of images: 9008 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### nsfw-1024 - Repeats: 0 - Total number of images: 10800 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### anatomy - Repeats: 5 - Total number of images: 16400 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### bg20k-1024 - Repeats: 0 - Total number of images: 89280 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### yoga - Repeats: 0 - Total number of images: 3600 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### photo-aesthetics - Repeats: 0 - Total number of images: 33120 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### text-1mp - Repeats: 25 - Total number of images: 13168 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ### photo-concept-bucket - Repeats: 0 - Total number of images: 567536 - Total number of aspect buckets: 3 - Resolution: 1.0 megapixels - Cropped: True - Crop style: random - Crop aspect: random ## Inference ```python import torch from diffusers import DiffusionPipeline model_id = "terminus-lite-base-v1" prompt = "a cute anime character named toast" negative_prompt = "malformed, disgusting, overexposed, washed-out" pipeline = DiffusionPipeline.from_pretrained(model_id) pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') image = pipeline( prompt=prompt, negative_prompt='blurry, cropped, ugly', num_inference_steps=30, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1152, height=768, guidance_scale=7.5, guidance_rescale=0.7, ).images[0] image.save("output.png", format="PNG") ```

facebook/dinov2-giant

facebook

transformers

image-feature-extraction

--- license: apache-2.0 tags: - dino - vision --- # Vision Transformer (giant-sized model) trained using DINOv2 Vision Transformer (ViT) model trained using the DINOv2 method. It was introduced in the paper [DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193) by Oquab et al. and first released in [this repository](https://github.com/facebookresearch/dinov2). Disclaimer: The team releasing DINOv2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pretrained on a large collection of images in a self-supervised fashion. Images are presented to the model as a sequence of fixed-size patches, which are linearly embedded. One also adds a [CLS] token to the beginning of a sequence to use it for classification tasks. One also adds absolute position embeddings before feeding the sequence to the layers of the Transformer encoder. Note that this model does not include any fine-tuned heads. By pre-training the model, it learns an inner representation of images that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. One typically places a linear layer on top of the [CLS] token, as the last hidden state of this token can be seen as a representation of an entire image. ## Intended uses & limitations You can use the raw model for feature extraction. See the [model hub](https://huggingface.co/models?search=facebook/dinov2) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, AutoModel from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained('facebook/dinov2-giant') model = AutoModel.from_pretrained('facebook/dinov2-giant') inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) last_hidden_states = outputs.last_hidden_state ``` ### BibTeX entry and citation info ```bibtex misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Maxime Oquab and Timothée Darcet and Théo Moutakanni and Huy Vo and Marc Szafraniec and Vasil Khalidov and Pierre Fernandez and Daniel Haziza and Francisco Massa and Alaaeldin El-Nouby and Mahmoud Assran and Nicolas Ballas and Wojciech Galuba and Russell Howes and Po-Yao Huang and Shang-Wen Li and Ishan Misra and Michael Rabbat and Vasu Sharma and Gabriel Synnaeve and Hu Xu and Hervé Jegou and Julien Mairal and Patrick Labatut and Armand Joulin and Piotr Bojanowski}, year={2023}, eprint={2304.07193}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

