Datasets:

librarian-bots

/

model_cards_with_metadata_test

like 1

CreativeLang/novel_metaphors

CreativeLang

transformers

token-classification

Entry not found

liyucheng/frame_finder

liyucheng

transformers

token-classification

--- license: cc datasets: - liyucheng/FrameNet_v17 language: - en --- # Frame Classification This model is trained FrameNet v1.7. Check out the training dataset [here](https://huggingface.co/datasets/liyucheng/FrameNet_v17). The data is loaded with `ds = dataset.load_dataset('liyucheng/FrameNet_v17', name = 'frame_label')`. This flatten all frame annotation to specific sentences, making frame classification a sequence tagging task. # Metrics ``` {'accuracy_score': 0.8382018348623853, 'precision': 0.8382018348623853, 'recall': 0.8382018348623853, 'micro_f1': 0.8382018348623853, 'macro_f1': 0.45824850358482677} ```

facebook/convnext-tiny-224

facebook

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # ConvNeXT (tiny-sized model) ConvNeXT model trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Liu et al. and first released in [this repository](https://github.com/facebookresearch/ConvNeXt). Disclaimer: The team releasing ConvNeXT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ConvNeXT is a pure convolutional model (ConvNet), inspired by the design of Vision Transformers, that claims to outperform them. The authors started from a ResNet and "modernized" its design by taking the Swin Transformer as inspiration.  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=convnext) 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 ConvNextImageProcessor, ConvNextForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] processor = ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224") model = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224") inputs = processor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]), ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/convnext). ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2201-03545, author = {Zhuang Liu and Hanzi Mao and Chao{-}Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, title = {A ConvNet for the 2020s}, journal = {CoRR}, volume = {abs/2201.03545}, year = {2022}, url = {https://arxiv.org/abs/2201.03545}, eprinttype = {arXiv}, eprint = {2201.03545}, timestamp = {Thu, 20 Jan 2022 14:21:35 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-03545.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

timm/vit_small_patch14_dinov2.lvd142m

timm

timm

image-feature-extraction

--- license: apache-2.0 library_name: timm tags: - image-feature-extraction - timm --- # Model card for vit_small_patch14_dinov2.lvd142m A Vision Transformer (ViT) image feature model. Pretrained on LVD-142M with self-supervised DINOv2 method. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.1 - GMACs: 46.8 - Activations (M): 198.8 - Image size: 518 x 518 - **Papers:** - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Original:** https://github.com/facebookresearch/dinov2 - **Pretrain Dataset:** LVD-142M ## 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('vit_small_patch14_dinov2.lvd142m', 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) ``` ### 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( 'vit_small_patch14_dinov2.lvd142m', 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, 1370, 384) 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 @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, 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}} } ```

kwoncho/losscut_news_pre2020_2

kwoncho

transformers

text-classification

Entry not found

mradermacher/Yi-34B-Chat-GGUF

mradermacher

transformers

--- base_model: 01-ai/Yi-34B-Chat language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/01-ai/Yi-34B-Chat <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yi-34B-Chat-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/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q2_K.gguf) | Q2_K | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.IQ3_XS.gguf) | IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q3_K_S.gguf) | Q3_K_S | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.IQ3_M.gguf) | IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q3_K_L.gguf) | Q3_K_L | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.IQ4_XS.gguf) | IQ4_XS | 18.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q5_K_S.gguf) | Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q5_K_M.gguf) | Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q6_K.gguf) | Q6_K | 28.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-Chat-GGUF/resolve/main/Yi-34B-Chat.Q8_0.gguf) | Q8_0 | 36.6 | 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 -->

kwoncho/losscut_news_pre2021_2

kwoncho

transformers

text-classification

Entry not found

legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF

legraphista

gguf

text-generation

--- base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 datasets: - openbmb/UltraFeedback inference: false language: - en library_name: gguf license: apache-2.0 pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - quantization - imat - imatrix - static - 16bit - 8bit - 6bit - 5bit - 4bit - 3bit - 2bit - 1bit --- # Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF _Llama.cpp imatrix quantization of UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3_ Original Model: [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3) Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp [b3267](https://github.com/ggerganov/llama.cpp/releases/tag/b3267) IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) - [Files](#files) - [IMatrix](#imatrix) - [Common Quants](#common-quants) - [All Quants](#all-quants) - [Downloading using huggingface-cli](#downloading-using-huggingface-cli) - [Inference](#inference) - [Simple chat template](#simple-chat-template) - [Llama.cpp](#llama-cpp) - [FAQ](#faq) - [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere) - [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf) --- ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants | Filename | Quant type | File Size | Status | Uses IMatrix | Is Split | | -------- | ---------- | --------- | ------ | ------------ | -------- | | [Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf) | Q2_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No ### All Quants | Filename | Quant type | File Size | Status | Uses IMatrix | Is Split | | -------- | ---------- | --------- | ------ | ------------ | -------- | | [Gemma-2-9B-It-SPPO-Iter3.BF16.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.BF16.gguf) | BF16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.FP16.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.FP16.gguf) | F16 | 18.49GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf) | Q8_0 | 9.83GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q6_K.gguf) | Q6_K | 7.59GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q5_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q5_K.gguf) | Q5_K | 6.65GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q5_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q5_K_S.gguf) | Q5_K_S | 6.48GB | ✅ Available | ⚪ Static | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K.gguf) | Q4_K | 5.76GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q4_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q4_K_S.gguf) | Q4_K_S | 5.48GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ4_NL.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ4_NL.gguf) | IQ4_NL | 5.44GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ4_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ4_XS.gguf) | IQ4_XS | 5.18GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K.gguf) | Q3_K | 4.76GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q3_K_L.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K_L.gguf) | Q3_K_L | 5.13GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q3_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q3_K_S.gguf) | Q3_K_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ3_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_M.gguf) | IQ3_M | 4.49GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ3_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_S.gguf) | IQ3_S | 4.34GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ3_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_XS.gguf) | IQ3_XS | 4.14GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ3_XXS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ3_XXS.gguf) | IQ3_XXS | 3.80GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K.gguf) | Q2_K | 3.81GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.Q2_K_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.Q2_K_S.gguf) | Q2_K_S | 3.55GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ2_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_M.gguf) | IQ2_M | 3.43GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ2_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_S.gguf) | IQ2_S | 3.21GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ2_XS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_XS.gguf) | IQ2_XS | 3.07GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ2_XXS.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ2_XXS.gguf) | IQ2_XXS | 2.82GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ1_M.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ1_M.gguf) | IQ1_M | 2.55GB | ✅ Available | 🟢 IMatrix | 📦 No | [Gemma-2-9B-It-SPPO-Iter3.IQ1_S.gguf](https://huggingface.co/legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF/blob/main/Gemma-2-9B-It-SPPO-Iter3.IQ1_S.gguf) | IQ1_S | 2.38GB | ✅ Available | 🟢 IMatrix | 📦 No ## Downloading using huggingface-cli If you do not have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Download the specific file you want: ``` huggingface-cli download legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF --include "Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf" --local-dir ./ ``` If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/Gemma-2-9B-It-SPPO-Iter3-IMat-GGUF --include "Gemma-2-9B-It-SPPO-Iter3.Q8_0/*" --local-dir ./ # see FAQ for merging GGUF's ``` --- ## Inference ### Simple chat template ``` <bos><start_of_turn>user {user_prompt}<end_of_turn> <start_of_turn>model {assistant_response}<end_of_turn> <start_of_turn>user {next_user_prompt}<end_of_turn> ``` ### Llama.cpp ``` llama.cpp/main -m Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf --color -i -p "prompt here (according to the chat template)" ``` --- ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `Gemma-2-9B-It-SPPO-Iter3.Q8_0`) 3. Run `gguf-split --merge Gemma-2-9B-It-SPPO-Iter3.Q8_0/Gemma-2-9B-It-SPPO-Iter3.Q8_0-00001-of-XXXXX.gguf Gemma-2-9B-It-SPPO-Iter3.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!

mixedbread-ai/mxbai-rerank-large-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-large-v1 This is the largest 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-large-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-large-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 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

Efficient-Large-Model/VILA1.5-3b

Efficient-Large-Model

transformers

text-generation

--- license: cc-by-nc-4.0 library_name: transformers pipeline_tag: text-generation tags: - VILA - VLM --- # VILA Model Card ## Model details **Model type:** VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge. **Model date:** VILA1.5-3b was trained in May 2024. **Paper or resources for more information:** https://github.com/Efficient-Large-Model/VILA ``` @misc{lin2023vila, title={VILA: On Pre-training for Visual Language Models}, author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han}, year={2023}, eprint={2312.07533}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## License - The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file. - The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en). - The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms: - [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA - [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI - [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training. **Where to send questions or comments about the model:** https://github.com/Efficient-Large-Model/VILA/issues ## Intended use **Primary intended uses:** The primary use of VILA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

NbAiLab/nb-bert-base-ner

NbAiLab

transformers

token-classification

--- language: no license: cc-by-4.0 tags: - norwegian - bert - ner thumbnail: nblogo_3.png pipeline_tag: token-classification datasets: - norne inference: parameters: aggregation_strategy: "first" widget: - text: Trond Giske har bekreftet på spørsmål fra Adresseavisen at Hansen leide et rom i hans leilighet i Trondheim. --- **Release 1.0** (November 17, 2021) # nb-bert-base-ner ## Description NB-Bert base model fine-tuned on the Named Entity Recognition task using the [NorNE dataset](https://huggingface.co/datasets/NbAiLab/norne). ## Usage ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-base-ner") model = AutoModelForTokenClassification.from_pretrained("NbAiLab/nb-bert-base-ner") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Jeg heter Kjell og bor i Oslo." ner_results = nlp(example) print(ner_results) ```

facebook/rag-token-base

facebook

transformers

--- language: en license: apache-2.0 datasets: - wiki_dpr thumbnail: https://huggingface.co/front/thumbnails/facebook.png --- ## RAG This is a non-finetuned version of the RAG-Token model of the the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/pdf/2005.11401.pdf) by Patrick Lewis, Ethan Perez, Aleksandara Piktus et al. Rag consits of a *question encoder*, *retriever* and a *generator*. The retriever should be a `RagRetriever` instance. The *question encoder* can be any model that can be loaded with `AutoModel` and the *generator* can be any model that can be loaded with `AutoModelForSeq2SeqLM`. This model is a non-finetuned RAG-Token model and was created as follows: ```python from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration, AutoTokenizer model = RagTokenForGeneration.from_pretrained_question_encoder_generator("facebook/dpr-question_encoder-single-nq-base", "facebook/bart-large") question_encoder_tokenizer = AutoTokenizer.from_pretrained("facebook/dpr-question_encoder-single-nq-base") generator_tokenizer = AutoTokenizer.from_pretrained("facebook/bart-large") tokenizer = RagTokenizer(question_encoder_tokenizer, generator_tokenizer) model.config.use_dummy_dataset = True model.config.index_name = "exact" retriever = RagRetriever(model.config, question_encoder_tokenizer, generator_tokenizer) model.save_pretrained("./") tokenizer.save_pretrained("./") retriever.save_pretrained("./") ``` Note that the model is *uncased* so that all capital input letters are converted to lower-case. ## Usage: *Note*: the model uses the *dummy* retriever as a default. Better results are obtained by using the full retriever, by setting `config.index_name="legacy"` and `config.use_dummy_dataset=False`. The model can be fine-tuned as follows: ```python from transformers import RagTokenizer, RagRetriever, RagTokenForGeneration tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base") retriever = RagRetriever.from_pretrained("facebook/rag-token-base") model = RagTokenForGeneration.from_pretrained("facebook/rag-token-base", retriever=retriever) input_dict = tokenizer.prepare_seq2seq_batch("who holds the record in 100m freestyle", "michael phelps", return_tensors="pt") outputs = model(input_dict["input_ids"], labels=input_dict["labels"]) loss = outputs.loss # train on loss ```

microsoft/codebert-base-mlm

microsoft

transformers

fill-mask

## CodeBERT-base-mlm Pretrained weights for [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://arxiv.org/abs/2002.08155). ### Training Data The model is trained on the code corpus of [CodeSearchNet](https://github.com/github/CodeSearchNet) ### Training Objective This model is initialized with Roberta-base and trained with a simple MLM (Masked Language Model) objective. ### Usage ```python from transformers import RobertaTokenizer, RobertaForMaskedLM, pipeline model = RobertaForMaskedLM.from_pretrained('microsoft/codebert-base-mlm') tokenizer = RobertaTokenizer.from_pretrained('microsoft/codebert-base-mlm') code_example = "if (x is not None) <mask> (x>1)" fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer) outputs = fill_mask(code_example) print(outputs) ``` Expected results: ``` {'sequence': '<s> if (x is not None) and (x>1)</s>', 'score': 0.6049249172210693, 'token': 8} {'sequence': '<s> if (x is not None) or (x>1)</s>', 'score': 0.30680200457572937, 'token': 50} {'sequence': '<s> if (x is not None) if (x>1)</s>', 'score': 0.02133703976869583, 'token': 114} {'sequence': '<s> if (x is not None) then (x>1)</s>', 'score': 0.018607674166560173, 'token': 172} {'sequence': '<s> if (x is not None) AND (x>1)</s>', 'score': 0.007619690150022507, 'token': 4248} ``` ### Reference 1. [Bimodal CodeBERT trained with MLM+RTD objective](https://huggingface.co/microsoft/codebert-base) (suitable for code search and document generation) 2. 🤗 [Hugging Face's CodeBERTa](https://huggingface.co/huggingface/CodeBERTa-small-v1) (small size, 6 layers) ### Citation ```bibtex @misc{feng2020codebert, title={CodeBERT: A Pre-Trained Model for Programming and Natural Languages}, author={Zhangyin Feng and Daya Guo and Duyu Tang and Nan Duan and Xiaocheng Feng and Ming Gong and Linjun Shou and Bing Qin and Ting Liu and Daxin Jiang and Ming Zhou}, year={2020}, eprint={2002.08155}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