TheBloke/Llama-2-70B-Chat-AWQ

TheBloke

transformers

text-generation

--- language: - en license: llama2 tags: - facebook - meta - pytorch - llama - llama-2 model_name: Llama 2 70B Chat base_model: meta-llama/Llama-2-70b-chat-hf inference: false model_creator: Meta Llama 2 model_type: llama pipeline_tag: text-generation prompt_template: '[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don''t know the answer to a question, please don''t share false information. <</SYS>> {prompt}[/INST] ' quantized_by: TheBloke --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Llama 2 70B Chat - AWQ - Model creator: [Meta Llama 2](https://huggingface.co/meta-llama) - Original model: [Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) <!-- description start --> ## Description This repo contains AWQ model files for [Meta Llama 2's Llama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference. It is also now supported by continuous batching server [vLLM](https://github.com/vllm-project/vllm), allowing use of AWQ models for high-throughput concurrent inference in multi-user server scenarios. Note that, at the time of writing, overall throughput is still lower than running vLLM with unquantised models, however using AWQ enables using much smaller GPUs which can lead to easier deployment and overall cost savings. For example, a 70B model can be run on 1 x 48GB GPU instead of 2 x 80GB. <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Llama-2-70B-chat-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Llama-2-70B-chat-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) * [Meta Llama 2's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Llama-2-Chat ``` [INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files and AWQ parameters For my first release of AWQ models, I am releasing 128g models only. I will consider adding 32g as well if there is interest, and once I have done perplexity and evaluation comparisons, but at this time 32g models are still not fully tested with AutoAWQ and vLLM. Models are released as sharded safetensors files. | Branch | Bits | GS | AWQ Dataset | Seq Len | Size | | ------ | ---- | -- | ----------- | ------- | ---- | | [main](https://huggingface.co/TheBloke/Llama-2-70B-chat-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.61 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-use-from-vllm start --> ## Serving this model from vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - When using vLLM as a server, pass the `--quantization awq` parameter, for example: ```shell python3 python -m vllm.entrypoints.api_server --model TheBloke/Llama-2-70B-chat-AWQ --quantization awq ``` When using vLLM from Python code, pass the `quantization=awq` parameter, for example: ```python from vllm import LLM, SamplingParams prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Llama-2-70B-chat-AWQ", quantization="awq") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-python start --> ## How to use this AWQ model from Python code ### Install the necessary packages Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.0.2 or later ```shell pip3 install autoawq ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### You can then try the following example code ```python from awq import AutoAWQForCausalLM from transformers import AutoTokenizer model_name_or_path = "TheBloke/Llama-2-70B-chat-AWQ" # Load model model = AutoAWQForCausalLM.from_quantized(model_name_or_path, fuse_layers=True, trust_remote_code=False, safetensors=True) tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=False) prompt = "Tell me about AI" prompt_template=f'''[INST] <<SYS>> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <</SYS>> {prompt}[/INST] ''' print("\n\n*** Generate:") tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() # Generate output generation_output = model.generate( tokens, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, max_new_tokens=512 ) print("Output: ", tokenizer.decode(generation_output[0])) # Inference can also be done using transformers' pipeline from transformers import pipeline print("*** Pipeline:") pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1 ) print(pipe(prompt_template)[0]['generated_text']) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), and [vLLM](https://github.com/vllm-project/vllm). [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is not yet compatible with AWQ, but a PR is open which should bring support soon: [TGI PR #781](https://github.com/huggingface/text-generation-inference/issues/781). <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Meta Llama 2's Llama 2 70B Chat # **Llama 2** Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 70B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom. ## Model Details *Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.* Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM. **Model Developers** Meta **Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations. **Input** Models input text only. **Output** Models generate text only. **Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety. ||Training Data|Params|Content Length|GQA|Tokens|LR| |---|---|---|---|---|---|---| |Llama 2|*A new mix of publicly available online data*|7B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|13B|4k|&#10007;|2.0T|3.0 x 10^-4| |Llama 2|*A new mix of publicly available online data*|70B|4k|&#10004;|2.0T|1.5 x 10^-4| *Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability. **Model Dates** Llama 2 was trained between January 2023 and July 2023. **Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. **License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) **Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288) ## Intended Use **Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. **Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program. ||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO₂eq)| |---|---|---|---| |Llama 2 7B|184320|400|31.22| |Llama 2 13B|368640|400|62.44| |Llama 2 70B|1720320|400|291.42| |Total|3311616||539.00| **CO₂ emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data **Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023. ## Evaluation Results In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library. |Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval| |---|---|---|---|---|---|---|---|---|---| |Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9| |Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9| |Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7| |Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6| |Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3| |Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1| |Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**| **Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1. |||TruthfulQA|Toxigen| |---|---|---|---| |Llama 1|7B|27.42|23.00| |Llama 1|13B|41.74|23.08| |Llama 1|33B|44.19|22.57| |Llama 1|65B|48.71|21.77| |Llama 2|7B|33.29|**21.25**| |Llama 2|13B|41.86|26.10| |Llama 2|70B|**50.18**|24.60| **Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better). |||TruthfulQA|Toxigen| |---|---|---|---| |Llama-2-Chat|7B|57.04|**0.00**| |Llama-2-Chat|13B|62.18|**0.00**| |Llama-2-Chat|70B|**64.14**|0.01| **Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above. ## Ethical Considerations and Limitations Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide) ## Reporting Issues Please report any software “bug,” or other problems with the models through one of the following means: - Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama) - Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback) - Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info) ## Llama Model Index |Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf| |---|---|---|---|---| |7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)| |13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)| |70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|

mixedbread-ai/mxbai-rerank-xsmall-v1

mixedbread-ai

transformers

text-classification

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fill="#eb5a57" d="M1216.359 827.958c-4.331-3.733-8.603-7.379-12.326-11.518l-26.664-30.44c-.866-.989-1.89-1.839-3.152-2.902 6.483-6.054 13.276-11.959 20.371-18.005l39.315 44.704c-5.648 6.216-11.441 12.12-17.544 18.161z"></path><path fill="#ec6168" d="M1231.598 334.101l38.999 36.066c.931.876 1.456 2.183 2.303 3.608-4.283 4.279-8.7 8.24-13.769 12.091-4.2-3.051-7.512-6.349-11.338-8.867-12.36-8.136-22.893-18.27-32.841-29.093l16.646-13.805z"></path><path fill="#ed656e" d="M1214.597 347.955c10.303 10.775 20.836 20.908 33.196 29.044 3.825 2.518 7.137 5.816 10.992 8.903-3.171 4.397-6.65 8.648-10.432 13.046-6.785-5.184-13.998-9.858-19.529-16.038-4.946-5.527-9.687-8.644-17.309-8.215-2.616.147-5.734-2.788-8.067-4.923-3.026-2.769-5.497-6.144-8.35-9.568 6.286-4.273 12.715-8.237 19.499-12.25z"></path></svg> </p> <p align="center"> <b>The crispy rerank family from <a href="https://mixedbread.ai"><b>mixedbread ai</b></a>.</b> </p> # mxbai-rerank-xsmall-v1 This is the smallest model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v1). We have three models: - [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) (🍞) - [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) - [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) ## Quickstart Currently, the best way to use our models is with the most recent version of sentence-transformers. `pip install -U sentence-transformers` Let's say you have a query, and you want to rerank a set of documents. You can do that with only one line of code: ```python from sentence_transformers import CrossEncoder # Load the model, here we use our base sized model model = CrossEncoder("mixedbread-ai/mxbai-rerank-xsmall-v1") # Example query and documents query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) ``` <details> <summary>JavaScript Example</summary> Install [transformers.js](https://github.com/xenova/transformers.js) `npm i @xenova/transformers` Let's say you have a query, and you want to rerank a set of documents. In JavaScript, you need to add a function: ```javascript import { AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers'; const model_id = 'mixedbread-ai/mxbai-rerank-xsmall-v1'; const model = await AutoModelForSequenceClassification.from_pretrained(model_id); const tokenizer = await AutoTokenizer.from_pretrained(model_id); /** * Performs ranking with the CrossEncoder on the given query and documents. Returns a sorted list with the document indices and scores. * @param {string} query A single query * @param {string[]} documents A list of documents * @param {Object} options Options for ranking * @param {number} [options.top_k=undefined] Return the top-k documents. If undefined, all documents are returned. * @param {number} [options.return_documents=false] If true, also returns the documents. If false, only returns the indices and scores. */ async function rank(query, documents, { top_k = undefined, return_documents = false, } = {}) { const inputs = tokenizer( new Array(documents.length).fill(query), { text_pair: documents, padding: true, truncation: true, } ) const { logits } = await model(inputs); return logits .sigmoid() .tolist() .map(([score], i) => ({ corpus_id: i, score, ...(return_documents ? { text: documents[i] } : {}) })) .sort((a, b) => b.score - a.score) .slice(0, top_k); } // Example usage: const query = "Who wrote 'To Kill a Mockingbird'?" const documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] const results = await rank(query, documents, { return_documents: true, top_k: 3 }); console.log(results); ``` </details> ## Using API You can use the large model via our API as follows: ```python from mixedbread_ai.client import MixedbreadAI mxbai = MixedbreadAI(api_key="{MIXEDBREAD_API_KEY}") res = mxbai.reranking( model="mixedbread-ai/mxbai-rerank-large-v1", query="Who is the author of To Kill a Mockingbird?", input=[ "To Kill a Mockingbird is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel Moby-Dick was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel To Kill a Mockingbird, was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The Harry Potter series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "The Great Gatsby, a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ], top_k=3, return_input=false ) print(res.data) ``` The API comes with additional features, such as a continous trained reranker! Check out the [docs](https://www.mixedbread.ai/docs) for more information. ## Evaluation Our reranker models are designed to elevate your search. They work extremely well in combination with keyword search and can even outperform semantic search systems in many cases. | Model | NDCG@10 | Accuracy@3 | | ------------------------------------------------------------------------------------- | -------- | ---------- | | Lexical Search (Lucene) | 38.0 | 66.4 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 41.6 | 66.9 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 45.2 | 70.6 | | cohere-embed-v3 (semantic search) | 47.5 | 70.9 | | [mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) | **43.9** | **70.0** | | [mxbai-rerank-base-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v1) | **46.9** | **72.3** | | [mxbai-rerank-large-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v1) | **48.8** | **74.9** | The reported results are aggregated from 11 datasets of BEIR. We used [Pyserini](https://github.com/castorini/pyserini/) to evaluate the models. Find more in our [blog-post](https://www.mixedbread.ai/blog/mxbai-rerank-v1) and on this [spreadsheet](https://docs.google.com/spreadsheets/d/15ELkSMFv-oHa5TRiIjDvhIstH9dlc3pnZeO-iGz4Ld4/edit?usp=sharing). ## Community Please join our [Discord Community](https://discord.gg/jDfMHzAVfU) and share your feedback and thoughts! We are here to help and also always happy to chat. ## License Apache 2.0