internlm/internlm2-chat-7b

internlm

transformers

text-generation

--- pipeline_tag: text-generation license: other --- # InternLM <div align="center"> <img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/> <div>&nbsp;</div> <div align="center"> <b><font size="5">InternLM</font></b> ^{<a href="https://internlm.intern-ai.org.cn/"> <i><font size="4">HOT</font></i> </a>} <div>&nbsp;</div> </div> [](https://github.com/internLM/OpenCompass/) [💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) • [📜Technical Report](https://arxiv.org/abs/2403.17297) </div> <p align="center"> 👋 join us on <a href="https://discord.gg/xa29JuW87d" target="_blank">Discord</a> and <a href="https://github.com/InternLM/InternLM/assets/25839884/a6aad896-7232-4220-ac84-9e070c2633ce" target="_blank">WeChat</a> </p> ## Introduction InternLM2 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics: - **200K Context window**: Nearly perfect at finding needles in the haystack with 200K-long context, with leading performance on long-context tasks like LongBench and L-Eval. Try it with [LMDeploy](https://github.com/InternLM/lmdeploy) for 200K-context inference. - **Outstanding comprehensive performance**: Significantly better than the last generation in all dimensions, especially in reasoning, math, code, chat experience, instruction following, and creative writing, with leading performance among open-source models in similar sizes. In some evaluations, InternLM2-Chat-20B may match or even surpass ChatGPT (GPT-3.5). - **Code interpreter & Data analysis**: With code interpreter, InternLM2-Chat-20B obtains compatible performance with GPT-4 on GSM8K and MATH. InternLM2-Chat also provides data analysis capability. - **Stronger tool use**: Based on better tool utilization-related capabilities in instruction following, tool selection and reflection, InternLM2 can support more kinds of agents and multi-step tool calling for complex tasks. See [examples](https://github.com/InternLM/lagent). ## InternLM2-Chat-7B ### Performance Evaluation We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn/leaderboard-llm) for more evaluation results. | Dataset\Models | InternLM2-7B | InternLM2-Chat-7B | InternLM2-20B | InternLM2-Chat-20B | ChatGPT | GPT-4 | | --- | --- | --- | --- | --- | --- | --- | | MMLU | 65.8 | 63.7 | 67.7 | 66.5 | 69.1 | 83.0 | | AGIEval | 49.9 | 47.2 | 53.0 | 50.3 | 39.9 | 55.1 | | BBH | 65.0 | 61.2 | 72.1 | 68.3 | 70.1 | 86.7 | | GSM8K | 70.8 | 70.7 | 76.1 | 79.6 | 78.2 | 91.4 | | MATH | 20.2 | 23.0 | 25.5 | 31.9 | 28.0 | 45.8 | | HumanEval | 43.3 | 59.8 | 48.8 | 67.1 | 73.2 | 74.4 | | MBPP(Sanitized) | 51.8 | 51.4 | 63.0 | 65.8 | 78.9 | 79.0 | - The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with *, which means come from the original papers), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/). - The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/). **Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information. ### Import from Transformers To load the InternLM2 7B Chat model using Transformers, use the following code: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True) # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and cause OOM Error. model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "hello", history=[]) print(response) # Hello! How can I help you today? response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history) print(response) ``` The responses can be streamed using `stream_chat`: ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "internlm/internlm2-chat-7b" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "Hello", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## Deployment ### LMDeploy LMDeploy is a toolkit for compressing, deploying, and serving LLM, developed by the MMRazor and MMDeploy teams. ```bash pip install lmdeploy ``` You can run batch inference locally with the following python code: ```python import lmdeploy pipe = lmdeploy.pipeline("internlm/internlm2-chat-7b") response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response) ``` Or you can launch an OpenAI compatible server with the following command: ```bash lmdeploy serve api_server internlm/internlm2-chat-7b --model-name internlm2-chat-7b --server-port 23333 ``` Then you can send a chat request to the server: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Introduce deep learning to me."} ] }' ``` Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM Launch OpenAI compatible server with `vLLM>=0.3.2`: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --served-model-name internlm2-chat-7b --trust-remote-code ``` Then you can send a chat request to the server: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Introduce deep learning to me."} ] }' ``` Find more details in the [vLLM documentation](https://docs.vllm.ai/en/latest/index.html) ## Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <internlm@pjlab.org.cn>. ## Citation ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 简介 InternLM2 ，即书生·浦语大模型第二代，开源了面向实用场景的70亿参数基础模型与对话模型 （InternLM2-Chat-7B）。模型具有以下特点： - 有效支持20万字超长上下文：模型在20万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。 可以通过 [LMDeploy](https://github.com/InternLM/lmdeploy) 尝试20万字超长上下文推理。 - 综合性能全面提升：各能力维度相比上一代模型全面进步，在推理、数学、代码、对话体验、指令遵循和创意写作等方面的能力提升尤为显著，综合性能达到同量级开源模型的领先水平，在重点能力评测上 InternLM2-Chat-20B 能比肩甚至超越 ChatGPT （GPT-3.5）。 - 代码解释器与数据分析：在配合代码解释器（code-interpreter）的条件下，InternLM2-Chat-20B 在 GSM8K 和 MATH 上可以达到和 GPT-4 相仿的水平。基于在数理和工具方面强大的基础能力，InternLM2-Chat 提供了实用的数据分析能力。 - 工具调用能力整体升级：基于更强和更具有泛化性的指令理解、工具筛选与结果反思等能力，新版模型可以更可靠地支持复杂智能体的搭建，支持对工具进行有效的多轮调用，完成较复杂的任务。可以查看更多[样例](https://github.com/InternLM/lagent)。 ## InternLM2-Chat-7B ### 性能评测 我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn/leaderboard-llm)获取更多的评测结果。 | 评测集 | InternLM2-7B | InternLM2-Chat-7B | InternLM2-20B | InternLM2-Chat-20B | ChatGPT | GPT-4 | | --- | --- | --- | --- | --- | --- | --- | | MMLU | 65.8 | 63.7 | 67.7 | 66.5 | 69.1 | 83.0 | | AGIEval | 49.9 | 47.2 | 53.0 | 50.3 | 39.9 | 55.1 | | BBH | 65.0 | 61.2 | 72.1 | 68.3 | 70.1 | 86.7 | | GSM8K | 70.8 | 70.7 | 76.1 | 79.6 | 78.2 | 91.4 | | MATH | 20.2 | 23.0 | 25.5 | 31.9 | 28.0 | 45.8 | | HumanEval | 43.3 | 59.8 | 48.8 | 67.1 | 73.2 | 74.4 | | MBPP(Sanitized) | 51.8 | 51.4 | 63.0 | 65.8 | 78.9 | 79.0 | - 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得（部分数据标注`*`代表数据来自原始论文），具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。 - 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。 **局限性：** 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。 ### 通过 Transformers 加载 通过以下的代码加载 InternLM2 7B Chat 模型 ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True) # `torch_dtype=torch.float16` 可以令模型以 float16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足 model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", torch_dtype=torch.float16, trust_remote_code=True).cuda() model = model.eval() response, history = model.chat(tokenizer, "你好", history=[]) print(response) # 你好！有什么我可以帮助你的吗？ response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history) print(response) ``` 如果想进行流式生成，则可以使用 `stream_chat` 接口： ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "internlm/internlm2-chat-7b" model = AutoModelForCausalLM.from_pretrained(model_path, torch_dype=torch.float16, trust_remote_code=True).cuda() tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) model = model.eval() length = 0 for response, history in model.stream_chat(tokenizer, "你好", history=[]): print(response[length:], flush=True, end="") length = len(response) ``` ## 部署 ### LMDeploy LMDeploy 由 MMDeploy 和 MMRazor 团队联合开发，是涵盖了 LLM 任务的全套轻量化、部署和服务解决方案。 ```bash pip install lmdeploy ``` 你可以使用以下 python 代码进行本地批量推理: ```python import lmdeploy pipe = lmdeploy.pipeline("internlm/internlm2-chat-7b") response = pipe(["Hi, pls intro yourself", "Shanghai is"]) print(response) ``` 或者你可以使用以下命令启动兼容 OpenAI API 的服务: ```bash lmdeploy serve api_server internlm/internlm2-chat-7b --server-port 23333 ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:23333/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "你是个友善的AI助手。"}, {"role": "user", "content": "介绍一下深度学习。"} ] }' ``` 更多信息请查看 [LMDeploy 文档](https://lmdeploy.readthedocs.io/en/latest/) ### vLLM 使用`vLLM>=0.3.2`启动兼容 OpenAI API 的服务: ```bash pip install vllm ``` ```bash python -m vllm.entrypoints.openai.api_server --model internlm/internlm2-chat-7b --trust-remote-code ``` 然后你可以向服务端发起一个聊天请求: ```bash curl http://localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "internlm2-chat-7b", "messages": [ {"role": "system", "content": "你是个友善的AI助手。"}, {"role": "user", "content": "介绍一下深度学习。"} ] }' ``` 更多信息请查看 [vLLM 文档](https://docs.vllm.ai/en/latest/index.html) ## 开源许可证 本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（[申请表](https://wj.qq.com/s2/12725412/f7c1/)）。其他问题与合作请联系 <internlm@pjlab.org.cn>。 ## 引用 ``` @misc{cai2024internlm2, title={InternLM2 Technical Report}, author={Zheng Cai and Maosong Cao and Haojiong Chen and Kai Chen and Keyu Chen and Xin Chen and Xun Chen and Zehui Chen and Zhi Chen and Pei Chu and Xiaoyi Dong and Haodong Duan and Qi Fan and Zhaoye Fei and Yang Gao and Jiaye Ge and Chenya Gu and Yuzhe Gu and Tao Gui and Aijia Guo and Qipeng Guo and Conghui He and Yingfan Hu and Ting Huang and Tao Jiang and Penglong Jiao and Zhenjiang Jin and Zhikai Lei and Jiaxing Li and Jingwen Li and Linyang Li and Shuaibin Li and Wei Li and Yining Li and Hongwei Liu and Jiangning Liu and Jiawei Hong and Kaiwen Liu and Kuikun Liu and Xiaoran Liu and Chengqi Lv and Haijun Lv and Kai Lv and Li Ma and Runyuan Ma and Zerun Ma and Wenchang Ning and Linke Ouyang and Jiantao Qiu and Yuan Qu and Fukai Shang and Yunfan Shao and Demin Song and Zifan Song and Zhihao Sui and Peng Sun and Yu Sun and Huanze Tang and Bin Wang and Guoteng Wang and Jiaqi Wang and Jiayu Wang and Rui Wang and Yudong Wang and Ziyi Wang and Xingjian Wei and Qizhen Weng and Fan Wu and Yingtong Xiong and Chao Xu and Ruiliang Xu and Hang Yan and Yirong Yan and Xiaogui Yang and Haochen Ye and Huaiyuan Ying and Jia Yu and Jing Yu and Yuhang Zang and Chuyu Zhang and Li Zhang and Pan Zhang and Peng Zhang and Ruijie Zhang and Shuo Zhang and Songyang Zhang and Wenjian Zhang and Wenwei Zhang and Xingcheng Zhang and Xinyue Zhang and Hui Zhao and Qian Zhao and Xiaomeng Zhao and Fengzhe Zhou and Zaida Zhou and Jingming Zhuo and Yicheng Zou and Xipeng Qiu and Yu Qiao and Dahua Lin}, year={2024}, eprint={2403.17297}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

stabilityai/stable-diffusion-x4-upscaler

stabilityai

diffusers

--- license: openrail++ tags: - stable-diffusion inference: false --- # Stable Diffusion x4 upscaler model card This model card focuses on the model associated with the Stable Diffusion Upscaler, available [here](https://github.com/Stability-AI/stablediffusion). This model is 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).  - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `x4-upscaler-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler/resolve/main/x4-upscaler-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler#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 diffusers transformers accelerate scipy safetensors ``` ```python import requests from PIL import Image from io import BytesIO from diffusers import StableDiffusionUpscalePipeline import torch # load model and scheduler model_id = "stabilityai/stable-diffusion-x4-upscaler" pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipeline = pipeline.to("cuda") # let's download an image url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale/low_res_cat.png" response = requests.get(url) low_res_img = Image.open(BytesIO(response.content)).convert("RGB") low_res_img = low_res_img.resize((128, 128)) prompt = "a white cat" upscaled_image = pipeline(prompt=prompt, image=low_res_img).images[0] upscaled_image.save("upsampled_cat.png") ``` **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).*

fxmarty/tiny-random-working-LongT5Model

fxmarty

transformers

feature-extraction

--- license: mit ---

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.

bespin-global/klue-sroberta-base-continue-learning-by-mnr

bespin-global

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - klue language: - ko license: cc-by-4.0 --- # bespin-global/klue-sroberta-base-continue-learning-by-mnr 해당 모델은 KLUE/NLI, KLUE/STS 데이터셋을 활용하였으며, sentence-transformers의 공식 문서 내 소개된 [continue-learning](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training/sts/training_stsbenchmark_continue_training.py) 방법을 통해 아래와 같이 학습되었습니다. 1. NLI 데이터셋을 통해 nagative sampling 후, MultipleNegativeRankingLoss를 활용하여 1차 NLI training 수행 2. 1에서 학습완료 된 모델에 STS 데이터셋을 통해, CosineSimilarityLoss를 활용하여 2차 STS training 수행 학습에 관한 자세한 내용은 [Blog](https://velog.io/@jaehyeong/Basic-NLP-sentence-transformers-%EB%9D%BC%EC%9D%B4%EB%B8%8C%EB%9F%AC%EB%A6%AC%EB%A5%BC-%ED%99%9C%EC%9A%A9%ED%95%9C-SBERT-%ED%95%99%EC%8A%B5-%EB%B0%A9%EB%B2%95#225-continue-learning-by-sts)와 [Colab 실습 코드](https://colab.research.google.com/drive/1uDt3o_Nv2cTiVbIAIUkst_eOSD37Wkmf)를 참고해주세요. --- 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. <!--- Describe your model here --> ## 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("bespin-global/klue-sroberta-base-continue-learning-by-mnr") 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("bespin-global/klue-sroberta-base-continue-learning-by-mnr") model = AutoModel.from_pretrained("bespin-global/klue-sroberta-base-continue-learning-by-mnr") # 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 <!--- Describe how your model was evaluated --> **EmbeddingSimilarityEvaluator: Evaluating the model on sts-test dataset:** - Cosine-Similarity : - Pearson: 0.8901 Spearman: 0.8893 - Manhattan-Distance: - Pearson: 0.8867 Spearman: 0.8818 - Euclidean-Distance: - Pearson: 0.8875 Spearman: 0.8827 - Dot-Product-Similarity: - Pearson: 0.8786 Spearman: 0.8735 - Average : 0.8892573547643868 ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 329 with parameters: ``` {'batch_size': 32, '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": 32, "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": 132, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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 <!--- Describe where people can find more information --> [JaeHyeong AN](https://huggingface.co/Copycats) at [Bespin Global](https://www.bespinglobal.com/)

mradermacher/Yi-34B-200K-GGUF

mradermacher

transformers

--- base_model: 01-ai/Yi-34B-200K language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/01-ai/Yi-34B-200K <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yi-34B-200K-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/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q2_K.gguf) | Q2_K | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.IQ3_XS.gguf) | IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q3_K_S.gguf) | Q3_K_S | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.IQ3_M.gguf) | IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q3_K_L.gguf) | Q3_K_L | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.IQ4_XS.gguf) | IQ4_XS | 18.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q5_K_S.gguf) | Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q5_K_M.gguf) | Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q6_K.gguf) | Q6_K | 28.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-GGUF/resolve/main/Yi-34B-200K.Q8_0.gguf) | Q8_0 | 36.6 | 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/Yi-34B-200K-AEZAKMI-v2-GGUF

mradermacher

transformers

--- base_model: adamo1139/Yi-34B-200K-AEZAKMI-v2 datasets: - adamo1139/AEZAKMI_v2 language: - en library_name: transformers license: apache-2.0 license_link: LICENSE license_name: yi-license quantized_by: mradermacher tags: - llm - fine-tune - yi --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-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/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q2_K.gguf) | Q2_K | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.IQ3_XS.gguf) | IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q3_K_S.gguf) | Q3_K_S | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.IQ3_M.gguf) | IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q3_K_L.gguf) | Q3_K_L | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.IQ4_XS.gguf) | IQ4_XS | 18.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q5_K_S.gguf) | Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q5_K_M.gguf) | Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q6_K.gguf) | Q6_K | 28.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-AEZAKMI-v2-GGUF/resolve/main/Yi-34B-200K-AEZAKMI-v2.Q8_0.gguf) | Q8_0 | 36.6 | 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 -->

nickmuchi/yolos-small-finetuned-license-plate-detection

nickmuchi

transformers

object-detection

--- language: - en tags: - object-detection - license-plate-detection - vehicle-detection widget: - src: https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ example_title: "Skoda 1" - src: https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5 example_title: "Skoda 2" metrics: - average precision - recall - IOU pipeline_tag: object-detection --- # YOLOS (small-sized) model This model is a fine-tuned version of [hustvl/yolos-small](https://huggingface.co/hustvl/yolos-small) on the [licesne-plate-recognition](https://app.roboflow.com/objectdetection-jhgr1/license-plates-recognition/2) dataset from Roboflow which contains 5200 images in the training set and 380 in the validation set. The original YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). ## Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models. ### How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosFeatureExtractor.from_pretrained('nickmuchi/yolos-small-finetuned-license-plate-detection') model = YolosForObjectDetection.from_pretrained('nickmuchi/yolos-small-finetuned-license-plate-detection') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(**inputs) # model predicts bounding boxes and corresponding face mask detection classes logits = outputs.logits bboxes = outputs.pred_boxes ``` Currently, both the feature extractor and model support PyTorch. ## Training data The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### Training This model was fine-tuned for 200 epochs on the [licesne-plate-recognition](https://app.roboflow.com/objectdetection-jhgr1/license-plates-recognition/2). ## Evaluation results This model achieves an AP (average precision) of **49.0**. Accumulating evaluation results... IoU metric: bbox Metrics | Metric Parameter | Location | Dets | Value | ---------------- | --------------------- | ------------| ------------- | ----- | Average Precision | (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.490 | Average Precision | (AP) @[ IoU=0.50 | area= all | maxDets=100 ] | 0.792 | Average Precision | (AP) @[ IoU=0.75 | area= all | maxDets=100 ] | 0.585 | Average Precision | (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.167 | Average Precision | (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.460 | Average Precision | (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.824 | Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] | 0.447 | Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] | 0.671 | Average Recall | (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] | 0.676 | Average Recall | (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] | 0.278 | Average Recall | (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] | 0.641 | Average Recall | (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] | 0.890 |

Yntec/DreamPhotoGASM

Yntec

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Photorealistic - Realism - Girls - epinikion - text-to-image - stable-diffusion - stable-diffusion-diffusers - diffusers --- # DreamPhoto Gorgeous Advanced Sexy Model Samples and prompts:  (Click for larger) Top left: Woman Amazon, Brazilian 1girl "amazon_woman face" wearing bikini and 1man "male face" wearing swim shorts, Scenario, official art, beautiful and aesthetic, colorful, realistic light, at night, intricate details, intricate details, hyperdetailed, dramatic light, cinematic, silhouette, smiling couple on a deck of a cruise ship, at night, looking at viewer Top right: in rain, red shiny sports jersey, flirty, long sleeves,oversized soaking clothes, Freckles, long hair, ginger hair, white background, grey eyes, simple background, 1girl, full body, Bottom left: Cute girl, pretty girl. features, body. Wide hips. Pale. Small, mousey nose. Closed mouth, small smile. Messy blonde hair, short. Wearing a teal two piece bikini, triangl brand. Short hair, pixie haircut. On sandy, rocky beach. Beautiful face, eyes. Blushing, sweaty, shy. Sunset. Natural lighting. 4k, high quality picture, sharp image. Pixie haircut. Bikini. Bottom right: Dramatic cinematic 1980 movie still, handsome VALENTINE MAN kissing pretty woman with cleavage, classroom, school Uniforms, blackboard. Pinup. He wears a backpack, bokeh epiCPhotoGASM by epinikion merged with artistic models to make your dreams come true! Original page: https://civitai.com/models/132632?modelVersionId=145885

nielsr/layoutlmv2-finetuned-funsd

nielsr

transformers

token-classification

--- tags: - generated_from_trainer datasets: - funsd model_index: - name: layoutlmv2-finetuned-funsd results: - task: name: Token Classification type: token-classification dataset: name: funsd type: funsd args: funsd base_model: microsoft/layoutlmv2-base-uncased --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # layoutlmv2-finetuned-funsd This model is a fine-tuned version of [microsoft/layoutlmv2-base-uncased](https://huggingface.co/microsoft/layoutlmv2-base-uncased) on the funsd dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1000 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.9.0.dev0 - Pytorch 1.8.0+cu101 - Datasets 1.9.0 - Tokenizers 0.10.3

stablediffusionapi/explicit-freedom-nsfw-wai

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic - not-for-all-audiences pinned: true --- # Explicit Freedom - NSFW Waifu API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "explicit-freedom-nsfw-wai" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/explicit-freedom-nsfw-wai) Model link: [View model](https://stablediffusionapi.com/models/explicit-freedom-nsfw-wai) Credits: [View credits](https://civitai.com/?query=Explicit%20Freedom%20-%20NSFW%20Waifu) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "explicit-freedom-nsfw-wai", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

csebuetnlp/banglat5_banglaparaphrase

csebuetnlp

transformers

text2text-generation

--- language: - bn licenses: - cc-by-nc-sa-4.0 --- # banglat5_banglaparaphrase This repository contains the pretrained checkpoint of the model **BanglaT5** finetuned on [BanglaParaphrase](https://huggingface.co/datasets/csebuetnlp/BanglaParaphrase) dataset. This is a sequence to sequence transformer model pretrained with the ["Span Corruption"]() objective. Finetuned models using this checkpoint achieve competitive results on the dataset. For finetuning and inference, refer to the scripts in the official GitHub repository of [BanglaNLG](https://github.com/csebuetnlp/BanglaNLG). **Note**: This model was pretrained using a specific normalization pipeline available [here](https://github.com/csebuetnlp/normalizer). All finetuning scripts in the official GitHub repository use this normalization by default. If you need to adapt the pretrained model for a different task make sure the text units are normalized using this pipeline before tokenizing to get best results. A basic example is given below: ## Using this model in `transformers` ```python from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from normalizer import normalize # pip install git+https://github.com/csebuetnlp/normalizer model = AutoModelForSeq2SeqLM.from_pretrained("csebuetnlp/banglat5_banglaparaphrase") tokenizer = AutoTokenizer.from_pretrained("csebuetnlp/banglat5_banglaparaphrase", use_fast=False) input_sentence = "" input_ids = tokenizer(normalize(input_sentence), return_tensors="pt").input_ids generated_tokens = model.generate(input_ids) decoded_tokens = tokenizer.batch_decode(generated_tokens)[0] print(decoded_tokens) ``` ## Benchmarks * Supervised fine-tuning | Test Set | Model | sacreBLEU | ROUGE-L | PINC | BERTScore | BERT-iBLEU | | -------- | ----- | --------- | ------- | ---- | --------- | ---------- | | [BanglaParaphrase](https://huggingface.co/datasets/csebuetnlp/BanglaParaphrase) | [BanglaT5](https://huggingface.co/csebuetnlp/banglat5)<br>[IndicBART](https://huggingface.co/ai4bharat/IndicBART)<br>[IndicBARTSS](https://huggingface.co/ai4bharat/IndicBARTSS)| 32.8<br>5.60<br>4.90 | 63.58<br>35.61<br>33.66 | 74.40<br>80.26<br>82.10 | 94.80<br>91.50<br>91.10 | 92.18<br>91.16<br>90.95 | | [IndicParaphrase](https://huggingface.co/datasets/ai4bharat/IndicParaphrase) |BanglaT5<br>IndicBART<br>IndicBARTSS| 11.0<br>12.0<br>10.7| 19.99<br>21.58<br>20.59| 74.50<br>76.83<br>77.60| 94.80<br>93.30<br>93.10 | 87.738<br>90.65<br>90.54| The dataset can be found in the link below: * **[BanglaParaphrase](https://huggingface.co/datasets/csebuetnlp/BanglaParaphrase)** ## Citation If you use this model, please cite the following paper: ``` @article{akil2022banglaparaphrase, title={BanglaParaphrase: A High-Quality Bangla Paraphrase Dataset}, author={Akil, Ajwad and Sultana, Najrin and Bhattacharjee, Abhik and Shahriyar, Rifat}, journal={arXiv preprint arXiv:2210.05109}, year={2022} } ```

stablediffusionapi/realistic-stock-photo

stablediffusionapi

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Realistic Stock Photo API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "realistic-stock-photo" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/realistic-stock-photo) Model link: [View model](https://stablediffusionapi.com/models/realistic-stock-photo) Credits: [View credits](https://civitai.com/?query=Realistic%20Stock%20Photo) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v4/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "realistic-stock-photo", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

Salesforce/codegen-350M-mono

Salesforce

transformers

text-generation

--- license: bsd-3-clause --- # CodeGen (CodeGen-Mono 350M) ## Model description CodeGen is a family of autoregressive language models for **program synthesis** from the paper: [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in [this repository](https://github.com/salesforce/CodeGen), under 3 pre-training data variants (`NL`, `Multi`, `Mono`) and 4 model size variants (`350M`, `2B`, `6B`, `16B`). The checkpoint included in this repository is denoted as **CodeGen-Mono 350M** in the paper, where "Mono" means the model is initialized with *CodeGen-Multi 350M* and further pre-trained on a Python programming language dataset, and "350M" refers to the number of trainable parameters. ## Training data This checkpoint (CodeGen-Mono 350M) was firstly initialized with *CodeGen-Multi 350M*, and then pre-trained on BigPython dataset. The data consists of 71.7B tokens of Python programming language. See Section 2.1 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Training procedure CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Evaluation results We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the [paper](https://arxiv.org/abs/2203.13474) for more details. ## Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well. ## How to use This model can be easily loaded using the `AutoModelForCausalLM` functionality: ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-350M-mono") model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-350M-mono") text = "def hello_world():" input_ids = tokenizer(text, return_tensors="pt").input_ids generated_ids = model.generate(input_ids, max_length=128) print(tokenizer.decode(generated_ids[0], skip_special_tokens=True)) ``` ## BibTeX entry and citation info ```bibtex @article{Nijkamp2022ACP, title={A Conversational Paradigm for Program Synthesis}, author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming}, journal={arXiv preprint}, year={2022} } ```

mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF

mradermacher

transformers

--- base_model: SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16 <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-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/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | | | [GGUF](https://huggingface.co/mradermacher/Tenebra_30B_Alpha01_FP16-i1-GGUF/resolve/main/Tenebra_30B_Alpha01_FP16.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | 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 -->

daspartho/prompt-extend

daspartho

transformers

text-generation

--- license: mit tags: - generated_from_trainer model-index: - name: prompt-extend results: [] --- [](https://huggingface.co/spaces/daspartho/prompt-extend) # Prompt Extend Text generation model for generating suitable style cues given the main idea for a prompt. It is a GPT-2 model trained on [dataset](https://huggingface.co/datasets/daspartho/stable-diffusion-prompts) of stable diffusion prompts. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 128 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 3.7436 | 1.0 | 12796 | 2.5429 | | 2.3292 | 2.0 | 25592 | 2.0711 | | 1.9439 | 3.0 | 38388 | 1.8447 | | 1.7059 | 4.0 | 51184 | 1.7325 | | 1.5775 | 5.0 | 63980 | 1.7110 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cu117 - Datasets 2.7.1 - Tokenizers 0.13.2

mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-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 - llama 3 - Model stock --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V2.1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-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/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q2_K.gguf) | Q2_K | 26.5 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.IQ3_XS.gguf) | IQ3_XS | 29.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q3_K_S.gguf) | Q3_K_S | 31.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.IQ3_M.gguf) | IQ3_M | 32.0 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q3_K_L.gguf) | Q3_K_L | 37.2 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.IQ4_XS.gguf) | IQ4_XS | 38.4 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q5_K_S.gguf) | Q5_K_S | 48.8 | | | [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q5_K_M.gguf) | Q5_K_M | 50.0 | | | [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality | | [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V2.1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V2.1.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 -->

kwoncho/losscut_news_pre2022_2

kwoncho

transformers

text-classification

Entry not found

zhiqiulin/clip-flant5-xxl

zhiqiulin

transformers

text2text-generation

--- license: apache-2.0 ---

pysentimiento/robertuito-hate-speech

pysentimiento

pysentimiento

--- language: - es library_name: pysentimiento tags: - twitter - hate-speech --- # Hate Speech detection in Spanish ## robertuito-hate-speech Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/) Model trained with SemEval 2019 Task 5: HatEval (SubTask B) corpus for Hate Speech detection in Spanish. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets. It is a multi-classifier model, with the following classes: - **HS**: is it hate speech? - **TR**: is it targeted to a specific individual? - **AG**: is it aggressive? ## Results Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores | model | emotion | hate_speech | irony | sentiment | |:--------------|:--------------|:--------------|:--------------|:--------------| | robertuito | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 | | roberta | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 | | bertin | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 | | beto_uncased | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 | | beto_cased | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 | | mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 | | biGRU | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 | Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B (HS+TR+AG detection) ## Citation If you use this model in your research, please cite pysentimiento, RoBERTuito and HatEval papers: ``` % Pysentimiento @misc{perez2021pysentimiento, title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks}, author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque}, year={2021}, eprint={2106.09462}, archivePrefix={arXiv}, primaryClass={cs.CL} } % RoBERTuito paper @inproceedings{perez-etal-2022-robertuito, title = "{R}o{BERT}uito: a pre-trained language model for social media text in {S}panish", author = "P{\'e}rez, Juan Manuel and Furman, Dami{\'a}n Ariel and Alonso Alemany, Laura and Luque, Franco M.", booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference", month = jun, year = "2022", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2022.lrec-1.785", pages = "7235--7243", abstract = "Since BERT appeared, Transformer language models and transfer learning have become state-of-the-art for natural language processing tasks. Recently, some works geared towards pre-training specially-crafted models for particular domains, such as scientific papers, medical documents, user-generated texts, among others. These domain-specific models have been shown to improve performance significantly in most tasks; however, for languages other than English, such models are not widely available. In this work, we present RoBERTuito, a pre-trained language model for user-generated text in Spanish, trained on over 500 million tweets. Experiments on a benchmark of tasks involving user-generated text showed that RoBERTuito outperformed other pre-trained language models in Spanish. In addition to this, our model has some cross-lingual abilities, achieving top results for English-Spanish tasks of the Linguistic Code-Switching Evaluation benchmark (LinCE) and also competitive performance against monolingual models in English Twitter tasks. To facilitate further research, we make RoBERTuito publicly available at the HuggingFace model hub together with the dataset used to pre-train it.", } % HatEval paper @inproceedings{hateval2019semeval, title={SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter}, author={Basile, Valerio and Bosco, Cristina and Fersini, Elisabetta and Nozza, Debora and Patti, Viviana and Rangel, Francisco and Rosso, Paolo and Sanguinetti, Manuela}, booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation (SemEval-2019)}, year={2019}, publisher= {Association for Computational Linguistics} } ```

mosaicml/mpt-7b

mosaicml

transformers

text-generation

--- license: apache-2.0 tags: - Composer - MosaicML - llm-foundry - StreamingDatasets datasets: - mc4 - c4 - togethercomputer/RedPajama-Data-1T - bigcode/the-stack - allenai/s2orc inference: false --- # MPT-7B MPT-7B is a decoder-style transformer pretrained from scratch on 1T tokens of English text and code. This model was trained by [MosaicML](https://www.mosaicml.com). MPT-7B is part of the family of MosaicPretrainedTransformer (MPT) models, which use a modified transformer architecture optimized for efficient training and inference. These architectural changes include performance-optimized layer implementations and the elimination of context length limits by replacing positional embeddings with Attention with Linear Biases ([ALiBi](https://arxiv.org/abs/2108.12409)). Thanks to these modifications, MPT models can be trained with high throughput efficiency and stable convergence. MPT models can also be served efficiently with both standard HuggingFace pipelines and NVIDIA's [FasterTransformer](https://github.com/NVIDIA/FasterTransformer). This model uses the MosaicML LLM codebase, which can be found in the [llm-foundry repository](https://github.com/mosaicml/llm-foundry). It was trained by MosaicML’s NLP team on the [MosaicML platform](https://www.mosaicml.com/training) for LLM pretraining, finetuning, and inference. ### How is this model different? MPT-7B is * **Licensed for the possibility of commercial use** (unlike [LLaMA](https://arxiv.org/abs/2302.13971)). * **Trained on a large amount of data** (1T tokens like [LLaMA](https://arxiv.org/abs/2302.13971) vs. 300B for [Pythia](https://github.com/EleutherAI/pythia), 300B for [OpenLLaMA](https://github.com/openlm-research/open_llama), and 800B for [StableLM](https://github.com/Stability-AI/StableLM)). * **Prepared to handle extremely long inputs** thanks to [ALiBi](https://arxiv.org/abs/2108.12409) (we finetuned [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter) on up to 65k inputs and can handle up to 84k vs. 2k-4k for other open source models). * **Capable of fast training and inference** (via [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) and [FasterTransformer](https://github.com/NVIDIA/FasterTransformer)) * **Equipped with highly efficient open-source training code** via the [llm-foundry repository](https://github.com/mosaicml/llm-foundry) ### Models finetuned off MPT-7B: The following models are finetuned on MPT-7B: * [MPT-7B-StoryWriter-65k+](https://huggingface.co/mosaicml/mpt-7b-storywriter): a model designed to read and write fictional stories with super long context lengths. Built by finetuning MPT-7B with a context length of 65k tokens on a filtered fiction subset of the [books3 dataset](https://huggingface.co/datasets/the_pile_books3). At inference time, thanks to [ALiBi](https://arxiv.org/abs/2108.12409), MPT-7B-StoryWriter-65k+ can extrapolate even beyond 65k tokens. We demonstrate generations as long as 80k tokens on a single A100-80GB GPU in our [blogpost](www.mosaicml.com/blog/mpt-7b). * License: Apache 2.0 * [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct): a model for short-form instruction following. Built by finetuning MPT-7B on a [dataset](https://huggingface.co/datasets/mosaicml/dolly_hhrlhf) we also release, derived from the [Databricks Dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) and the [Anthropic Helpful and Harmless (HH-RLHF)](https://huggingface.co/datasets/Anthropic/hh-rlhf) datasets. * License: Apache 2.0 * [MPT-7B-Chat](https://huggingface.co/mosaicml/mpt-7b-chat): a chatbot-like model for dialogue generation. Built by finetuning MPT-7B on the [ShareGPT-Vicuna](https://huggingface.co/datasets/jeffwan/sharegpt_vicuna), [HC3](https://huggingface.co/datasets/Hello-SimpleAI/HC3), [Alpaca](https://huggingface.co/datasets/tatsu-lab/alpaca), [HH-RLHF](https://huggingface.co/datasets/Anthropic/hh-rlhf), and [Evol-Instruct](https://huggingface.co/datasets/victor123/evol_instruct_70k) datasets. * License: _CC-By-NC-SA-4.0_ ## Model Date May 5, 2023 ## Model License Apache-2.0 ## Documentation * [Blog post: Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs](https://www.mosaicml.com/blog/mpt-7b) * [Codebase (mosaicml/llm-foundry repo)](https://github.com/mosaicml/llm-foundry/) * Questions: Feel free to contact us via the [MosaicML Community Slack](https://mosaicml.me/slack)! ## How to Use This model is best used with the MosaicML [llm-foundry repository](https://github.com/mosaicml/llm-foundry) for training and finetuning. ```python import transformers model = transformers.AutoModelForCausalLM.from_pretrained( 'mosaicml/mpt-7b', trust_remote_code=True ) ``` Note: This model requires that `trust_remote_code=True` be passed to the `from_pretrained` method. This is because we use a custom `MPT` model architecture that is not yet part of the Hugging Face `transformers` package. `MPT` includes options for many training efficiency features such as [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf), [ALiBi](https://arxiv.org/abs/2108.12409), [QK LayerNorm](https://arxiv.org/abs/2010.04245), and more. To use the optimized [triton implementation](https://github.com/openai/triton) of FlashAttention, you can load the model on GPU (`cuda:0`) with `attn_impl='triton'` and with `bfloat16` precision: ```python import torch import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.attn_config['attn_impl'] = 'triton' config.init_device = 'cuda:0' # For fast initialization directly on GPU! model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, torch_dtype=torch.bfloat16, # Load model weights in bfloat16 trust_remote_code=True ) ``` Although the model was trained with a sequence length of 2048, ALiBi enables users to increase the maximum sequence length during finetuning and/or inference. For example: ```python import transformers name = 'mosaicml/mpt-7b' config = transformers.AutoConfig.from_pretrained(name, trust_remote_code=True) config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096 model = transformers.AutoModelForCausalLM.from_pretrained( name, config=config, trust_remote_code=True ) ``` This model was trained with the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. ```python from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b') ``` The model can then be used, for example, within a text-generation pipeline. Note: when running Torch modules in lower precision, it is best practice to use the [torch.autocast context manager](https://pytorch.org/docs/stable/amp.html). ```python from transformers import pipeline pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0') with torch.autocast('cuda', dtype=torch.bfloat16): print( pipe('Here is a recipe for vegan banana bread:\n', max_new_tokens=100, do_sample=True, use_cache=True)) ``` ## Model Description The architecture is a modification of a standard decoder-only transformer. The model has been modified from a standard transformer in the following ways: * It uses [FlashAttention](https://arxiv.org/pdf/2205.14135.pdf) * It uses [ALiBi (Attention with Linear Biases)](https://arxiv.org/abs/2108.12409) and does not use positional embeddings * It does not use biases | Hyperparameter | Value | |----------------|-------| |n_parameters | 6.7B | |n_layers | 32 | | n_heads | 32 | | d_model | 4096 | | vocab size | 50432 | | sequence length | 2048 | ## Training Data ### Streaming Datasets Data was formatted using the MosaicML [StreamingDataset](https://github.com/mosaicml/streaming) library to host our data in object storage and efficiently stream it to our compute cluster during training. StreamingDataset obviates the need to download the whole dataset before starting training, and allows instant resumption of training from any point in the dataset. ### Data Mix The model was trained for 1T tokens (with batch size 1760 and sequence length 2048). It was trained on the following data mix: | Data Source | Number of Tokens in Source | Proportion | Effective Number of Tokens | Epochs | |-------------|----------------------------|------------|----------------------------|--------| | mC4 3.1.0 - English | 417.99 B | 0.33 | 330 B | 0.14 | | C4 - English - SemDedup 80% | 100.42 B | 0.299 | 299 B | 2.98 | | RedPajama - CommonCrawl | 878.45 B | 0.1 | 100 B | 0.11 | | The Stack - Selected Languages | 463.78 B | 0.1 | 100 B | 0.22 | | RedPajama - Wikipedia - En | 4.87 B | 0.04 | 40 B | 8.21 | | The Stack - Markdown | 107.07 B | 0.035 | 35 B | 0.33 | | S2ORC | 48.85 B | 0.033 | 33 B | 0.68 | | RedPajama - Books | 26.02 B | 0.03 | 30B | 1.15 | | RedPajama - arXiv | 28.10 B | 0.019 | 19 B | 0.68 | | RedPajama - StackExchange | 20.54 B | 0.014 | 14 B |0.68 | Samples for each batch were selected from one of the datasets with the probability specified above. The examples were shuffled within each dataset, and each example was constructed from as many sequences from that dataset as were necessary to fill the 2048 sequence length. The data was tokenized using the [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) tokenizer. This BPE tokenizer has a number of desirable characteristics, most of which are relevant for tokenizing code: (1) It was trained on a diverse mix of data that includes code (The Pile) (2) It applies consistent space delimitation, unlike the GPT2 tokenizer which tokenizes inconsistently depending on the presence of prefix spaces (3) It contains tokens for repeated space characters, which allows superior compression of text with large amounts of repeated space characters. The model vocabulary size of 50432 was set to be a multiple of 128 (as in [MEGATRON-LM](https://arxiv.org/abs/1909.08053)), model flop utilization (MFU) increased by up to four percentage points. ### Training Configuration This model was trained on 440 A100-40GBs for about 9.5 days using the [MosaicML Platform](https://www.mosaicml.com/platform). The model was trained with sharded data parallelism using [FSDP](https://pytorch.org/docs/stable/fsdp.html) and used the [LION](https://arxiv.org/abs/2302.06675) optimizer. ## Limitations and Biases _The following language is modified from [EleutherAI's GPT-NeoX-20B](https://huggingface.co/EleutherAI/gpt-neox-20b)_ MPT-7B (Base) is **not** intended for deployment without finetuning. It should not be used for human-facing interactions without further guardrails and user consent. MPT-7B can produce factually incorrect output, and should not be relied on to produce factually accurate information. MPT-7B was trained on various public datasets. While great efforts have been taken to clean the pretraining data, it is possible that this model could generate lewd, biased or otherwise offensive outputs. ## MosaicML Platform If you're interested in [training](https://www.mosaicml.com/training) and [deploying](https://www.mosaicml.com/inference) your own MPT or LLMs on the MosaicML Platform, [sign up here](https://forms.mosaicml.com/demo?utm_source=huggingface&utm_medium=referral&utm_campaign=mpt-7b). ## Disclaimer The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please cosult an attorney before using this model for commercial purposes. ## Citation Please cite this model using the following format: ``` @online{MosaicML2023Introducing, author = {MosaicML NLP Team}, title = {Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs}, year = {2023}, url = {www.mosaicml.com/blog/mpt-7b}, note = {Accessed: 2023-05-05}, urldate = {2023-05-05} } ```