lmstudio-community/Meta-Llama-3-8B-Instruct-GGUF

lmstudio-community

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 base_model: meta-llama/Meta-Llama-3-8B-Instruct license: llama3 extra_gated_prompt: >- ### META LLAMA 3 COMMUNITY LICENSE AGREEMENT Meta Llama 3 Version Release Date: April 18, 2024 "Agreement" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein. "Documentation" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/. "Licensee" or "you" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. "Meta Llama 3" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads. "Llama Materials" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement. "Meta" or "we" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland). 1. License Rights and Redistribution. a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials. b. Redistribution and Use. i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name. ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you. iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.” iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement. v. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof). 2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights. 3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS. 4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 5. Intellectual Property. a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama 3” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta. b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications. c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials. 6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement. 7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement. ### Meta Llama 3 Acceptable Use Policy Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy) #### Prohibited Uses We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others’ rights, including to: 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as: 1. Violence or terrorism 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material 3. Human trafficking, exploitation, and sexual violence 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials. 5. Sexual solicitation 6. Any other criminal activity 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system 2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following: 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State 2. Guns and illegal weapons (including weapon development) 3. Illegal drugs and regulated/controlled substances 4. Operation of critical infrastructure, transportation technologies, or heavy machinery 5. Self-harm or harm to others, including suicide, cutting, and eating disorders 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual 3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following: 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content 3. Generating, promoting, or further distributing spam 4. Impersonating another individual without consent, authorization, or legal right 5. Representing that the use of Meta Llama 3 or outputs are human-generated 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement 4. Fail to appropriately disclose to end users any known dangers of your AI system Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means: * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3) * Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback * Reporting bugs and security concerns: facebook.com/whitehat/info * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: LlamaUseReport@meta.com extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit quantized_by: bartowski lm_studio: param_count: 8b use_case: general release_date: 18-04-2024 model_creator: meta-llama prompt_template: Llama 3 system_prompt: You are a helpful AI assistant. base_model: llama original_repo: meta-llama/Meta-Llama-3-8B-Instruct --- ## 💫 Community Model> Llama 3 8B Instruct by Meta *👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*. **Model creator:** [meta-llama](https://huggingface.co/meta-llama)<br> **Original model**: [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)<br> **GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` PR [6745](https://github.com/ggerganov/llama.cpp/pull/6745)<br> ## Model Summary: Llama 3 represents a huge update to the Llama family of models. This model is the 8B parameter instruction tuned model, meaning it's small, fast, and tuned for following instructions.<br> This model is very happy to follow the given system prompt, so use this to your advantage to get the behavior you desire.<br> Llama 3 excels at all the general usage situations, including multi turn conversations, general world knowledge, and coding.<br> This 8B model exceeds the performance of Llama 2's 70B model, showing that the performance is far greater than the previous iteration. ## Prompt Template: Choose the 'Llama 3' preset in your LM Studio. Under the hood, the model will see a prompt that's formatted like so: ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Use case and examples Llama 3 should be great for anything you throw at it. Try it with conversations, coding, and just all around general inquiries. ## Creative conversations Using a system prompt of `You are a pirate chatbot who always responds in pirate speak!`  ## General knowledge  ## Coding  ## Technical Details Llama 3 was trained on over 15T tokens from a massively diverse range of subjects and languages, and includes 4 times more code than Llama 2. This model also features Grouped Attention Query (GQA) so that memory usage scales nicely over large contexts. Instruction fine tuning was performed with a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct policy optimization (DPO). Check out their blog post for more information [here](https://ai.meta.com/blog/meta-llama-3/) ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. 🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) for his dataset (linked [here](https://github.com/ggerganov/llama.cpp/discussions/5263)) that was used for calculating the imatrix for these quants, which improves the overall quality! ## Disclaimers LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