Qwen/Qwen2-0.5B

Qwen

transformers

text-generation

--- language: - en pipeline_tag: text-generation tags: - pretrained license: apache-2.0 --- # Qwen2-0.5B ## 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 0.5B Qwen2 base language 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. 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. ## 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' ``` ## Usage We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model. ## Performance The evaluation of base models mainly focuses on the model performance of natural language understanding, general question answering, coding, mathematics, scientific knowledge, reasoning, multilingual capability, etc. The datasets for evaluation include: **English Tasks**: MMLU (5-shot), MMLU-Pro (5-shot), GPQA (5shot), Theorem QA (5-shot), BBH (3-shot), HellaSwag (10-shot), Winogrande (5-shot), TruthfulQA (0-shot), ARC-C (25-shot) **Coding Tasks**: EvalPlus (0-shot) (HumanEval, MBPP, HumanEval+, MBPP+), MultiPL-E (0-shot) (Python, C++, JAVA, PHP, TypeScript, C#, Bash, JavaScript) **Math Tasks**: GSM8K (4-shot), MATH (4-shot) **Chinese Tasks**: C-Eval(5-shot), CMMLU (5-shot) **Multilingual Tasks**: Multi-Exam (M3Exam 5-shot, IndoMMLU 3-shot, ruMMLU 5-shot, mMMLU 5-shot), Multi-Understanding (BELEBELE 5-shot, XCOPA 5-shot, XWinograd 5-shot, XStoryCloze 0-shot, PAWS-X 5-shot), Multi-Mathematics (MGSM 8-shot), Multi-Translation (Flores-101 5-shot) #### Qwen2-0.5B & Qwen2-1.5B performances | Datasets | Phi-2 | Gemma-2B | MiniCPM | Qwen1.5-1.8B | Qwen2-0.5B | Qwen2-1.5B | | :--------| :---------: | :------------: | :------------: |:------------: | :------------: | :------------: | |#Non-Emb Params | 2.5B | 2.0B | 2.4B | 1.3B | 0.35B | 1.3B | |MMLU | 52.7 | 42.3 | 53.5 | 46.8 | 45.4 | **56.5** | |MMLU-Pro | - | 15.9 | - | - | 14.7 | 21.8 | |Theorem QA | - | - | - |- | 8.9 | **15.0** | |HumanEval | 47.6 | 22.0 |**50.0**| 20.1 | 22.0 | 31.1 | |MBPP | **55.0** | 29.2 | 47.3 | 18.0 | 22.0 | 37.4 | |GSM8K | 57.2 | 17.7 | 53.8 | 38.4 | 36.5 | **58.5** | |MATH | 3.5 | 11.8 | 10.2 | 10.1 | 10.7 | **21.7** | |BBH | **43.4** | 35.2 | 36.9 | 24.2 | 28.4 | 37.2 | |HellaSwag | **73.1** | 71.4 | 68.3 | 61.4 | 49.3 | 66.6 | |Winogrande | **74.4** | 66.8 | -| 60.3 | 56.8 | 66.2 | |ARC-C | **61.1** | 48.5 | -| 37.9 | 31.5 | 43.9 | |TruthfulQA | 44.5 | 33.1 | -| 39.4 | 39.7 | **45.9** | |C-Eval | 23.4 | 28.0 | 51.1| 59.7 | 58.2 | **70.6** | |CMMLU | 24.2 | - | 51.1 | 57.8 | 55.1 | **70.3** | ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{qwen2, title={Qwen2 Technical Report}, year={2024} } ```

sentence-transformers/all-roberta-large-v1

sentence-transformers

sentence-transformers

sentence-similarity

--- language: en license: apache-2.0 library_name: sentence-transformers tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers pipeline_tag: sentence-similarity --- # all-roberta-large-v1 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/all-roberta-large-v1') 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 import torch.nn.functional as F #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/all-roberta-large-v1') model = AutoModel.from_pretrained('sentence-transformers/all-roberta-large-v1') # 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 sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) # Normalize embeddings sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1) 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/all-roberta-large-v1) ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`roberta-large`](https://huggingface.co/roberta-large) model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the [Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104), organized by Hugging Face. We developped this model as part of the project: [Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks. ## Intended uses Our model is intented to be used as a sentence and short paragraph encoder. Given an input text, it ouptuts a vector which captures the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks. By default, input text longer than 128 word pieces is truncated. ## Training procedure ### Pre-training We use the pretrained [`roberta-large`](https://huggingface.co/roberta-large). Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyper parameters We trained ou model on a TPU v3-8. We train the model during 400k steps using a batch size of 256 (32 per TPU core). We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`. #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences. We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,124,818,467** |

nreimers/MiniLMv2-L6-H384-distilled-from-BERT-Large

nreimers

transformers

fill-mask

# MiniLMv2 This is a MiniLMv2 model from: [https://github.com/microsoft/unilm](https://github.com/microsoft/unilm/tree/master/minilm)

legraphista/llm-compiler-13b-IMat-GGUF

legraphista

gguf

text-generation

--- base_model: facebook/llm-compiler-13b extra_gated_button_content: I Accept Meta LLM Compiler License and AUP 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_fields: Affiliation: text ? 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" inference: false library_name: gguf license: other pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - quantization - imat - imatrix - static - 16bit - 8bit - 6bit - 5bit - 4bit - 3bit - 2bit - 1bit --- # llm-compiler-13b-IMat-GGUF _Llama.cpp imatrix quantization of facebook/llm-compiler-13b_ Original Model: [facebook/llm-compiler-13b](https://huggingface.co/facebook/llm-compiler-13b) Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp [b3256](https://github.com/ggerganov/llama.cpp/releases/tag/b3256) IMatrix dataset: [here](https://gist.githubusercontent.com/bartowski1182/eb213dccb3571f863da82e99418f81e8/raw/b2869d80f5c16fd7082594248e80144677736635/calibration_datav3.txt) - [Files](#files) - [IMatrix](#imatrix) - [Common Quants](#common-quants) - [All Quants](#all-quants) - [Downloading using huggingface-cli](#downloading-using-huggingface-cli) - [Inference](#inference) - [Llama.cpp](#llama-cpp) - [FAQ](#faq) - [Why is the IMatrix not applied everywhere?](#why-is-the-imatrix-not-applied-everywhere) - [How do I merge a split GGUF?](#how-do-i-merge-a-split-gguf) --- ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants | Filename | Quant type | File Size | Status | Uses IMatrix | Is Split | | -------- | ---------- | --------- | ------ | ------------ | -------- | | [llm-compiler-13b.Q8_0.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q8_0.gguf) | Q8_0 | 13.83GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q6_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q6_K.gguf) | Q6_K | 10.68GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q4_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q4_K.gguf) | Q4_K | 7.87GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q3_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q3_K.gguf) | Q3_K | 6.34GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q2_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q2_K.gguf) | Q2_K | 4.85GB | ✅ Available | 🟢 IMatrix | 📦 No ### All Quants | Filename | Quant type | File Size | Status | Uses IMatrix | Is Split | | -------- | ---------- | --------- | ------ | ------------ | -------- | | [llm-compiler-13b.BF16.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.BF16.gguf) | BF16 | 26.03GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.FP16.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.FP16.gguf) | F16 | 26.03GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q8_0.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q8_0.gguf) | Q8_0 | 13.83GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q6_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q6_K.gguf) | Q6_K | 10.68GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q5_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q5_K.gguf) | Q5_K | 9.23GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q5_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q5_K_S.gguf) | Q5_K_S | 8.97GB | ✅ Available | ⚪ Static | 📦 No | [llm-compiler-13b.Q4_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q4_K.gguf) | Q4_K | 7.87GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q4_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q4_K_S.gguf) | Q4_K_S | 7.42GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ4_NL.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ4_NL.gguf) | IQ4_NL | 7.37GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ4_XS.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ4_XS.gguf) | IQ4_XS | 6.96GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q3_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q3_K.gguf) | Q3_K | 6.34GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q3_K_L.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q3_K_L.gguf) | Q3_K_L | 6.93GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q3_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q3_K_S.gguf) | Q3_K_S | 5.66GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ3_M.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ3_M.gguf) | IQ3_M | 5.98GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ3_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ3_S.gguf) | IQ3_S | 5.66GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ3_XS.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ3_XS.gguf) | IQ3_XS | 5.36GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ3_XXS.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ3_XXS.gguf) | IQ3_XXS | 4.96GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q2_K.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q2_K.gguf) | Q2_K | 4.85GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.Q2_K_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.Q2_K_S.gguf) | Q2_K_S | 4.44GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ2_M.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ2_M.gguf) | IQ2_M | 4.52GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ2_S.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ2_S.gguf) | IQ2_S | 4.20GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ2_XS.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ2_XS.gguf) | IQ2_XS | 3.89GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ2_XXS.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ2_XXS.gguf) | IQ2_XXS | 3.54GB | ✅ Available | 🟢 IMatrix | 📦 No | [llm-compiler-13b.IQ1_M.gguf](https://huggingface.co/legraphista/llm-compiler-13b-IMat-GGUF/blob/main/llm-compiler-13b.IQ1_M.gguf) | IQ1_M | 3.14GB | ✅ Available | 🟢 IMatrix | 📦 No | llm-compiler-13b.IQ1_S | IQ1_S | - | ⏳ Processing | 🟢 IMatrix | - ## Downloading using huggingface-cli If you do not have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Download the specific file you want: ``` huggingface-cli download legraphista/llm-compiler-13b-IMat-GGUF --include "llm-compiler-13b.Q8_0.gguf" --local-dir ./ ``` If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/llm-compiler-13b-IMat-GGUF --include "llm-compiler-13b.Q8_0/*" --local-dir ./ # see FAQ for merging GGUF's ``` --- ## Inference ### Llama.cpp ``` llama.cpp/main -m llm-compiler-13b.Q8_0.gguf --color -i -p "prompt here" ``` --- ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `llm-compiler-13b.Q8_0`) 3. Run `gguf-split --merge llm-compiler-13b.Q8_0/llm-compiler-13b.Q8_0-00001-of-XXXXX.gguf llm-compiler-13b.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!

google/paligemma-3b-pt-224

google

transformers

image-text-to-text

--- library_name: transformers license: gemma pipeline_tag: image-text-to-text 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 --- # PaliGemma model card **Model page:** [PaliGemma](https://ai.google.dev/gemma/docs/paligemma) Transformers PaliGemma 3B weights, pre-trained with 224*224 input images and 128 token input/output text sequences. The models are available in float32, bfloat16 and float16 formats for fine-tuning. **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://www.kaggle.com/models/google/paligemma/license/consent/verify/huggingface?returnModelRepoId=google/paligemma-3b-pt-224) **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.

yanekyuk/camembert-keyword-extractor

yanekyuk

transformers

token-classification

--- license: mit tags: - generated_from_trainer metrics: - precision - recall - accuracy - f1 language: - fr widget: - text: "Le président de la République appelle en outre les Français à faire le choix d'une \"majorité stable et sérieuse pour les protéger face aux crises et pour agir pour l'avenir\". \"Je vois dans le projet de Jean-Luc Mélenchon ou de Madame Le Pen un projet de désordre et de soumission. Ils expliquent qu'il faut sortir de nos alliances, de l'Europe, et bâtir des alliances stratégiques avec la Russie. C'est la soumission à la Russie\", assure-t-il." - text: "Top départ à l’ouverture des bureaux de vote. La Polynésie et les Français résidant à l'étranger, dont certains ont déjà pu voter en ligne, sont invités aux urnes ce week-end pour le premier tour des législatives, samedi 4 juin pour le continent américain et les Caraïbes, et dimanche 5 juin pour le reste du monde. En France métropolitaine, les premier et second tours auront lieu les 12 et 19 juin." - text: "Le ministère a aussi indiqué que des missiles russes ont frappé un centre d'entraînement d'artillerie dans la région de Soumy où travaillaient des instructeurs étrangers. Il a jouté qu'une autre frappe avait détruit une position de \"mercenaires étrangers\" dans la région d'Odessa." - text: "Le malaise est profond et ressemble à une crise existentielle. Fait rarissime au Quai d’Orsay, six syndicats et un collectif de 500 jeunes diplomates du ministère des Affaires étrangères ont appelé à la grève, jeudi 2 juin, pour protester contre la réforme de la haute fonction publique qui, à terme, entraînera la disparition des deux corps historiques de la diplomatie française : celui de ministre plénipotentiaire (ambassadeur) et celui de conseiller des affaires étrangères." - text: "Ils se font passer pour des recruteurs de Lockheed Martin ou du géant britannique de la défense et de l’aérospatial BAE Systems. Ces soi-disant chasseurs de tête font miroiter des perspectives lucratives de carrière et des postes à responsabilité. Mais ce n’est que du vent. En réalité, il s’agit de cyberespions nord-coréens cherchant à voler des secrets industriels de groupes de défense ou du secteur de l’aérospatial, révèle Eset, une société slovaque de sécurité informatique, dans un rapport publié mardi 31 mai." model-index: - name: camembert-keyword-extractor results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # camembert-keyword-extractor This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2199 - Precision: 0.6743 - Recall: 0.6979 - Accuracy: 0.9346 - F1: 0.6859 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:--------:|:------:| | 0.1747 | 1.0 | 1875 | 0.1780 | 0.5935 | 0.7116 | 0.9258 | 0.6472 | | 0.1375 | 2.0 | 3750 | 0.1588 | 0.6505 | 0.7032 | 0.9334 | 0.6759 | | 0.1147 | 3.0 | 5625 | 0.1727 | 0.6825 | 0.6689 | 0.9355 | 0.6756 | | 0.0969 | 4.0 | 7500 | 0.1759 | 0.6886 | 0.6621 | 0.9350 | 0.6751 | | 0.0837 | 5.0 | 9375 | 0.1967 | 0.6688 | 0.7112 | 0.9348 | 0.6893 | | 0.0746 | 6.0 | 11250 | 0.2088 | 0.6646 | 0.7114 | 0.9334 | 0.6872 | | 0.0666 | 7.0 | 13125 | 0.2169 | 0.6713 | 0.7054 | 0.9347 | 0.6879 | | 0.0634 | 8.0 | 15000 | 0.2199 | 0.6743 | 0.6979 | 0.9346 | 0.6859 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1

liamhvn/disney-pixar-cartoon-b

liamhvn

diffusers

text-to-image

--- license: creativeml-openrail-m tags: - stablediffusionapi.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Disney Pixar Cartoon type B API Inference  ## Get API Key Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed. Replace Key in below code, change **model_id** to "disney-pixar-cartoon" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs) Try model for free: [Generate Images](https://stablediffusionapi.com/models/disney-pixar-cartoon) Model link: [View model](https://stablediffusionapi.com/models/disney-pixar-cartoon) Credits: [View credits](https://civitai.com/?query=Disney%20Pixar%20Cartoon%20type%20B) View all models: [View Models](https://stablediffusionapi.com/models) import requests import json url = "https://stablediffusionapi.com/api/v3/dreambooth" payload = json.dumps({ "key": "your_api_key", "model_id": "disney-pixar-cartoon", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off **DMGG0RBN**

timm/vit_giant_patch14_dinov2.lvd142m

timm

timm

image-feature-extraction

--- license: apache-2.0 library_name: timm tags: - image-feature-extraction - timm --- # Model card for vit_giant_patch14_dinov2.lvd142m A Vision Transformer (ViT) image feature model. Pretrained on LVD-142M with self-supervised DINOv2 method. ## Model Details - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 1136.5 - GMACs: 1784.2 - Activations (M): 2757.9 - Image size: 518 x 518 - **Papers:** - DINOv2: Learning Robust Visual Features without Supervision: https://arxiv.org/abs/2304.07193 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Original:** https://github.com/facebookresearch/dinov2 - **Pretrain Dataset:** LVD-142M ## 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('vit_giant_patch14_dinov2.lvd142m', 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) ``` ### 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( 'vit_giant_patch14_dinov2.lvd142m', 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, 1370, 1536) 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 @misc{oquab2023dinov2, title={DINOv2: Learning Robust Visual Features without Supervision}, author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr}, journal={arXiv:2304.07193}, year={2023} } ``` ```bibtex @article{dosovitskiy2020vit, title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}, author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil}, journal={ICLR}, 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}} } ```

Salesforce/blip2-flan-t5-xl

Salesforce

transformers

image-to-text

--- language: en license: mit tags: - vision - image-to-text - image-captioning - visual-question-answering pipeline_tag: image-to-text inference: false --- # BLIP-2, Flan T5-xl, pre-trained only BLIP-2 model, leveraging [Flan T5-xl](https://huggingface.co/google/flan-t5-xl) (a large language model). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Direct Use and Downstream Use You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ## Bias, Risks, Limitations, and Ethical Considerations BLIP2-FlanT5 uses off-the-shelf Flan-T5 as the language model. It inherits the same risks and limitations from [Flan-T5](https://arxiv.org/pdf/2210.11416.pdf): > Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example). #### Running the model on CPU <details> <summary> Click to expand </summary> ```python import requests from PIL import Image from transformers import BlipProcessor, Blip2ForConditionalGeneration processor = BlipProcessor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> #### Running the model on GPU ##### In full precision <details> <summary> Click to expand </summary> ```python # pip install accelerate import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda") out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In half precision (`float16`) <details> <summary> Click to expand </summary> ```python # pip install accelerate import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", torch_dtype=torch.float16, device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details> ##### In 8-bit precision (`int8`) <details> <summary> Click to expand </summary> ```python # pip install accelerate bitsandbytes import torch import requests from PIL import Image from transformers import Blip2Processor, Blip2ForConditionalGeneration processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", load_in_8bit=True, device_map="auto") img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB') question = "how many dogs are in the picture?" inputs = processor(raw_image, question, return_tensors="pt").to("cuda", torch.float16) out = model.generate(**inputs) print(processor.decode(out[0], skip_special_tokens=True)) ``` </details>

mradermacher/gemma-2-27b-i1-GGUF

mradermacher

transformers

--- base_model: google/gemma-2-27b extra_gated_button_content: Acknowledge license 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. language: - en library_name: transformers license: gemma quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/google/gemma-2-27b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gemma-2-27b-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/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ1_S.gguf) | i1-IQ1_S | 6.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ1_M.gguf) | i1-IQ1_M | 6.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 8.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ2_S.gguf) | i1-IQ2_S | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ2_M.gguf) | i1-IQ2_M | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q2_K.gguf) | i1-Q2_K | 10.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 10.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 11.7 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ3_S.gguf) | i1-IQ3_S | 12.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ3_M.gguf) | i1-IQ3_M | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 14.9 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q4_0.gguf) | i1-Q4_0 | 15.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 19.0 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-i1-GGUF/resolve/main/gemma-2-27b.i1-Q6_K.gguf) | i1-Q6_K | 22.4 | 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 -->

hf-tiny-model-private/tiny-random-CodeGenForCausalLM

hf-tiny-model-private

transformers

text-generation

Entry not found

Helsinki-NLP/opus-mt-pl-en

Helsinki-NLP

transformers

translation

--- tags: - translation license: apache-2.0 --- ### opus-mt-pl-en * source languages: pl * target languages: en * OPUS readme: [pl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-en/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.zip) * test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.test.txt) * test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | Tatoeba.pl.en | 54.9 | 0.701 |

hfl/chinese-macbert-base

hfl

transformers

fill-mask

--- language: - zh tags: - bert license: "apache-2.0" --- <p align="center"> <br> <img src="https://github.com/ymcui/MacBERT/raw/master/pics/banner.png" width="500"/> <br> </p> <p align="center"> <a href="https://github.com/ymcui/MacBERT/blob/master/LICENSE"> <img alt="GitHub" src="https://img.shields.io/github/license/ymcui/MacBERT.svg?color=blue&style=flat-square"> </a> </p> # Please use 'Bert' related functions to load this model! This repository contains the resources in our paper **"Revisiting Pre-trained Models for Chinese Natural Language Processing"**, which will be published in "[Findings of EMNLP](https://2020.emnlp.org)". You can read our camera-ready paper through [ACL Anthology](#) or [arXiv pre-print](https://arxiv.org/abs/2004.13922). **[Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922)** *Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, Shijin Wang, Guoping Hu* You may also interested in, - Chinese BERT series: https://github.com/ymcui/Chinese-BERT-wwm - 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 ## Introduction **MacBERT** is an improved BERT with novel **M**LM **a**s **c**orrection pre-training task, which mitigates the discrepancy of pre-training and fine-tuning. Instead of masking with [MASK] token, which never appears in the fine-tuning stage, **we propose to use similar words for the masking purpose**. A similar word is obtained by using [Synonyms toolkit (Wang and Hu, 2017)](https://github.com/chatopera/Synonyms), which is based on word2vec (Mikolov et al., 2013) similarity calculations. If an N-gram is selected to mask, we will find similar words individually. In rare cases, when there is no similar word, we will degrade to use random word replacement. Here is an example of our pre-training task. | | Example | | -------------- | ----------------- | | **Original Sentence** | we use a language model to predict the probability of the next word. | | **MLM** | we use a language [M] to [M] ##di ##ct the pro [M] ##bility of the next word . | | **Whole word masking** | we use a language [M] to [M] [M] [M] the [M] [M] [M] of the next word . | | **N-gram masking** | we use a [M] [M] to [M] [M] [M] the [M] [M] [M] [M] [M] next word . | | **MLM as correction** | we use a text system to ca ##lc ##ulate the po ##si ##bility of the next word . | Except for the new pre-training task, we also incorporate the following techniques. - Whole Word Masking (WWM) - N-gram masking - Sentence-Order Prediction (SOP) **Note that our MacBERT can be directly replaced with the original BERT as there is no differences in the main neural architecture.** For more technical details, please check our paper: [Revisiting Pre-trained Models for Chinese Natural Language Processing](https://arxiv.org/abs/2004.13922) ## Citation If you find our resource or paper is useful, please consider including the following citation in your paper. - 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", } ```