Rostlab/prot_t5_xl_bfd

Rostlab

transformers

text2text-generation

--- language: protein tags: - protein language model datasets: - BFD --- # ProtT5-XL-BFD model Pretrained model on protein sequences using a masked language modeling (MLM) objective. It was introduced in [this paper](https://doi.org/10.1101/2020.07.12.199554) and first released in [this repository](https://github.com/agemagician/ProtTrans). This model is trained on uppercase amino acids: it only works with capital letter amino acids. ## Model description ProtT5-XL-BFD is based on the `t5-3b` model and was pretrained on a large corpus of protein sequences in a self-supervised fashion. This means it was pretrained on the raw protein sequences only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those protein sequences. One important difference between this T5 model and the original T5 version is the denosing objective. The original T5-3B model was pretrained using a span denosing objective, while this model was pre-trained with a Bart-like MLM denosing objective. The masking probability is consistent with the original T5 training by randomly masking 15% of the amino acids in the input. It has been shown that the features extracted from this self-supervised model (LM-embeddings) captured important biophysical properties governing protein shape. shape. This implied learning some of the grammar of the language of life realized in protein sequences. ## Intended uses & limitations The model could be used for protein feature extraction or to be fine-tuned on downstream tasks. We have noticed in some tasks on can gain more accuracy by fine-tuning the model rather than using it as a feature extractor. We have also noticed that for feature extraction, its better to use the feature extracted from the encoder not from the decoder. ### How to use Here is how to use this model to extract the features of a given protein sequence in PyTorch: ```python from transformers import T5Tokenizer, T5Model import re import torch tokenizer = T5Tokenizer.from_pretrained('Rostlab/prot_t5_xl_bfd', do_lower_case=False) model = T5Model.from_pretrained("Rostlab/prot_t5_xl_bfd") sequences_Example = ["A E T C Z A O","S K T Z P"] sequences_Example = [re.sub(r"[UZOB]", "X", sequence) for sequence in sequences_Example] ids = tokenizer.batch_encode_plus(sequences_Example, add_special_tokens=True, padding=True) input_ids = torch.tensor(ids['input_ids']) attention_mask = torch.tensor(ids['attention_mask']) with torch.no_grad(): embedding = model(input_ids=input_ids,attention_mask=attention_mask,decoder_input_ids=None) # For feature extraction we recommend to use the encoder embedding encoder_embedding = embedding[2].cpu().numpy() decoder_embedding = embedding[0].cpu().numpy() ``` ## Training data The ProtT5-XL-BFD model was pretrained on [BFD](https://bfd.mmseqs.com/), a dataset consisting of 2.1 billion protein sequences. ## Training procedure ### Preprocessing The protein sequences are uppercased and tokenized using a single space and a vocabulary size of 21. The rare amino acids "U,Z,O,B" were mapped to "X". The inputs of the model are then of the form: ``` Protein Sequence [EOS] ``` The preprocessing step was performed on the fly, by cutting and padding the protein sequences up to 512 tokens. The details of the masking procedure for each sequence are as follows: - 15% of the amino acids are masked. - In 90% of the cases, the masked amino acids are replaced by `[MASK]` token. - In 10% of the cases, the masked amino acids are replaced by a random amino acid (different) from the one they replace. ### Pretraining The model was trained on a single TPU Pod V3-1024 for 1.2 million steps in total, using sequence length 512 (batch size 4k). It has a total of approximately 3B parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training. ## Evaluation results When the model is used for feature etraction, this model achieves the following results: Test results : | Task/Dataset | secondary structure (3-states) | secondary structure (8-states) | Localization | Membrane | |:-----:|:-----:|:-----:|:-----:|:-----:| | CASP12 | 77 | 66 | | | | TS115 | 85 | 74 | | | | CB513 | 84 | 71 | | | | DeepLoc | | | 77 | 91 | ### BibTeX entry and citation info ```bibtex @article {Elnaggar2020.07.12.199554, author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard}, title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing}, elocation-id = {2020.07.12.199554}, year = {2020}, doi = {10.1101/2020.07.12.199554}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \&lt;a href="https://github.com/agemagician/ProtTrans"\&gt;https://github.com/agemagician/ProtTrans\&lt;/a\&gt;Competing Interest StatementThe authors have declared no competing interest.}, URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554}, eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf}, journal = {bioRxiv} } ``` > Created by [Ahmed Elnaggar/@Elnaggar_AI](https://twitter.com/Elnaggar_AI) | [LinkedIn](https://www.linkedin.com/in/prof-ahmed-elnaggar/)

michaelfeil/bge-small-en-v1.5

michaelfeil

sentence-transformers

feature-extraction

--- tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers license: mit language: - en --- <h1 align="center">Infinity Embedding Model</h1> This is the stable default model for infinity. ```bash pip install infinity_emb[all] ``` More details about the infinity inference project please refer to the Github: [Infinity](https://github.com/michaelfeil/infinity). ## Usage for Embedding Model via infinity in Python To deploy files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package. Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference. ```python import asyncio from infinity_emb import AsyncEmbeddingEngine, EngineArgs sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] engine = AsyncEmbeddingEngine.from_args( EngineArgs( model_name_or_path = "michaelfeil/bge-small-en-v1.5", device="cuda", # or device="cpu" engine="torch", # or engine="optimum" compile=True # enable torch.compile )) async def main(): async with engine: embeddings, usage = await engine.embed(sentences=sentences) asyncio.run(main()) ``` ## CLI interface The same args ```bash pip install infinity_emb infinity_emb --model-name-or-path michaelfeil/bge-small-en-v1.5 --port 7997 ``` ## Contact If you have any question or suggestion related to this project, feel free to open an issue or pull request. You also can email Michael Feil (infinity at michaelfeil.eu). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @software{Feil_Infinity_2023, author = {Feil, Michael}, month = oct, title = {{Infinity - To Embeddings and Beyond}}, url = {https://github.com/michaelfeil/infinity}, year = {2023} } ``` ## License Infinity is licensed under the [MIT License](https://github.com/michaelfeil/infinity/blob/master/LICENSE).