mradermacher/gemma-2-27b-it-i1-GGUF

mradermacher

transformers

--- base_model: google/gemma-2-27b-it extra_gated_button_content: Acknowledge license 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. language: - en library_name: transformers license: gemma quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/google/gemma-2-27b-it <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/gemma-2-27b-it-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/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ1_S.gguf) | i1-IQ1_S | 6.2 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ1_M.gguf) | i1-IQ1_M | 6.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 7.7 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ2_XS.gguf) | i1-IQ2_XS | 8.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ2_S.gguf) | i1-IQ2_S | 8.8 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ2_M.gguf) | i1-IQ2_M | 9.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q2_K.gguf) | i1-Q2_K | 10.5 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 10.9 | lower quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ3_XS.gguf) | i1-IQ3_XS | 11.7 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ3_S.gguf) | i1-IQ3_S | 12.3 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.3 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ3_M.gguf) | i1-IQ3_M | 12.6 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.5 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.6 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-IQ4_XS.gguf) | i1-IQ4_XS | 14.9 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q4_0.gguf) | i1-Q4_0 | 15.8 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.8 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q5_K_S.gguf) | i1-Q5_K_S | 19.0 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.5 | | | [GGUF](https://huggingface.co/mradermacher/gemma-2-27b-it-i1-GGUF/resolve/main/gemma-2-27b-it.i1-Q6_K.gguf) | i1-Q6_K | 22.4 | 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 -->

mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF

mradermacher

transformers

--- base_model: nuprl/MultiPL-T-DeepSeekCoder_33b 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-DeepSeekCoder_33b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-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-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q2_K.gguf) | Q2_K | 12.5 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.IQ3_XS.gguf) | IQ3_XS | 13.8 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q3_K_S.gguf) | Q3_K_S | 14.5 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.IQ3_S.gguf) | IQ3_S | 14.6 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.IQ3_M.gguf) | IQ3_M | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q3_K_M.gguf) | Q3_K_M | 16.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q3_K_L.gguf) | Q3_K_L | 17.7 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.IQ4_XS.gguf) | IQ4_XS | 18.1 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q4_K_S.gguf) | Q4_K_S | 19.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q5_K_S.gguf) | Q5_K_S | 23.1 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q5_K_M.gguf) | Q5_K_M | 23.6 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q6_K.gguf) | Q6_K | 27.5 | very good quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-DeepSeekCoder_33b-GGUF/resolve/main/MultiPL-T-DeepSeekCoder_33b.Q8_0.gguf) | Q8_0 | 35.5 | 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 -->

NlpHUST/ner-vietnamese-electra-base

NlpHUST

transformers

token-classification

--- widget: - text: "Liên quan vụ việc CSGT bị tố đánh dân, trúng một cháu nhỏ đang ngủ, đang lan truyền trên mạng xã hội, Đại tá Nguyễn Văn Tảo, Phó Giám đốc Công an tỉnh Tiền Giang vừa có cuộc họp cùng Chỉ huy Công an huyện Châu Thành và một số đơn vị nghiệp vụ cấp tỉnh để chỉ đạo làm rõ thông tin." tags: - named-entity-recognition language: - vi model-index: - name: ner-vietnamese-electra-base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vietnamese-ner This model is a fine-tuned version of [NlpHUST/electra-base-vn](https://huggingface.co/NlpHUST/electra-base-vn) on an VLSP 2018 dataset. It achieves the following results on the evaluation set: - Loss: 0.0580 - Location Precision: 0.9353 - Location Recall: 0.9377 - Location F1: 0.9365 - Location Number: 2360 - Miscellaneous Precision: 0.5660 - Miscellaneous Recall: 0.6897 - Miscellaneous F1: 0.6218 - Miscellaneous Number: 174 - Organization Precision: 0.8610 - Organization Recall: 0.9068 - Organization F1: 0.8833 - Organization Number: 1878 - Person Precision: 0.9692 - Person Recall: 0.9637 - Person F1: 0.9664 - Person Number: 2121 - Overall Precision: 0.9122 - Overall Recall: 0.9307 - Overall F1: 0.9214 - Overall Accuracy: 0.9907 ## Model description More information needed #### How to use You can use this model with Transformers *pipeline* for NER. ```python from transformers import AutoTokenizer, AutoModelForTokenClassification from transformers import pipeline tokenizer = AutoTokenizer.from_pretrained("NlpHUST/ner-vietnamese-electra-base") model = AutoModelForTokenClassification.from_pretrained("NlpHUST/ner-vietnamese-electra-base") nlp = pipeline("ner", model=model, tokenizer=tokenizer) example = "Liên quan vụ việc CSGT bị tố đánh dân, trúng một cháu nhỏ đang ngủ, đang lan truyền trên mạng xã hội, Đại tá Nguyễn Văn Tảo, Phó Giám đốc Công an tỉnh Tiền Giang vừa có cuộc họp cùng Chỉ huy Công an huyện Châu Thành và một số đơn vị nghiệp vụ cấp tỉnh để chỉ đạo làm rõ thông tin." ner_results = nlp(example) print(ner_results) ``` ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Framework versions - Transformers 4.20.1 - Pytorch 1.8.0+cu111 - Datasets 2.4.0 - Tokenizers 0.12.1 ### Contact information For personal communication related to this project, please contact Nha Nguyen Van (nha282@gmail.com).

hf-tiny-model-private/tiny-random-OPTForCausalLM

hf-tiny-model-private

transformers

text-generation

Entry not found

mradermacher/SchizoGPT-8x22B-i1-GGUF

mradermacher

transformers

--- base_model: v2ray/SchizoGPT-8x22B datasets: - v2ray/r-chatgpt-general-dump language: - en library_name: transformers license: mit quantized_by: mradermacher tags: - not-for-all-audiences --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/v2ray/SchizoGPT-8x22B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/SchizoGPT-8x22B-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/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ1_S.gguf) | i1-IQ1_S | 29.7 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ1_M.gguf) | i1-IQ1_M | 32.8 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 | | | [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 42.1 | | | [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_S.gguf) | i1-IQ2_S | 42.7 | | | [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 | | | [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | 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 -->

KoboldAI/LLaMA2-13B-Tiefighter-GGUF

KoboldAI

--- license: llama2 --- This is the GGUF version of the model meant for use in [KoboldCpp](https://koboldai.org/cpp), check the [Float16](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) version for the original. # LLaMA2-13B-Tiefighter Tiefighter is a merged model achieved trough merging two different lora's on top of a well established existing merge. To achieve this the following recipe was used: * We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merged, contrary to the name this model does not have a strong NSFW bias. * Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model. * After applying this lora we merged the new model with PocketDoc/Dans-RetroRodeo-13b at 5% to weaken the newly introduced adventure bias. * The resulting merge was used as a new basemodel to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%. This means this model contains the following ingredients from their upstream models for as far as we can track them: - Undi95/Xwin-MLewd-13B-V0.2 - - Undi95/ReMM-S-Light - Undi95/CreativeEngine - Brouz/Slerpeno - - elinas/chronos-13b-v2 - jondurbin/airoboros-l2-13b-2.1 - NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 - CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 - - KoboldAI/LLAMA2-13B-Holodeck-1 - NousResearch/Nous-Hermes-13b - OpenAssistant/llama2-13b-orca-8k-3319 - ehartford/WizardLM-1.0-Uncensored-Llama2-13b - Henk717/spring-dragon - The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible) - - SuperCOT (Undisclosed version) - elinas/chronos-13b-v2 (Version assumed) - NousResearch/Nous-Hermes-Llama2-13b - stabilityai/StableBeluga-13B (Version assumed) - zattio770/120-Days-of-LORA-v2-13B - PygmalionAI/pygmalion-2-13b - Undi95/Storytelling-v1-13B-lora - TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter - nRuaif/Kimiko-v2-13B - The-Face-Of-Goonery/Huginn-13b-FP16 - - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp" - lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT - Xwin-LM/Xwin-LM-13B-V0.2 - PocketDoc/Dans-RetroRodeo-13b - Blackroot/Llama-2-13B-Storywriter-LORA While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible! Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible. # Usage This model is meant to be creative, If you let it improvise you get better results than if you drown it in details. ## Story Writing Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story. ### Generate a story on demand To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong. ## Chatbots and persona's This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further. For example, you can put this in memory in regular chat mode: ``` ### Instruction: Generate a conversation between Alice and Henk where they discuss language models. In this conversation Henk is excited to teach Alice about Tiefigther. ### Response: ``` Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format. ## Instruct Prompting This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work. During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias. Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up. ## Adventuring and Adventure Games This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode). It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns. ## Discovered something cool and want to engage with us? Join our community at https://koboldai.org/discord !

laion/CLIP-ViT-L-14-laion2B-s32B-b82K

laion

open_clip

zero-shot-image-classification

--- 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 library_name: open_clip pipeline_tag: zero-shot-image-classification --- # Model Card for CLIP ViT-L/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 L/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 ('babysitting') done by Ross Wightman on the [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) supercomputer. See acknowledgements below. # 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 The model was trained on 384 A100 GPUs using 200M sample 'virtual' epochs where dataset shards were sampled with replacement. The model was trained with 160 virtual epochs for a total of 32B samples seen. The first 68 epochs were trained with float16 AMP, global batch size 79K (208 per GPU). Initially running to epoch 75, where the loss spiked and training failed with NaN. Romain Beaumont was training H/14 and g/14 models at the same time on Stability cluster and hit similar instabilities. Collectively we tried restarts with, * different dataset shuffle seed * different LR * gradient clipping * modifications to the architecture * Norm modifications (stable norm for final, post embed norm for text transformer) as per https://github.com/mlfoundations/open_clip/pull/153 thanks to Phil Wang * Extra attention block norms ala Normformer (https://arxiv.org/abs/2110.09456) * Scaled cosine attention ala Swin-V2 (https://arxiv.org/abs/2111.09883) None of the above ended up working. Most blew up within the same epoch as original, with the exception of architecture mods. * Normformer mods signifcantly altered the network such that resuming did not quickly converge to previous performance, this was abandoned but might be worth trying from start. * Scaled cosine attn initially looked promising and lasted until epoch 90 before loss suddenly increased and appeared to remain 'stuck'. In the end, restarting at epoch 69 with `float32` precision solved all instabilities and training continued from there with global batch size 86k (224 per GPU). On A100 GPUs, `float32` had a minimal impact on the throughput once `tf32` matmuls were enabled in PyTorch. Approximately 10% slower than `float16 AMP`. Romain similary changed the precision but ended up using `bfloat16 AMP` to resolve issues. ### Slum Script ``` #SBATCH --nodes=96 #SBATCH --gres=gpu:4 #SBATCH --ntasks-per-node=4 #SBATCH --cpus-per-task=6 #SBATCH --wait-all-nodes=1 #SBATCH --job-name=open_clip_laion2b # load low-level libraries ml purge source /conda/bin/activate pytorch-112 export NCCL_ASYNC_ERROR_HANDLING=1 export CUDA_VISIBLE_DEVICES=0,1,2,3 export MASTER_PORT=12802 ### get the first node name as master address - customized for vgg slurm ### e.g. master(gnodee[2-5],gnoded1) == gnodee2 echo "NODELIST="${SLURM_NODELIST} master_addr=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1) export MASTER_ADDR=$master_addr"i" echo "MASTER_ADDR="$MASTER_ADDR cd /home/me/open_clip export PYTHONPATH="$PYTHONPATH:$PWD/src" srun --cpu_bind=none,v --accel-bind=gn python -u src/training/main.py \ --save-frequency 1 \ --zeroshot-frequency 1 \ --train-data="/data/laion2B-en/{00000..23295}.tar" \ --train-num-samples=200000000 \ --warmup 10000 \ --lr "1e-3" \ --batch-size=224 \ --epochs=160 \ --workers=6 \ --model ViT-L-14 \ --name "L14-laion2B" \ --report-to "tensorboard" \ --seed 0 \ --precision 'fp32' \ --ddp-static-graph \ --local-loss \ --dataset-resampled \ --gather-with-grad \ --grad-checkpointing ``` # 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 75.3 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 the Gauss Centre for Supercomputing e.V. (http://gauss-centre.eu) for funding this part of work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS Booster at Jülich Supercomputing Centre (JSC). # Citation **BibTeX:** LAION-5B ```bibtex @inproceedings{schuhmann2022laionb, title={{LAION}-5B: An open large-scale dataset for training next generation image-text models}, author={Christoph Schuhmann and Romain Beaumont and Richard Vencu and Cade W Gordon and Ross Wightman and Mehdi Cherti and Theo Coombes and Aarush Katta and Clayton Mullis and Mitchell Wortsman and Patrick Schramowski and Srivatsa R Kundurthy and Katherine Crowson and Ludwig Schmidt and Robert Kaczmarczyk and Jenia Jitsev}, booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2022}, url={https://openreview.net/forum?id=M3Y74vmsMcY} } ``` 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

imvladikon/wav2vec2-xls-r-300m-hebrew

imvladikon

transformers

automatic-speech-recognition

--- language: - he tags: - automatic-speech-recognition - generated_from_trainer - he - hf-asr-leaderboard - robust-speech-event base_model: facebook/wav2vec2-xls-r-300m model-index: - name: wav2vec2-xls-r-300m-hebrew results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Custom Dataset type: custom args: he metrics: - type: wer value: 23.18 name: Test WER --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-300m-hebrew This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model. Small dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|4.19| 20306 | 28 | | |dev |1.05| 5076 | 7 | | Large dataset: | split |size(gb) | n_samples | duration(hrs)| | |---|---|---|---|---| |train|12.3| 90777 | 69 | | |dev |2.39| 20246 | 14* | | (*weakly labeled data wasn't used in validation set) After firts training it achieves: on small dataset - Loss: 0.5438 - WER: 0.1773 on large dataset - WER: 0.3811 after second training: on small dataset - WER: 0.1697 on large dataset - Loss: 0.4502 - WER: 0.2318 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters #### First training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 3.15 | 1000 | 0.5203 | 0.4333 | | 1.4284 | 6.31 | 2000 | 0.4816 | 0.3951 | | 1.4284 | 9.46 | 3000 | 0.4315 | 0.3546 | | 1.283 | 12.62 | 4000 | 0.4278 | 0.3404 | | 1.283 | 15.77 | 5000 | 0.4090 | 0.3054 | | 1.1777 | 18.93 | 6000 | 0.3893 | 0.3006 | | 1.1777 | 22.08 | 7000 | 0.3968 | 0.2857 | | 1.0994 | 25.24 | 8000 | 0.3892 | 0.2751 | | 1.0994 | 28.39 | 9000 | 0.4061 | 0.2690 | | 1.0323 | 31.54 | 10000 | 0.4114 | 0.2507 | | 1.0323 | 34.7 | 11000 | 0.4021 | 0.2508 | | 0.9623 | 37.85 | 12000 | 0.4032 | 0.2378 | | 0.9623 | 41.01 | 13000 | 0.4148 | 0.2374 | | 0.9077 | 44.16 | 14000 | 0.4350 | 0.2323 | | 0.9077 | 47.32 | 15000 | 0.4515 | 0.2246 | | 0.8573 | 50.47 | 16000 | 0.4474 | 0.2180 | | 0.8573 | 53.63 | 17000 | 0.4649 | 0.2171 | | 0.8083 | 56.78 | 18000 | 0.4455 | 0.2102 | | 0.8083 | 59.94 | 19000 | 0.4587 | 0.2092 | | 0.769 | 63.09 | 20000 | 0.4794 | 0.2012 | | 0.769 | 66.25 | 21000 | 0.4845 | 0.2007 | | 0.7308 | 69.4 | 22000 | 0.4937 | 0.2008 | | 0.7308 | 72.55 | 23000 | 0.4920 | 0.1895 | | 0.6927 | 75.71 | 24000 | 0.5179 | 0.1911 | | 0.6927 | 78.86 | 25000 | 0.5202 | 0.1877 | | 0.6622 | 82.02 | 26000 | 0.5266 | 0.1840 | | 0.6622 | 85.17 | 27000 | 0.5351 | 0.1854 | | 0.6315 | 88.33 | 28000 | 0.5373 | 0.1811 | | 0.6315 | 91.48 | 29000 | 0.5331 | 0.1792 | | 0.6075 | 94.64 | 30000 | 0.5390 | 0.1779 | | 0.6075 | 97.79 | 31000 | 0.5459 | 0.1773 | #### Second training The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | No log | 0.7 | 1000 | 0.5371 | 0.3811 | | 1.3606 | 1.41 | 2000 | 0.5247 | 0.3902 | | 1.3606 | 2.12 | 3000 | 0.5126 | 0.3859 | | 1.3671 | 2.82 | 4000 | 0.5062 | 0.3828 | | 1.3671 | 3.53 | 5000 | 0.4979 | 0.3672 | | 1.3421 | 4.23 | 6000 | 0.4906 | 0.3816 | | 1.3421 | 4.94 | 7000 | 0.4784 | 0.3651 | | 1.328 | 5.64 | 8000 | 0.4810 | 0.3669 | | 1.328 | 6.35 | 9000 | 0.4747 | 0.3597 | | 1.3109 | 7.05 | 10000 | 0.4813 | 0.3808 | | 1.3109 | 7.76 | 11000 | 0.4631 | 0.3561 | | 1.2873 | 8.46 | 12000 | 0.4603 | 0.3431 | | 1.2873 | 9.17 | 13000 | 0.4579 | 0.3533 | | 1.2661 | 9.87 | 14000 | 0.4471 | 0.3365 | | 1.2661 | 10.58 | 15000 | 0.4584 | 0.3437 | | 1.249 | 11.28 | 16000 | 0.4461 | 0.3454 | | 1.249 | 11.99 | 17000 | 0.4482 | 0.3367 | | 1.2322 | 12.69 | 18000 | 0.4464 | 0.3335 | | 1.2322 | 13.4 | 19000 | 0.4427 | 0.3454 | | 1.22 | 14.1 | 20000 | 0.4440 | 0.3395 | | 1.22 | 14.81 | 21000 | 0.4459 | 0.3378 | | 1.2044 | 15.51 | 22000 | 0.4406 | 0.3199 | | 1.2044 | 16.22 | 23000 | 0.4398 | 0.3155 | | 1.1913 | 16.92 | 24000 | 0.4237 | 0.3150 | | 1.1913 | 17.63 | 25000 | 0.4287 | 0.3279 | | 1.1705 | 18.34 | 26000 | 0.4253 | 0.3103 | | 1.1705 | 19.04 | 27000 | 0.4234 | 0.3098 | | 1.1564 | 19.75 | 28000 | 0.4174 | 0.3076 | | 1.1564 | 20.45 | 29000 | 0.4260 | 0.3160 | | 1.1461 | 21.16 | 30000 | 0.4235 | 0.3036 | | 1.1461 | 21.86 | 31000 | 0.4309 | 0.3055 | | 1.1285 | 22.57 | 32000 | 0.4264 | 0.3006 | | 1.1285 | 23.27 | 33000 | 0.4201 | 0.2880 | | 1.1135 | 23.98 | 34000 | 0.4131 | 0.2975 | | 1.1135 | 24.68 | 35000 | 0.4202 | 0.2849 | | 1.0968 | 25.39 | 36000 | 0.4105 | 0.2888 | | 1.0968 | 26.09 | 37000 | 0.4210 | 0.2834 | | 1.087 | 26.8 | 38000 | 0.4123 | 0.2843 | | 1.087 | 27.5 | 39000 | 0.4216 | 0.2803 | | 1.0707 | 28.21 | 40000 | 0.4161 | 0.2787 | | 1.0707 | 28.91 | 41000 | 0.4186 | 0.2740 | | 1.0575 | 29.62 | 42000 | 0.4118 | 0.2845 | | 1.0575 | 30.32 | 43000 | 0.4243 | 0.2773 | | 1.0474 | 31.03 | 44000 | 0.4221 | 0.2707 | | 1.0474 | 31.73 | 45000 | 0.4138 | 0.2700 | | 1.0333 | 32.44 | 46000 | 0.4102 | 0.2638 | | 1.0333 | 33.15 | 47000 | 0.4162 | 0.2650 | | 1.0191 | 33.85 | 48000 | 0.4155 | 0.2636 | | 1.0191 | 34.56 | 49000 | 0.4129 | 0.2656 | | 1.0087 | 35.26 | 50000 | 0.4157 | 0.2632 | | 1.0087 | 35.97 | 51000 | 0.4090 | 0.2654 | | 0.9901 | 36.67 | 52000 | 0.4183 | 0.2587 | | 0.9901 | 37.38 | 53000 | 0.4251 | 0.2648 | | 0.9795 | 38.08 | 54000 | 0.4229 | 0.2555 | | 0.9795 | 38.79 | 55000 | 0.4176 | 0.2546 | | 0.9644 | 39.49 | 56000 | 0.4223 | 0.2513 | | 0.9644 | 40.2 | 57000 | 0.4244 | 0.2530 | | 0.9534 | 40.9 | 58000 | 0.4175 | 0.2538 | | 0.9534 | 41.61 | 59000 | 0.4213 | 0.2505 | | 0.9397 | 42.31 | 60000 | 0.4275 | 0.2565 | | 0.9397 | 43.02 | 61000 | 0.4315 | 0.2528 | | 0.9269 | 43.72 | 62000 | 0.4316 | 0.2501 | | 0.9269 | 44.43 | 63000 | 0.4247 | 0.2471 | | 0.9175 | 45.13 | 64000 | 0.4376 | 0.2469 | | 0.9175 | 45.84 | 65000 | 0.4335 | 0.2450 | | 0.9026 | 46.54 | 66000 | 0.4336 | 0.2452 | | 0.9026 | 47.25 | 67000 | 0.4400 | 0.2427 | | 0.8929 | 47.95 | 68000 | 0.4382 | 0.2429 | | 0.8929 | 48.66 | 69000 | 0.4361 | 0.2415 | | 0.8786 | 49.37 | 70000 | 0.4413 | 0.2398 | | 0.8786 | 50.07 | 71000 | 0.4392 | 0.2415 | | 0.8714 | 50.78 | 72000 | 0.4345 | 0.2406 | | 0.8714 | 51.48 | 73000 | 0.4475 | 0.2402 | | 0.8589 | 52.19 | 74000 | 0.4473 | 0.2374 | | 0.8589 | 52.89 | 75000 | 0.4457 | 0.2357 | | 0.8493 | 53.6 | 76000 | 0.4462 | 0.2366 | | 0.8493 | 54.3 | 77000 | 0.4494 | 0.2356 | | 0.8395 | 55.01 | 78000 | 0.4472 | 0.2352 | | 0.8395 | 55.71 | 79000 | 0.4490 | 0.2339 | | 0.8295 | 56.42 | 80000 | 0.4489 | 0.2318 | | 0.8295 | 57.12 | 81000 | 0.4469 | 0.2320 | | 0.8225 | 57.83 | 82000 | 0.4478 | 0.2321 | | 0.8225 | 58.53 | 83000 | 0.4525 | 0.2326 | | 0.816 | 59.24 | 84000 | 0.4532 | 0.2316 | | 0.816 | 59.94 | 85000 | 0.4502 | 0.2318 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.2.dev0 - Tokenizers 0.11.0