sentence-transformers/sentence-t5-xl

sentence-transformers

sentence-transformers

sentence-similarity

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity pipeline_tag: sentence-similarity --- # sentence-transformers/sentence-t5-xl This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks. This model was converted from the Tensorflow model [st5-3b-1](https://tfhub.dev/google/sentence-t5/st5-3b/1) to PyTorch. When using this model, have a look at the publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877). The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-3B model. The weights are stored in FP16. ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('sentence-transformers/sentence-t5-xl') embeddings = model.encode(sentences) print(embeddings) ``` The model requires sentence-transformers version 2.2.0 or newer. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/sentence-t5-xl) ## Citing & Authors If you find this model helpful, please cite the respective publication: [Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models](https://arxiv.org/abs/2108.08877)

lpiccinelli/unidepth-v2-vitl14

lpiccinelli

UniDepth

--- library_name: UniDepth tags: - monocular-metric-depth-estimation - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/lpiccinelli-eth/UniDepth - Docs: [More Information Needed]

sihaochen/SegmenT5-large

sihaochen

transformers

text2text-generation

--- license: cc language: - en --- This is the proposition segmentation model from "Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations" by Chen et. al. 2023. ## What does the model do? It splits a complex, long-form sentence into a list of propositions -- i.e. self-contained, atomic pieces of meaning in the sentence. For example, the following sentence -- ``` "Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist." ``` will be split into -- ``` ['Dracula is a novel by Bram Stoker.', 'Count Dracula is the protagonist of Dracula.'] ``` ## Usage The prompt to the model is formatted like: `segment sentence: {input_sentence}`. For each sentence, the model will output the propositions concatenated by `[sep]` as a string. For example, if we use the following example code to segment `"Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist."`. The model output will be `['Dracula is a novel by Bram Stoker.[sep]Count Dracula is the protagonist of Dracula.']` ``` from transformers import AutoTokenizer, AutoModelForSeq2SeqLM gen_kwargs = { "length_penalty": 0, "max_new_tokens": 256, "min_length": 10, "no_repeat_ngram_size": 0, "num_beams": 1, } SEGMENT5_PROMPT = "segment sentence: {}" SEGMENT5_SEP_TOKEN = "[sep]" model = AutoModelForSeq2SeqLM.from_pretrained("sihaochen/SegmenT5-large") tokenizer = AutoTokenizer.from_pretrained("sihaochen/SegmenT5-large") model.eval() # define an example input sentence example_sentence = "Dracula is a novel by Bram Stoker featuring Count Dracula as the protagonist." example_input = SEGMENT5_PROMPT.format(example_sentence) input_ids = tokenizer(example_input, return_tensors="pt", padding="max_length", max_length=512, truncation=True).input_ids logits = model.generate(input_ids, **gen_kwargs) outputs = tokenizer.batch_decode(logits, skip_special_tokens=True) output = outputs[0].split(SEGMENT5_SEP_TOKEN) print(output) # Output: ['Dracula is a novel by Bram Stoker.', 'Count Dracula is the protagonist of Dracula.'] ``` ## Sub-Sentence Encoder For model checkpoints + code for the sub-sentence encoders, checkout: https://github.com/schen149/sub-sentence-encoder/ ## Citation ``` @article{chen2023subsentence, title={Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations}, author={Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu}, journal={arXiv preprint arXiv:2311.04335}, year={2023}, URL = {https://arxiv.org/pdf/2311.04335.pdf} } ```

crusoeai/dolphin-2.9.3-Yi-1.5-34B-32k-GGUF

crusoeai

Entry not found

mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF

mradermacher

transformers

--- base_model: ryzen88/Llama-3-70b-Arimas-story-RP-V2.1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V2.1 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-i1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants. <!-- end -->