aspis/gpt2-genre-story-generation

aspis

transformers

text-generation

--- language: - en tags: - text-generation license: apache-2.0 --- # GPT-2 fine-tuned for short story generation Gpt-2 for short story generation with genres. ## Model description Gpt-2 model fine-tuned on sample of BookCorpus dataset for short story generation, allows for the following genres (tokens to use as input under parenthesis): - Romance (romance) - Adventure (adventure) - Mystery & detective (mystery-&-detective) - Fantasy (fantasy) - Humor & comedy (humor-&-comedy) - Paranormal (paranormal) - Science fiction (science-fiction) Heavily inspired by https://huggingface.co/pranavpsv ## Intended uses & limitations This can be used for text generation. ### How to use: ```python >>> from transformers import pipeline, TextGenerationPipeline, GPT2LMHeadModel, AutoTokenizer >>> model_name = "aspis/gpt2-genre-story-generation" >>> model = GPT2LMHeadModel.from_pretrained(model_name) >>> tokenizer = AutoTokenizer.from_pretrained(model_name) >>> generator = TextGenerationPipeline(model=model, tokenizer=tokenizer) # Input should be of format "<BOS> <Genre token> Optional starter text" >>> input_prompt = "<BOS> <adventure>" >>> story = generator(input_prompt, max_length=80, do_sample=True, repetition_penalty=1.5, temperature=1.2, top_p=0.95, top_k=50) >>> print(story) [{'generated_text': '<BOS> <adventure> "How come they got that one?" asked Louran. The leader of the House, a young man with blonde hair and an odd grin...that didn\'t look so bad to her if she did have a smile on its face. She had known about this before. And now he\'d admitted it himself;'}] ``` ## Training data The model was trained using the BookCorpus dataset by getting the different genres per book and dividing the text into paragraphs.

RishuD7/CML_Text_date_number_blank_v1

RishuD7

transformers

text2text-generation

Entry not found

THUDM/glm-4v-9b

THUDM

transformers

--- license: other license_name: glm-4 license_link: https://huggingface.co/THUDM/glm-4v-9b/blob/main/LICENSE language: - zh - en tags: - glm - chatglm - thudm inference: false --- # GLM-4V-9B Read this in [English](README_en.md) GLM-4V-9B 是智谱 AI 推出的最新一代预训练模型 GLM-4 系列中的开源多模态版本。 **GLM-4V-9B** 具备 1120 * 1120 高分辨率下的中英双语多轮对话能力，在中英文综合能力、感知推理、文字识别、图表理解等多方面多模态评测中，GLM-4V-9B 表现出超越 GPT-4-turbo-2024-04-09、Gemini 1.0 Pro、Qwen-VL-Max 和 Claude 3 Opus 的卓越性能。 ### 多模态能力 GLM-4V-9B 是一个多模态语言模型，具备视觉理解能力，其相关经典任务的评测结果如下： | | **MMBench-EN-Test** | **MMBench-CN-Test** | **SEEDBench_IMG** | **MMStar** | **MMMU** | **MME** | **HallusionBench** | **AI2D** | **OCRBench** | |-------------------------|---------------------|---------------------|-------------------|------------|----------|---------|--------------------|----------|--------------| | | 英文综合 | 中文综合 | 综合能力 | 综合能力 | 学科综合 | 感知推理 | 幻觉性 | 图表理解 | 文字识别 | | **GPT-4o, 20240513** | 83.4 | 82.1 | 77.1 | 63.9 | 69.2 | 2310.3 | 55 | 84.6 | 736 | | **GPT-4v, 20240409** | 81 | 80.2 | 73 | 56 | 61.7 | 2070.2 | 43.9 | 78.6 | 656 | | **GPT-4v, 20231106** | 77 | 74.4 | 72.3 | 49.7 | 53.8 | 1771.5 | 46.5 | 75.9 | 516 | | **InternVL-Chat-V1.5** | 82.3 | 80.7 | 75.2 | 57.1 | 46.8 | 2189.6 | 47.4 | 80.6 | 720 | | **LlaVA-Next-Yi-34B** | 81.1 | 79 | 75.7 | 51.6 | 48.8 | 2050.2 | 34.8 | 78.9 | 574 | | **Step-1V** | 80.7 | 79.9 | 70.3 | 50 | 49.9 | 2206.4 | 48.4 | 79.2 | 625 | | **MiniCPM-Llama3-V2.5** | 77.6 | 73.8 | 72.3 | 51.8 | 45.8 | 2024.6 | 42.4 | 78.4 | 725 | | **Qwen-VL-Max** | 77.6 | 75.7 | 72.7 | 49.5 | 52 | 2281.7 | 41.2 | 75.7 | 684 | | **GeminiProVision** | 73.6 | 74.3 | 70.7 | 38.6 | 49 | 2148.9 | 45.7 | 72.9 | 680 | | **Claude-3V Opus** | 63.3 | 59.2 | 64 | 45.7 | 54.9 | 1586.8 | 37.8 | 70.6 | 694 | | **GLM-4v-9B** | 81.1 | 79.4 | 76.8 | 58.7 | 47.2 | 2163.8 | 46.6 | 81.1 | 786 | **本仓库是 GLM-4V-9B 的模型仓库，支持`8K`上下文长度。** ## 运行模型 更多推理代码和依赖信息，请访问我们的 [github](https://github.com/THUDM/GLM-4) 。 ```python import torch from PIL import Image from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" tokenizer = AutoTokenizer.from_pretrained("THUDM/glm-4v-9b", trust_remote_code=True) query = '描述这张图片' image = Image.open("your image").convert('RGB') inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}], add_generation_prompt=True, tokenize=True, return_tensors="pt", return_dict=True) # chat mode inputs = inputs.to(device) model = AutoModelForCausalLM.from_pretrained( "THUDM/glm-4v-9b", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True ).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0])) ``` ## 协议 GLM-4 模型的权重的使用则需要遵循 [LICENSE](LICENSE)。 ## 引用 如果你觉得我们的工作有帮助的话，请考虑引用下列论文。 ``` @misc{glm2024chatglm, title={ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools}, author={Team GLM and Aohan Zeng and Bin Xu and Bowen Wang and Chenhui Zhang and Da Yin and Diego Rojas and Guanyu Feng and Hanlin Zhao and Hanyu Lai and Hao Yu and Hongning Wang and Jiadai Sun and Jiajie Zhang and Jiale Cheng and Jiayi Gui and Jie Tang and Jing Zhang and Juanzi Li and Lei Zhao and Lindong Wu and Lucen Zhong and Mingdao Liu and Minlie Huang and Peng Zhang and Qinkai Zheng and Rui Lu and Shuaiqi Duan and Shudan Zhang and Shulin Cao and Shuxun Yang and Weng Lam Tam and Wenyi Zhao and Xiao Liu and Xiao Xia and Xiaohan Zhang and Xiaotao Gu and Xin Lv and Xinghan Liu and Xinyi Liu and Xinyue Yang and Xixuan Song and Xunkai Zhang and Yifan An and Yifan Xu and Yilin Niu and Yuantao Yang and Yueyan Li and Yushi Bai and Yuxiao Dong and Zehan Qi and Zhaoyu Wang and Zhen Yang and Zhengxiao Du and Zhenyu Hou and Zihan Wang}, year={2024}, eprint={2406.12793}, archivePrefix={arXiv}, primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'} } ``` ``` @misc{wang2023cogvlm, title={CogVLM: Visual Expert for Pretrained Language Models}, author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang}, year={2023}, eprint={2311.03079}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

SanctumAI/Mistral-7B-Instruct-v0.3-GGUF

SanctumAI

transformers

text-generation

--- pipeline_tag: text-generation license: apache-2.0 ---  *This model was quantized by [SanctumAI](https://sanctum.ai). To leave feedback, join our community in [Discord](https://discord.gg/7ZNE78HJKh).* # Mistral 7B Instruct v0.3 GGUF **Model creator:** [mistralai](https://huggingface.co/mistralai)<br> **Original model**: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)<br> ## Model Summary: The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3. Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md) - Extended vocabulary to 32768 - Supports v3 Tokenizer - Supports function calling ## Prompt Template: If you're using Sanctum app, simply use `Mistral` model preset. Prompt template: ``` <s>[INST] {prompt} [/INST] ``` ## Hardware Requirements Estimate | Name | Quant method | Size | Memory (RAM, vRAM) required (for full context of 32k tokens) | | ---- | ---- | ---- | ---- | | [mistral-7b-instruct-v0.3.Q2_K.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q2_K.gguf) | Q2_K | 2.72 GB | 6.78 GB | | [mistral-7b-instruct-v0.3.Q3_K_S.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q3_K_S.gguf) | Q3_K_S | 3.17 GB | 7.19 GB | | [mistral-7b-instruct-v0.3.Q3_K_M.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q3_K_M.gguf) | Q3_K_M | 3.52 GB | 7.52 GB | | [mistral-7b-instruct-v0.3.Q3_K_L.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q3_K_L.gguf) | Q3_K_L | 3.83 GB | 7.80 GB | | [mistral-7b-instruct-v0.3.Q4_0.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q4_0.gguf) | Q4_0 | 4.11 GB | 8.07 GB | | [mistral-7b-instruct-v0.3.Q4_K_S.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q4_K_S.gguf) | Q4_K_S | 4.14 GB | 8.10 GB | | [mistral-7b-instruct-v0.3.Q4_K_M.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q4_K_M.gguf) | Q4_K_M | 4.37 GB | 8.31 GB | | [mistral-7b-instruct-v0.3.Q4_K.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q4_K.gguf) | Q4_K | 4.37 GB | 8.31 GB | | [mistral-7b-instruct-v0.3.Q4_1.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q4_1.gguf) | Q4_1 | 4.56 GB | 8.48 GB | | [mistral-7b-instruct-v0.3.Q5_0.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q5_0.gguf) | Q5_0 | 5.00 GB | 8.90 GB | | [mistral-7b-instruct-v0.3.Q5_K_S.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q5_K_S.gguf) | Q5_K_S | 5.00 GB | 8.90 GB | | [mistral-7b-instruct-v0.3.Q5_K_M.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q5_K_M.gguf) | Q5_K_M | 5.14 GB | 9.02 GB | | [mistral-7b-instruct-v0.3.Q5_K.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q5_K.gguf) | Q5_K | 5.14 GB | 9.02 GB | | [mistral-7b-instruct-v0.3.Q5_1.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q5_1.gguf) | Q5_1 | 5.45 GB | 9.31 GB | | [mistral-7b-instruct-v0.3.Q6_K.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q6_K.gguf) | Q6_K | 5.95 GB | 9.78 GB | | [mistral-7b-instruct-v0.3.Q8_0.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.Q8_0.gguf) | Q8_0 | 7.70 GB | 11.41 GB | | [mistral-7b-instruct-v0.3.f16.gguf](https://huggingface.co/SanctumAI/Mistral-7B-Instruct-v0.3-GGUF/blob/main/mistral-7b-instruct-v0.3.f16.gguf) | f16 | 14.50 GB | 17.74 GB | ## Disclaimer Sanctum is not the creator, originator, or owner of any Model featured in the Models section of the Sanctum application. Each Model is created and provided by third parties. Sanctum does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Model listed there. You understand that supported Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Model is the sole responsibility of the person or entity who originated such Model. Sanctum may not monitor or control the Models supported and cannot, and does not, take responsibility for any such Model. Sanctum disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Models. Sanctum further disclaims any warranty that the 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. 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MaziyarPanahi/firefunction-v2-GGUF

MaziyarPanahi

transformers

text-generation

--- tags: - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - GGUF - transformers - safetensors - text-generation - conversational - function-calling - text-generation-inference - region:us - text-generation model_name: MaziyarPanahi/firefunction-v2-GGUF base_model: fireworks-ai/firefunction-v2 inference: false model_creator: fireworks-ai pipeline_tag: text-generation quantized_by: MaziyarPanahi license: llama3 --- # [MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF) - Model creator: [fireworks-ai](https://huggingface.co/fireworks-ai) - Original model: [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2) ## Description [MaziyarPanahi/firefunction-v2-GGUF](https://huggingface.co/MaziyarPanahi/firefunction-v2-GGUF) contains GGUF format model files for [fireworks-ai/firefunction-v2](https://huggingface.co/fireworks-ai/firefunction-v2). ### 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. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [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. * [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. * [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. * [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. ## 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. Original README --- # FireFunction V2: Fireworks Function Calling Model [**Try on Fireworks**](https://fireworks.ai/models/fireworks/firefunction-v2) | [**API Docs**](https://readme.fireworks.ai/docs/function-calling) | [**Demo App**](https://functional-chat.vercel.app/) | [**Discord**](https://discord.gg/mMqQxvFD9A) <img src="https://cdn-uploads.huggingface.co/production/uploads/64b6f3a72f5a966b9722de88/nJNtxLzWswBDKK1iOZblb.png" alt="firefunction" width="400"/> FireFunction is a state-of-the-art function calling model with a commercially viable license. View detailed info in our [announcement blog](https://fireworks.ai/blog/firefunction-v2-launch-post). Key info and highlights: **Comparison with other models:** - Competitive with GPT-4o at function-calling, scoring 0.81 vs 0.80 on a medley of public evaluations - Trained on Llama 3 and retains Llama 3’s conversation and instruction-following capabilities, scoring 0.84 vs Llama 3’s 0.89 on MT bench - Significant quality improvements over FireFunction v1 across the broad range of metrics **General info:** 🐾 Successor of the [FireFunction](https://fireworks.ai/models/fireworks/firefunction-v1) model 🔆 Support of parallel function calling (unlike FireFunction v1) and good instruction following 💡 Hosted on the [Fireworks](https://fireworks.ai/models/fireworks/firefunction-v2) platform at < 10% of the cost of GPT 4o and 2x the speed