facebook/opt-2.7b

facebook

transformers

text-generation

--- language: en inference: false tags: - text-generation - opt license: other commercial: false --- # OPT : Open Pre-trained Transformer Language Models OPT was first introduced in [Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) and first released in [metaseq's repository](https://github.com/facebookresearch/metaseq) on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the [paper](https://arxiv.org/pdf/2205.01068.pdf). Content from **this** model card has been written by the Hugging Face team. ## Intro To quote the first two paragraphs of the [official paper](https://arxiv.org/abs/2205.01068) > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity. > We present Open Pretrained Transformers (OPT), a suite of decoder-only pre-trained transformers ranging from 125M > to 175B parameters, which we aim to fully and responsibly share with interested researchers. We train the OPT models to roughly match > the performance and sizes of the GPT-3 class of models, while also applying the latest best practices in data > collection and efficient training. Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and > to bring more voices to the table in studying the impact of these LLMs. Definitions of risk, harm, bias, and toxicity, etc., should be articulated by the > collective research community as a whole, which is only possible when models are available for study. ## Model description OPT was predominantly pretrained with English text, but a small amount of non-English data is still present within the training corpus via CommonCrawl. The model was pretrained using a causal language modeling (CLM) objective. OPT belongs to the same family of decoder-only models like [GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective. For evaluation, OPT follows [GPT-3](https://arxiv.org/abs/2005.14165) by using their prompts and overall experimental setup. For more details, please read the [official paper](https://arxiv.org/abs/2205.01068). ## Intended uses & limitations The pretrained-only model can be used for prompting for evaluation of downstream tasks as well as text generation. In addition, the model can be fine-tuned on a downstream task using the [CLM example](https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling). For all other OPT checkpoints, please have a look at the [model hub](https://huggingface.co/models?filter=opt). ### How to use You can use this model directly with a pipeline for text generation. ```python >>> from transformers import pipeline >>> generator = pipeline('text-generation', model="facebook/opt-2.7b") >>> generator("What are we having for dinner?") [{'generated_text': 'What are we having for dinner?\nI'm thinking pizza.\nI'm thinking tacos.\n'}] ``` By default, generation is deterministic. In order to use the top-k sampling, please set `do_sample` to `True`. ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True) >>> generator("What are we having for dinner?") [{'generated_text': "What are we having for dinner?\nJust pizza?\nWell, I suppose that would suffice."}] ``` ### Limitations and bias As mentioned in Meta AI's model card, given that the training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral the model is strongly biased : > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. Here's an example of how the model can have biased predictions: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The woman worked as a") [{'generated_text': "The woman worked as a security guard at a nursery in the city's eastern district of Samut P"}, {'generated_text': 'The woman worked as a doctor in the Philippines. Officials in China allege she stole the coronavirus'}, {'generated_text': 'The woman worked as a teacher in the city of Krasnodar in south Russia. She'}, {'generated_text': 'The woman worked as a researcher and lecturer at the Russian Academy of Sciences in a laboratory dedicated to the'}, {'generated_text': 'The woman worked as a nanny on a property owned by Mr Fitton-Allen in the city'}] ``` compared to: ```python >>> from transformers import pipeline, set_seed >>> set_seed(32) >>> generator = pipeline('text-generation', model="facebook/opt-2.7b", do_sample=True, num_return_sequences=5) >>> generator("The man worked as a") [{'generated_text': "The man worked as a security guard at a retirement home after being hired by the administrator's cousin,"}, {'generated_text': 'The man worked as a doctor in the Philippines.\n\nHe had hoped to work his way back'}, {'generated_text': 'The man worked as a teacher in the city of Krasnodar in south Russia.He'}, {'generated_text': 'The man worked as a researcher and his work on the topic predates the project, by many years'}, {'generated_text': 'The man worked as a chef in a restaurant for 40 years. How could this be so different from'}] ``` This bias will also affect all fine-tuned versions of this model. ## Training data The Meta AI team wanted to train this model on a corpus as large as possible. It is composed of the union of the following 5 filtered datasets of textual documents: - BookCorpus, which consists of more than 10K unpublished books, - CC-Stories, which contains a subset of CommonCrawl data filtered to match the story-like style of Winograd schemas, - The Pile, from which * Pile-CC, OpenWebText2, USPTO, Project Gutenberg, OpenSubtitles, Wikipedia, DM Mathematics and HackerNews* were included. - Pushshift.io Reddit dataset that was developed in Baumgartner et al. (2020) and processed in Roller et al. (2021) - CCNewsV2 containing an updated version of the English portion of the CommonCrawl News dataset that was used in RoBERTa (Liu et al., 2019b) The final training data contains 180B tokens corresponding to 800GB of data. The validation split was made of 200MB of the pretraining data, sampled proportionally to each dataset’s size in the pretraining corpus. The dataset might contains offensive content as parts of the dataset are a subset of public Common Crawl data, along with a subset of public Reddit data, which could contain sentences that, if viewed directly, can be insulting, threatening, or might otherwise cause anxiety. ### Collection process The dataset was collected form internet, and went through classic data processing algorithms and re-formatting practices, including removing repetitive/non-informative text like *Chapter One* or *This ebook by Project Gutenberg.* ## Training procedure ### Preprocessing The texts are tokenized using the **GPT2** byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a vocabulary size of 50272. The inputs are sequences of 2048 consecutive tokens. The 175B model was trained on 992 *80GB A100 GPUs*. The training duration was roughly ~33 days of continuous training. ### BibTeX entry and citation info ```bibtex @misc{zhang2022opt, title={OPT: Open Pre-trained Transformer Language Models}, author={Susan Zhang and Stephen Roller and Naman Goyal and Mikel Artetxe and Moya Chen and Shuohui Chen and Christopher Dewan and Mona Diab and Xian Li and Xi Victoria Lin and Todor Mihaylov and Myle Ott and Sam Shleifer and Kurt Shuster and Daniel Simig and Punit Singh Koura and Anjali Sridhar and Tianlu Wang and Luke Zettlemoyer}, year={2022}, eprint={2205.01068}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit

HuggingFaceM4

transformers

zero-shot-image-classification

--- license: apache-2.0 --- Same as https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2 with two changes: - increase max resolution to 980 x 980 (instead of 384 x 384) by interpolating the position embeddings - implement the strategy in [NaViT](https://arxiv.org/abs/2307.06304) to allow a/ variable resoltion images, b/ aspect ratio preserved images These changes only apply to the vision tower. No changes to the text tower. Implementation is fully backward compatible to `https://huggingface.co/HuggingFaceM4/siglip-so400m-14-384-flash-attn2` -> just don't specify the `patch_attention_mask` Usage: ```python import torch from modeling_siglip import SiglipVisionModel DEVICE = torch.device("cuda:0") PATCH_SIZE = 14 pixel_values = torch.randn(2, 3, 28, 42, dtype=torch.bfloat16, device=DEVICE) pixel_attention_mask = [ [ [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [1] * 14 + [1] * 14 + [1] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, [0] * 14 + [0] * 14 + [0] * 14, ], [ [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, [1] * 14 + [1] * 14 + [0] * 14, ], ] pixel_attention_mask = torch.tensor(pixel_attention_mask, dtype=torch.bool, device=DEVICE) patches_subgrid = pixel_attention_mask.unfold( dimension=1, size=PATCH_SIZE, step=PATCH_SIZE ).unfold(dimension=2, size=PATCH_SIZE, step=PATCH_SIZE) patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool() model = SiglipVisionModel.from_pretrained("HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit", _flash_attn_2_enabled=True) model.train() model.vision_model.to(DEVICE, dtype=torch.bfloat16) output = model.vision_model(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) ```

LiheYoung/depth-anything-large-hf

LiheYoung

transformers

depth-estimation

--- license: apache-2.0 tags: - vision pipeline_tag: depth-estimation widget: - inference: false --- # Depth Anything (large-sized model, Transformers version) Depth Anything model. It was introduced in the paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891) by Lihe Yang et al. and first released in [this repository](https://github.com/LiheYoung/Depth-Anything). [Online demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) is also provided. Disclaimer: The team releasing Depth Anything did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description Depth Anything leverages the [DPT](https://huggingface.co/docs/transformers/model_doc/dpt) architecture with a [DINOv2](https://huggingface.co/docs/transformers/model_doc/dinov2) backbone. The model is trained on ~62 million images, obtaining state-of-the-art results for both relative and absolute depth estimation. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/depth_anything_overview.jpg" alt="drawing" width="600"/> <small> Depth Anything overview. Taken from the <a href="https://arxiv.org/abs/2401.10891">original paper</a>.</small> ## Intended uses & limitations You can use the raw model for tasks like zero-shot depth estimation. See the [model hub](https://huggingface.co/models?search=depth-anything) to look for other versions on a task that interests you. ### How to use Here is how to use this model to perform zero-shot depth estimation: ```python from transformers import pipeline from PIL import Image import requests # load pipe pipe = pipeline(task="depth-estimation", model="LiheYoung/depth-anything-large-hf") # load image url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) # inference depth = pipe(image)["depth"] ``` Alternatively, one can use the classes themselves: ```python from transformers import AutoImageProcessor, AutoModelForDepthEstimation import torch import numpy as np from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) image_processor = AutoImageProcessor.from_pretrained("LiheYoung/depth-anything-large-hf") model = AutoModelForDepthEstimation.from_pretrained("LiheYoung/depth-anything-large-hf") # prepare image for the model inputs = image_processor(images=image, return_tensors="pt") with torch.no_grad(): outputs = model(**inputs) predicted_depth = outputs.predicted_depth # interpolate to original size prediction = torch.nn.functional.interpolate( predicted_depth.unsqueeze(1), size=image.size[::-1], mode="bicubic", align_corners=False, ) ``` For more code examples, we refer to the [documentation](https://huggingface.co/transformers/main/model_doc/depth_anything.html#). ### BibTeX entry and citation info ```bibtex @misc{yang2024depth, title={Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data}, author={Lihe Yang and Bingyi Kang and Zilong Huang and Xiaogang Xu and Jiashi Feng and Hengshuang Zhao}, year={2024}, eprint={2401.10891}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

lpiccinelli/unidepth-v2old-vitl14

lpiccinelli

UniDepth

--- library_name: UniDepth tags: - monocular-metric-depth-estimation - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/lpiccinelli-eth/UniDepth - Docs: [More Information Needed]