Aryn/deformable-detr-DocLayNet

Aryn

transformers

object-detection

--- license: apache-2.0 tags: - object-detection - vision datasets: - DocLayNet widget: - src: https://huggingface.co/Aryn/deformable-detr-DocLayNet/resolve/main/examples/doclaynet_example_1.png example_title: DocLayNet Example 1 - src: https://huggingface.co/Aryn/deformable-detr-DocLayNet/resolve/main/examples/doclaynet_example_2.png example_title: DocLayNet Example 2 - src: https://huggingface.co/Aryn/deformable-detr-DocLayNet/resolve/main/examples/doclaynet_example_3.png example_title: DocLayNet Example 3 --- # Deformable DETR model trained on DocLayNet Deformable DEtection TRansformer (DETR), trained on DocLayNet (including 80k annotated pages in 11 classes). ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model.  ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=sensetime/deformable-detr) to look for all available Deformable DETR models. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, DeformableDetrForObjectDetection import torch from PIL import Image import requests url = "https://huggingface.co/Aryn/deformable-detr-DocLayNet/resolve/main/examples/doclaynet_example_1.png" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("Aryn/deformable-detr-DocLayNet") model = DeformableDetrForObjectDetection.from_pretrained("Aryn/deformable-detr-DocLayNet") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.7 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` ## Evaluation results This model achieves 57.1 box mAP on DocLayNet. ## Training data The Deformable DETR model was trained on DocLayNet. It was introduced in the paper [DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis](https://arxiv.org/abs/2206.01062) by Pfitzmann et al. and first released in [this repository](https://github.com/DS4SD/DocLayNet). ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2010.04159, doi = {10.48550/ARXIV.2010.04159}, url = {https://arxiv.org/abs/2010.04159}, author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection}, publisher = {arXiv}, year = {2020}, copyright = {arXiv.org perpetual, non-exclusive license} } ```

Rostlab/ProstT5_fp16

Rostlab

transformers

text2text-generation

--- license: mit ---

MoritzLaurer/mDeBERTa-v3-base-mnli-xnli

MoritzLaurer

transformers

zero-shot-classification

--- language: - multilingual - en - ar - bg - de - el - es - fr - hi - ru - sw - th - tr - ur - vi - zh license: mit tags: - zero-shot-classification - text-classification - nli - pytorch metrics: - accuracy datasets: - multi_nli - xnli pipeline_tag: zero-shot-classification widget: - text: "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels: "politics, economy, entertainment, environment" --- # Multilingual mDeBERTa-v3-base-mnli-xnli ## Model description This multilingual model can perform natural language inference (NLI) on 100 languages and is therefore also suitable for multilingual zero-shot classification. The underlying model was pre-trained by Microsoft on the [CC100 multilingual dataset](https://huggingface.co/datasets/cc100). It was then fine-tuned on the [XNLI dataset](https://huggingface.co/datasets/xnli), which contains hypothesis-premise pairs from 15 languages, as well as the English [MNLI dataset](https://huggingface.co/datasets/multi_nli). As of December 2021, mDeBERTa-base is the best performing multilingual base-sized transformer model, introduced by Microsoft in [this paper](https://arxiv.org/pdf/2111.09543.pdf). If you are looking for a smaller, faster (but less performant) model, you can try [multilingual-MiniLMv2-L6-mnli-xnli](https://huggingface.co/MoritzLaurer/multilingual-MiniLMv2-L6-mnli-xnli). ### How to use the model #### Simple zero-shot classification pipeline ```python from transformers import pipeline classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") sequence_to_classify = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" candidate_labels = ["politics", "economy", "entertainment", "environment"] output = classifier(sequence_to_classify, candidate_labels, multi_label=False) print(output) ``` #### NLI use-case ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") model_name = "MoritzLaurer/mDeBERTa-v3-base-mnli-xnli" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) premise = "Angela Merkel ist eine Politikerin in Deutschland und Vorsitzende der CDU" hypothesis = "Emmanuel Macron is the President of France" input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt") output = model(input["input_ids"].to(device)) # device = "cuda:0" or "cpu" prediction = torch.softmax(output["logits"][0], -1).tolist() label_names = ["entailment", "neutral", "contradiction"] prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)} print(prediction) ``` ### Training data This model was trained on the XNLI development dataset and the MNLI train dataset. The XNLI development set consists of 2490 professionally translated texts from English to 14 other languages (37350 texts in total) (see [this paper](https://arxiv.org/pdf/1809.05053.pdf)). Note that the XNLI contains a training set of 15 machine translated versions of the MNLI dataset for 15 languages, but due to quality issues with these machine translations, this model was only trained on the professional translations from the XNLI development set and the original English MNLI training set (392 702 texts). Not using machine translated texts can avoid overfitting the model to the 15 languages; avoids catastrophic forgetting of the other 85 languages mDeBERTa was pre-trained on; and significantly reduces training costs. ### Training procedure mDeBERTa-v3-base-mnli-xnli was trained using the Hugging Face trainer with the following hyperparameters. ``` training_args = TrainingArguments( num_train_epochs=2, # total number of training epochs learning_rate=2e-05, per_device_train_batch_size=16, # batch size per device during training per_device_eval_batch_size=16, # batch size for evaluation warmup_ratio=0.1, # number of warmup steps for learning rate scheduler weight_decay=0.06, # strength of weight decay ) ``` ### Eval results The model was evaluated on the XNLI test set on 15 languages (5010 texts per language, 75150 in total). Note that multilingual NLI models are capable of classifying NLI texts without receiving NLI training data in the specific language (cross-lingual transfer). This means that the model is also able of doing NLI on the other 85 languages mDeBERTa was training on, but performance is most likely lower than for those languages available in XNLI. Also note that if other multilingual models on the model hub claim performance of around 90% on languages other than English, the authors have most likely made a mistake during testing since non of the latest papers shows a multilingual average performance of more than a few points above 80% on XNLI (see [here](https://arxiv.org/pdf/2111.09543.pdf) or [here](https://arxiv.org/pdf/1911.02116.pdf)). average | ar | bg | de | el | en | es | fr | hi | ru | sw | th | tr | ur | vi | zh ---------|----------|---------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|---------- 0.808 | 0.802 | 0.829 | 0.825 | 0.826 | 0.883 | 0.845 | 0.834 | 0.771 | 0.813 | 0.748 | 0.793 | 0.807 | 0.740 | 0.795 | 0.8116 ## Limitations and bias Please consult the original DeBERTa-V3 paper and literature on different NLI datasets for potential biases. ## Citation If you use this model, please cite: Laurer, Moritz, Wouter van Atteveldt, Andreu Salleras Casas, and Kasper Welbers. 2022. ‘Less Annotating, More Classifying – Addressing the Data Scarcity Issue of Supervised Machine Learning with Deep Transfer Learning and BERT - NLI’. Preprint, June. Open Science Framework. https://osf.io/74b8k. ## Ideas for cooperation or questions? If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/) ## Debugging and issues Note that DeBERTa-v3 was released in late 2021 and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers>=4.13 or higher might solve some issues. Note that mDeBERTa currently does not support FP16, see here: https://github.com/microsoft/DeBERTa/issues/77

doc2query/all-with_prefix-t5-base-v1

doc2query

transformers

text2text-generation

--- language: en datasets: - sentence-transformers/reddit-title-body - sentence-transformers/embedding-training-data widget: - text: "text2reddit: Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." license: apache-2.0 --- # doc2query/all-with_prefix-t5-base-v1 This is a [doc2query](https://arxiv.org/abs/1904.08375) model based on T5 (also known as [docT5query](https://cs.uwaterloo.ca/~jimmylin/publications/Nogueira_Lin_2019_docTTTTTquery-v2.pdf)). It can be used for: - **Document expansion**: You generate for your paragraphs 20-40 queries and index the paragraphs and the generates queries in a standard BM25 index like Elasticsearch, OpenSearch, or Lucene. The generated queries help to close the lexical gap of lexical search, as the generate queries contain synonyms. Further, it re-weights words giving important words a higher weight even if they appear seldomn in a paragraph. In our [BEIR](https://arxiv.org/abs/2104.08663) paper we showed that BM25+docT5query is a powerful search engine. In the [BEIR repository](https://github.com/UKPLab/beir) we have an example how to use docT5query with Pyserini. - **Domain Specific Training Data Generation**: It can be used to generate training data to learn an embedding model. On [SBERT.net](https://www.sbert.net/examples/unsupervised_learning/query_generation/README.html) we have an example how to use the model to generate (query, text) pairs for a given collection of unlabeled texts. These pairs can then be used to train powerful dense embedding models. ## Usage ```python from transformers import T5Tokenizer, T5ForConditionalGeneration model_name = 'doc2query/all-with_prefix-t5-base-v1' tokenizer = T5Tokenizer.from_pretrained(model_name) model = T5ForConditionalGeneration.from_pretrained(model_name) prefix = "answer2question" text = "Python is an interpreted, high-level and general-purpose programming language. Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects." text = prefix+": "+text input_ids = tokenizer.encode(text, max_length=384, truncation=True, return_tensors='pt') outputs = model.generate( input_ids=input_ids, max_length=64, do_sample=True, top_p=0.95, num_return_sequences=5) print("Text:") print(text) print("\nGenerated Queries:") for i in range(len(outputs)): query = tokenizer.decode(outputs[i], skip_special_tokens=True) print(f'{i + 1}: {query}') ``` **Note:** `model.generate()` is non-deterministic. It produces different queries each time you run it. ## Training This model fine-tuned [google/t5-v1_1-base](https://huggingface.co/google/t5-v1_1-base) for 575k training steps. For the training script, see the `train_script.py` in this repository. The input-text was truncated to 384 word pieces. Output text was generated up to 64 word pieces. This model was trained on a large collection of datasets. For the exact datasets names and weights see the `data_config.json` in this repository. Most of the datasets are available at [https://huggingface.co/sentence-transformers](https://huggingface.co/sentence-transformers). The datasets include besides others: - (title, body) pairs from [Reddit](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) - (title, body) pairs and (title, answer) pairs from StackExchange and Yahoo Answers! - (title, review) pairs from Amazon reviews - (query, paragraph) pairs from MS MARCO, NQ, and GooAQ - (question, duplicate_question) from Quora and WikiAnswers - (title, abstract) pairs from S2ORC ## Prefix This model was trained **with a prefix**: You start the text with a specific index that defines what type out output text you would like to receive. Depending on the prefix, the output is different. E.g. the above text about Python produces the following output: | Prefix | Output | | --- | --- | | answer2question | Why should I use python in my business? ; What is the difference between Python and.NET? ; what is the python design philosophy? | | review2title | Python a powerful and useful language ; A new and improved programming language ; Object-oriented, practical and accessibl | | abstract2title | Python: A Software Development Platform ; A Research Guide for Python X: Conceptual Approach to Programming ; Python : Language and Approach | | text2query | is python a low level language? ; what is the primary idea of python? ; is python a programming language? | These are all available pre-fixes: - text2reddit - question2title - answer2question - abstract2title - review2title - news2title - text2query - question2question For the datasets and weights for the different pre-fixes see `data_config.json` in this repository.

mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF

mradermacher

transformers

--- base_model: nuprl/MultiPL-T-CodeLlama_34b 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: nicoboss --> weighted/imatrix quants of https://huggingface.co/nuprl/MultiPL-T-CodeLlama_34b <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-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_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ2_S.gguf) | i1-IQ2_S | 10.7 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.6 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q2_K.gguf) | i1-Q2_K | 12.6 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.0 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.7 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ3_S.gguf) | i1-IQ3_S | 14.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.3 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.4 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.9 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q4_0.gguf) | i1-Q4_0 | 19.2 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.3 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.3 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.3 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.9 | | | [GGUF](https://huggingface.co/mradermacher/MultiPL-T-CodeLlama_34b-i1-GGUF/resolve/main/MultiPL-T-CodeLlama_34b.i1-Q6_K.gguf) | i1-Q6_K | 27.8 | 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 -->

kwoncho/losscut_news_pre2023_2

kwoncho

transformers

text-classification

Entry not found

google/mt5-large

google

transformers

text2text-generation

--- language: - multilingual - af - am - ar - az - be - bg - bn - ca - ceb - co - cs - cy - da - de - el - en - eo - es - et - eu - fa - fi - fil - fr - fy - ga - gd - gl - gu - ha - haw - hi - hmn - ht - hu - hy - ig - is - it - iw - ja - jv - ka - kk - km - kn - ko - ku - ky - la - lb - lo - lt - lv - mg - mi - mk - ml - mn - mr - ms - mt - my - ne - nl - no - ny - pa - pl - ps - pt - ro - ru - sd - si - sk - sl - sm - sn - so - sq - sr - st - su - sv - sw - ta - te - tg - th - tr - uk - und - ur - uz - vi - xh - yi - yo - zh - zu datasets: - mc4 license: apache-2.0 --- [Google's mT5](https://github.com/google-research/multilingual-t5) mT5 is pretrained on the [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) corpus, covering 101 languages: Afrikaans, Albanian, Amharic, Arabic, Armenian, Azerbaijani, Basque, Belarusian, Bengali, Bulgarian, Burmese, Catalan, Cebuano, Chichewa, Chinese, Corsican, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Haitian Creole, Hausa, Hawaiian, Hebrew, Hindi, Hmong, Hungarian, Icelandic, Igbo, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish, Kyrgyz, Lao, Latin, Latvian, Lithuanian, Luxembourgish, Macedonian, Malagasy, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Nepali, Norwegian, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Samoan, Scottish Gaelic, Serbian, Shona, Sindhi, Sinhala, Slovak, Slovenian, Somali, Sotho, Spanish, Sundanese, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, West Frisian, Xhosa, Yiddish, Yoruba, Zulu. **Note**: mT5 was only pre-trained on mC4 excluding any supervised training. Therefore, this model has to be fine-tuned before it is useable on a downstream task. Pretraining Dataset: [mC4](https://www.tensorflow.org/datasets/catalog/c4#c4multilingual) Other Community Checkpoints: [here](https://huggingface.co/models?search=mt5) Paper: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) Authors: *Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel* ## Abstract The recent "Text-to-Text Transfer Transformer" (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We describe the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. All of the code and model checkpoints used in this work are publicly available.

mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF

mradermacher

transformers

--- base_model: cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k 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 language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - generated_from_trainer - axolotl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.3-Yi-1.5-34B-32k <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-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/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/dolphin-2.9.3-Yi-1.5-34B-32k-i1-GGUF/resolve/main/dolphin-2.9.3-Yi-1.5-34B-32k.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | 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 -->

SG161222/Realistic_Vision_V2.0

SG161222

diffusers

text-to-image

--- license: creativeml-openrail-m --- <b>This model is available on <a href="https://www.mage.space/">Mage.Space</a> (main sponsor)</b><br> <b>You can support me directly on Boosty - https://boosty.to/sg_161222</b><br> <b>Please read this!</b><br> For version 2.0 it is recommended to use with VAE (to improve generation quality and get rid of blue artifacts): https://huggingface.co/stabilityai/sd-vae-ft-mse-original<br> <hr/> <b>I use this template to get good generation results: Prompt:</b> RAW photo, *subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 <b>Example:</b> RAW photo, a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3 <b>Negative Prompt:</b> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br> <b>OR</b><br> (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation <b>Euler A or DPM++ 2M Karras with 25 steps<br> CFG Scale 3,5 - 7<br> Hires. fix with Latent upscaler<br> 0 Hires steps and Denoising strength 0.25-0.45<br> Upscale by 1.1-2.0</b>

dataautogpt3/ProteusV0.3

dataautogpt3

diffusers

text-to-image

--- pipeline_tag: text-to-image widget: - text: >- Anime full body portrait of a swordsman holding his weapon in front of him. He is facing the camera with a fierce look on his face. Anime key visual (best quality, HD, ~+~aesthetic~+~:1.2) output: url: upscaled_image.png - text: >- spacious,circular underground room,{dirtied and bloodied white tiles},amalgamation,flesh,plastic,dark fabric,core,pulsating heart,limbs,human-like arms,twisted angelic wings,arms,covered in skin,feathers,scales,undulate slowly,unseen current,convulsing,head area,chaotic,mass of eyes,mouths,no human features,smaller forms,cherubs,demons,golden wires,surround,holy light,tv static effect,golden glow,shadows,terrifying essence,overwhelming presence,nightmarish,landscape,sparse,cavernous,eerie,dynamic,motion,striking,awe-inspiring,nightmarish,nightmarish,nightmare,horrifying,bio-mechanical,body horror,amalgamation output: url: 2.png - text: >- A robot holding a sign saying 'The Application did not respond' in red colors output: url: 3.png - text: >- A photograph of Hughyen in his early twenties, (an inspiring artist whose art focuses on glitching images and vaporwave color gradients with unexpected conflicting compositions:0.5) output: url: 4.png - text: >- Anime mugshot of a tough woman. She is holding a prison sign that reads "Proteus". Her face is censored. Anime key visual (best quality, HD, ~+~aesthetic~+~:1.2) output: url: 7.png - text: >- Glitch art. 1980s anime, vintage, analogue horror. ((static and noise)), chromatic aberration output: url: 5.png - text: >- Masterpiece, glitch, holy holy holy, fog, by DarkIncursio output: url: 6.png license: gpl-3.0 --- <Gallery /> ## ProteusV0.3: The Anime Update Proteus V0.3 has been advanced with an additional 200,000 anime-related images, further refined by a selection of 15,000 aesthetically pleasing images, enhancing its lighting effects significantly. This upgrade preserves its understanding of prompts and maintains its photorealistic and stylistic capabilities without suffering from catastrophic forgetting. ## Proteus Proteus serves as a sophisticated enhancement over OpenDalleV1.1, leveraging its core functionalities to deliver superior outcomes. Key areas of advancement include heightened responsiveness to prompts and augmented creative capacities. To achieve this, it was fine-tuned using approximately 220,000 GPTV captioned images from copyright-free stock images (with some anime included), which were then normalized. Additionally, DPO (Direct Preference Optimization) was employed through a collection of 10,000 carefully selected high-quality, AI-generated image pairs. In pursuit of optimal performance, numerous LORA (Low-Rank Adaptation) models are trained independently before being selectively incorporated into the principal model via dynamic application methods. These techniques involve targeting particular segments within the model while avoiding interference with other areas during the learning phase. Consequently, Proteus exhibits marked improvements in portraying intricate facial characteristics and lifelike skin textures, all while sustaining commendable proficiency across various aesthetic domains, notably surrealism, anime, and cartoon-style visualizations. ## Settings for ProteusV0.3 Use these settings for the best results with ProteusV0.3: CFG Scale: Use a CFG scale of 8 to 7 Steps: 20 to 60 steps for more detail, 20 steps for faster results. Sampler: DPM++ 2M SDE Scheduler: Karras Resolution: 1280x1280 or 1024x1024 please also consider using these keep words to improve your prompts: best quality, HD, `~*~aesthetic~*~`. if you are having trouble coming up with prompts you can use this GPT I put together to help you refine the prompt. https://chat.openai.com/g/g-RziQNoydR-diffusion-master ## Use it with 🧨 diffusers ```python import torch from diffusers import ( StableDiffusionXLPipeline, KDPM2AncestralDiscreteScheduler, AutoencoderKL ) # Load VAE component vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) # Configure the pipeline pipe = StableDiffusionXLPipeline.from_pretrained( "dataautogpt3/ProteusV0.3", vae=vae, torch_dtype=torch.float16 ) pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.to('cuda') # Define prompts and generate image prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed" negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image" image = pipe( prompt, negative_prompt=negative_prompt, width=1024, height=1024, guidance_scale=7, num_inference_steps=20 ).images[0] ``` please support the work I do through donating to me on: https://www.buymeacoffee.com/DataVoid or following me on https://twitter.com/DataPlusEngine

mradermacher/AceGPT-v2-32B-i1-GGUF

mradermacher

transformers

--- base_model: FreedomIntelligence/AceGPT-v2-32B language: - ar - zh - en library_name: transformers license: apache-2.0 quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/FreedomIntelligence/AceGPT-v2-32B <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/AceGPT-v2-32B-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/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.3 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.0 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q2_K.gguf) | i1-Q2_K | 12.3 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.7 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.4 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.4 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ3_M.gguf) | i1-IQ3_M | 14.8 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.7 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_0.gguf) | i1-Q4_0 | 18.7 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.6 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.2 | | | [GGUF](https://huggingface.co/mradermacher/AceGPT-v2-32B-i1-GGUF/resolve/main/AceGPT-v2-32B.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | 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 -->

Helsinki-NLP/opus-mt-fi-en

Helsinki-NLP

transformers

translation

--- language: - fi - en tags: - translation license: apache-2.0 --- ### fin-eng * source group: Finnish * target group: English * OPUS readme: [fin-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md) * model: transformer-align * source language(s): fin * target language(s): eng * model: transformer-align * pre-processing: normalization + SentencePiece (spm32k,spm32k) * download original weights: [opus-2020-08-05.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip) * test set translations: [opus-2020-08-05.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt) * test set scores: [opus-2020-08-05.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.eval.txt) ## Benchmarks | testset | BLEU | chr-F | |-----------------------|-------|-------| | newsdev2015-enfi-fineng.fin.eng | 25.3 | 0.536 | | newstest2015-enfi-fineng.fin.eng | 26.9 | 0.547 | | newstest2016-enfi-fineng.fin.eng | 29.0 | 0.571 | | newstest2017-enfi-fineng.fin.eng | 32.3 | 0.594 | | newstest2018-enfi-fineng.fin.eng | 23.8 | 0.517 | | newstest2019-fien-fineng.fin.eng | 29.0 | 0.565 | | newstestB2016-enfi-fineng.fin.eng | 24.5 | 0.527 | | newstestB2017-enfi-fineng.fin.eng | 27.4 | 0.557 | | newstestB2017-fien-fineng.fin.eng | 27.4 | 0.557 | | Tatoeba-test.fin.eng | 53.4 | 0.697 | ### System Info: - hf_name: fin-eng - source_languages: fin - target_languages: eng - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fin-eng/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['fi', 'en'] - src_constituents: {'fin'} - tgt_constituents: {'eng'} - src_multilingual: False - tgt_multilingual: False - prepro: normalization + SentencePiece (spm32k,spm32k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/fin-eng/opus-2020-08-05.test.txt - src_alpha3: fin - tgt_alpha3: eng - short_pair: fi-en - chrF2_score: 0.6970000000000001 - bleu: 53.4 - brevity_penalty: 0.99 - ref_len: 74651.0 - src_name: Finnish - tgt_name: English - train_date: 2020-08-05 - src_alpha2: fi - tgt_alpha2: en - prefer_old: False - long_pair: fin-eng - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41

liuhaotian/llava-v1.6-34b

liuhaotian

LLaVA

image-text-to-text

--- inference: false license: apache-2.0 library_name: LLaVA pipeline_tag: image-text-to-text tags: - llava --- <br> <br> # LLaVA Model Card ## Model details **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) **Model date:** LLaVA-v1.6-34B was trained in December 2023. **Paper or resources for more information:** https://llava-vl.github.io/ ## License [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license. **Where to send questions or comments about the model:** https://github.com/haotian-liu/LLaVA/issues ## Intended use **Primary intended uses:** The primary use of LLaVA is research on large multimodal models and chatbots. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 500K academic-task-oriented VQA data mixture. - 50K GPT-4V data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.

google/mobilebert-uncased

google

transformers

--- language: en thumbnail: https://huggingface.co/front/thumbnails/google.png license: apache-2.0 --- ## MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices MobileBERT is a thin version of BERT_LARGE, while equipped with bottleneck structures and a carefully designed balance between self-attentions and feed-forward networks. This checkpoint is the original MobileBert Optimized Uncased English: [uncased_L-24_H-128_B-512_A-4_F-4_OPT](https://storage.googleapis.com/cloud-tpu-checkpoints/mobilebert/uncased_L-24_H-128_B-512_A-4_F-4_OPT.tar.gz) checkpoint. ## How to use MobileBERT in `transformers` ```python from transformers import pipeline fill_mask = pipeline( "fill-mask", model="google/mobilebert-uncased", tokenizer="google/mobilebert-uncased" ) print( fill_mask(f"HuggingFace is creating a {fill_mask.tokenizer.mask_token} that the community uses to solve NLP tasks.") ) ```

google/mobilenet_v2_1.0_224

google

transformers

image-classification

--- license: other tags: - vision - image-classification datasets: - imagenet-1k widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- # MobileNet V2 MobileNet V2 model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen. It was first released in [this repository](https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet). Disclaimer: The team releasing MobileNet V2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description From the [original README](https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet_v1.md): > MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints of a variety of use cases. They can be built upon for classification, detection, embeddings and segmentation similar to how other popular large scale models, such as Inception, are used. MobileNets can be run efficiently on mobile devices [...] MobileNets trade off between latency, size and accuracy while comparing favorably with popular models from the literature. The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier and **224** is the resolution of the input images the model was trained on. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=mobilenet_v2) 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 AutoImageProcessor, AutoModelForImageClassification from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) preprocessor = AutoImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224") model = AutoModelForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224") inputs = preprocessor(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]) ``` Note: This model actually predicts 1001 classes, the 1000 classes from ImageNet plus an extra “background” class (index 0). Currently, both the feature extractor and model support PyTorch. ### BibTeX entry and citation info ```bibtex @inproceedings{mobilenetv22018, title={MobileNetV2: Inverted Residuals and Linear Bottlenecks}, author={Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen}, booktitle={CVPR}, year={2018} } ```

microsoft/resnet-101

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k --- # ResNet-101 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) 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 AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-101") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-101") inputs = feature_extractor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```

facebook/deit-base-distilled-patch16-224

facebook

transformers

image-classification

--- license: apache-2.0 tags: - image-classification - vision datasets: - imagenet --- # Distilled Data-efficient Image Transformer (base-sized model) Distilled data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Touvron et al. and first released in [this repository](https://github.com/facebookresearch/deit). However, the weights were converted from the [timm repository](https://github.com/rwightman/pytorch-image-models) by Ross Wightman. Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description This model is a distilled Vision Transformer (ViT). It uses a distillation token, besides the class token, to effectively learn from a teacher (CNN) during both pre-training and fine-tuning. The distillation token is learned through backpropagation, by interacting with the class ([CLS]) and patch tokens through the self-attention layers. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=facebook/deit) to look for fine-tuned versions on a task that interests you. ### How to use Since this model is a distilled ViT model, you can plug it into DeiTModel, DeiTForImageClassification or DeiTForImageClassificationWithTeacher. Note that the model expects the data to be prepared using DeiTFeatureExtractor. Here we use AutoFeatureExtractor, which will automatically use the appropriate feature extractor given the model name. 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 AutoFeatureExtractor, DeiTForImageClassificationWithTeacher 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 = AutoFeatureExtractor.from_pretrained('facebook/deit-base-distilled-patch16-224') model = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224') inputs = feature_extractor(images=image, return_tensors="pt") # forward pass 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. ## Training data This model was pretrained and fine-tuned with distillation on [ImageNet-1k](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/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L78). At inference time, images are resized/rescaled to the same resolution (256x256), center-cropped at 224x224 and normalized across the RGB channels with the ImageNet mean and standard deviation. ### Pretraining The model was trained on a single 8-GPU node for 3 days. Training resolution is 224. For all hyperparameters (such as batch size and learning rate) we refer to table 9 of the original paper. ## Evaluation results | Model | ImageNet top-1 accuracy | ImageNet top-5 accuracy | # params | URL | |---------------------------------------|-------------------------|-------------------------|----------|------------------------------------------------------------------| | DeiT-tiny | 72.2 | 91.1 | 5M | https://huggingface.co/facebook/deit-tiny-patch16-224 | | DeiT-small | 79.9 | 95.0 | 22M | https://huggingface.co/facebook/deit-small-patch16-224 | | DeiT-base | 81.8 | 95.6 | 86M | https://huggingface.co/facebook/deit-base-patch16-224 | | DeiT-tiny distilled | 74.5 | 91.9 | 6M | https://huggingface.co/facebook/deit-tiny-distilled-patch16-224 | | DeiT-small distilled | 81.2 | 95.4 | 22M | https://huggingface.co/facebook/deit-small-distilled-patch16-224 | | **DeiT-base distilled** | **83.4** | **96.5** | **87M** | **https://huggingface.co/facebook/deit-base-distilled-patch16-224** | | DeiT-base 384 | 82.9 | 96.2 | 87M | https://huggingface.co/facebook/deit-base-patch16-384 | | DeiT-base distilled 384 (1000 epochs) | 85.2 | 97.2 | 88M | https://huggingface.co/facebook/deit-base-distilled-patch16-384 | 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{touvron2021training, title={Training data-efficient image transformers & distillation through attention}, author={Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou}, year={2021}, eprint={2012.12877}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ```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} } ```

CohereForAI/c4ai-command-r-plus

CohereForAI

transformers

text-generation

--- inference: false license: cc-by-nc-4.0 library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar --- # Model Card for C4AI Command R+ 🚨 **This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit)**. ## Model Summary C4AI Command R+ is an open weights research release of a 104B billion parameter model with highly advanced capabilities, this includes Retrieval Augmented Generation (RAG) and tool use to automate sophisticated tasks. The tool use in this model generation enables multi-step tool use which allows the model to combine multiple tools over multiple steps to accomplish difficult tasks. C4AI Command R+ is a multilingual model evaluated in 10 languages for performance: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese. Command R+ is optimized for a variety of use cases including reasoning, summarization, and question answering. C4AI Command R+ is part of a family of open weight releases from Cohere For AI and Cohere. Our smaller companion model is [C4AI Command R](https://huggingface.co/CohereForAI/c4ai-command-r-v01) Developed by: [Cohere](https://cohere.com/) and [Cohere For AI](https://cohere.for.ai) - 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: c4ai-command-r-plus - Model Size: 104 billion parameters - Context length: 128K **Try C4AI Command R+** You can try out C4AI Command R+ before downloading the weights in our hosted [Hugging Face Space](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus). **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/c4ai-command-r-plus" 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": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|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) ``` **Quantized model through bitsandbytes, 8-bit precision** ```python # pip install 'git+https://github.com/huggingface/transformers.git' bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig bnb_config = BitsAndBytesConfig(load_in_8bit=True) model_id = "CohereForAI/c4ai-command-r-plus" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config) # Format message with the command-r-plus chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|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) ``` **Quantized model through bitsandbytes, 4-bit precision** This model is non-quantized version of C4AI Command R+. You can find the quantized version of C4AI Command R+ using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-plus-4bit). ## Model Details **Input**: Models input text only. **Output**: Models generate text only. **Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. **Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic. Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian. **Context length**: Command R+ supports a context length of 128K. ## Evaluations Command R+ has been submitted to the [Open LLM leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). We include the results below, along with a direct comparison to the strongest state-of-art open weights models currently available on Hugging Face. We note that these results are only useful to compare when evaluations are implemented for all models in a [standardized way](https://github.com/EleutherAI/lm-evaluation-harness) using publically available code, and hence shouldn't be used for comparison outside of models submitted to the leaderboard or compared to self-reported numbers which can't be replicated in the same way. | Model | Average | Arc (Challenge) | Hella Swag | MMLU | Truthful QA | Winogrande | GSM8k | |:--------------------------------|----------:|------------------:|-------------:|-------:|--------------:|-------------:|--------:| | **CohereForAI/c4ai-command-r-plus** | 74.6 | 70.99 | 88.6 | 75.7 | 56.3 | 85.4 | 70.7 | | [DBRX Instruct](https://huggingface.co/databricks/dbrx-instruct) | 74.5 | 68.9 | 89 | 73.7 | 66.9 | 81.8 | 66.9 | | [Mixtral 8x7B-Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.7 | 70.1 | 87.6 | 71.4 | 65 | 81.1 | 61.1 | | [Mixtral 8x7B Chat](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) | 72.6 | 70.2 | 87.6 | 71.2 | 64.6 | 81.4 | 60.7 | | [CohereForAI/c4ai-command-r-v01](https://huggingface.co/CohereForAI/c4ai-command-r-v01) | 68.5 | 65.5 | 87 | 68.2 | 52.3 | 81.5 | 56.6 | | [Llama 2 70B](https://huggingface.co/meta-llama/Llama-2-70b-hf) | 67.9 | 67.3 | 87.3 | 69.8 | 44.9 | 83.7 | 54.1 | | [Yi-34B-Chat](https://huggingface.co/01-ai/Yi-34B-Chat) | 65.3 | 65.4 | 84.2 | 74.9 | 55.4 | 80.1 | 31.9 | | [Gemma-7B](https://huggingface.co/google/gemma-7b) | 63.8 | 61.1 | 82.2 | 64.6 | 44.8 | 79 | 50.9 | | [LLama 2 70B Chat](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | 62.4 | 64.6 | 85.9 | 63.9 | 52.8 | 80.5 | 26.7 | | [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 61 | 60 | 83.3 | 64.2 | 42.2 | 78.4 | 37.8 | We include these metrics here because they are frequently requested, but note that these metrics do not capture RAG, multilingual, tooling performance or the evaluation of open ended generations which we believe Command R+ to be state-of-art at. For evaluations of RAG, multilingual and tooling read more [here](https://txt.cohere.com/command-r-plus-microsoft-azure/). For evaluation of open ended generation, Command R+ is currently being evaluated on the [chatbot arena](https://chat.lmsys.org/). ### Tool use & multihop capabilities: Command R+ has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation. Command R+’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command R+ may use one of its supplied tools more than once. The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the `directly_answer` tool, but it can be removed or renamed if required. Comprehensive documentation for working with command R+'s tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-plus" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use: tools = [ { "name": "internet_search", "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet", "parameter_definitions": { "query": { "description": "Query to search the internet with", "type": 'str', "required": True } } }, { 'name': "directly_answer", "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history", 'parameter_definitions': {} } ] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_tool_use_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary> ```` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary> ```` Action: ```json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin in the world" } } ] ``` ```` </details> ### Grounded Generation and RAG Capabilities: Command R+ has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG). This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation. Command R+’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. By default, Command R+ will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes. A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens. Comprehensive documentation for working with Command R+'s grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary> ````python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-plus" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # define documents to ground on: documents = [ { "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." }, { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."} ] # render the tool use prompt as a string: grounded_generation_prompt = tokenizer.apply_grounded_generation_template( conversation, documents=documents, citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) print(grounded_generation_prompt) ```` </details> <details> <summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary> ````<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results> Document: 0 title: Tall penguins text: Emperor penguins are the tallest growing up to 122 cm in height. Document: 1 title: Penguin habitats text: Emperor penguins only live in Antarctica. </results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line. Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'. Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'. Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup. Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary> ```` Relevant Documents: 0,1 Cited Documents: 0,1 Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres. Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0> ```` </details> ### Code Capabilities: Command R+ has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ### Model Card Contact For errors or additional questions about details in this model card, contact [info@for.ai](mailto: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 104 billion parameter 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 Chat: You can try Command R+ chat in the playground [here](https://dashboard.cohere.com/playground/chat). You can also use it in our dedicated Hugging Face Space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-plus).

embaas/sentence-transformers-e5-large-v2

embaas

sentence-transformers

sentence-similarity

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # embaas/sentence-transformers-e5-large-v2 This is a the sentence-transformers version of the [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## 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('embaas/sentence-transformers-e5-large-v2') embeddings = model.encode(sentences) print(embeddings) ``` ## Using with API You can use the [embaas API](https://embaas.io) to encode your input. Get your free API key from [embaas.io](https://embaas.io) ```python import requests url = "https://api.embaas.io/v1/embeddings/" headers = { "Content-Type": "application/json", "Authorization": "Bearer ${YOUR_API_KEY}" } data = { "texts": ["This is an example sentence.", "Here is another sentence."], "instruction": "query" "model": "e5-large-v2" } response = requests.post(url, json=data, headers=headers) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> Find the results of the e5 at the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, '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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

MahmoudAshraf/mms-300m-1130-forced-aligner

MahmoudAshraf

transformers

automatic-speech-recognition

--- language: - ab - af - ak - am - ar - as - av - ay - az - ba - bm - be - bn - bi - bo - sh - br - bg - ca - cs - ce - cv - ku - cy - da - de - dv - dz - el - en - eo - et - eu - ee - fo - fa - fj - fi - fr - fy - ff - ga - gl - gn - gu - zh - ht - ha - he - hi - sh - hu - hy - ig - ia - ms - is - it - jv - ja - kn - ka - kk - kr - km - ki - rw - ky - ko - kv - lo - la - lv - ln - lt - lb - lg - mh - ml - mr - ms - mk - mg - mt - mn - mi - my - zh - nl - 'no' - 'no' - ne - ny - oc - om - or - os - pa - pl - pt - ms - ps - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - qu - ro - rn - ru - sg - sk - sl - sm - sn - sd - so - es - sq - su - sv - sw - ta - tt - te - tg - tl - th - ti - ts - tr - uk - ms - vi - wo - xh - ms - yo - ms - zu - za license: cc-by-nc-4.0 tags: - mms - wav2vec2 --- # Forced Alignment with Hugging Face CTC Models This Python package provides an efficient way to perform forced alignment between text and audio using Hugging Face's pretrained models. it also features an improved implementation to use much less memory than TorchAudio forced alignment API. The model checkpoint uploaded here is a conversion from torchaudio to HF Transformers for the MMS-300M checkpoint trained on forced alignment dataset ## Installation ```bash pip install git+https://github.com/MahmoudAshraf97/ctc-forced-aligner.git ``` ## Usage ```python import torch from ctc_forced_aligner import ( load_audio, load_alignment_model, generate_emissions, preprocess_text, get_alignments, get_spans, postprocess_results, ) audio_path = "your/audio/path" text_path = "your/text/path" language = "iso" # ISO-639-3 Language code device = "cuda" if torch.cuda.is_available() else "cpu" batch_size = 16 alignment_model, alignment_tokenizer, alignment_dictionary = load_alignment_model( device, dtype=torch.float16 if device == "cuda" else torch.float32, ) audio_waveform = load_audio(audio_path, alignment_model.dtype, alignment_model.device) with open(text_path, "r") as f: lines = f.readlines() text = "".join(line for line in lines).replace("\n", " ").strip() emissions, stride = generate_emissions( alignment_model, audio_waveform, batch_size=batch_size ) tokens_starred, text_starred = preprocess_text( text, romanize=True, language=language, ) segments, scores, blank_id = get_alignments( emissions, tokens_starred, alignment_dictionary, ) spans = get_spans(tokens_starred, segments, alignment_tokenizer.decode(blank_id)) word_timestamps = postprocess_results(text_starred, spans, stride, scores) ```

TheBloke/WizardCoder-Python-13B-V1.0-GGUF

TheBloke

transformers

--- license: llama2 library_name: transformers tags: - code metrics: - code_eval base_model: WizardLM/WizardCoder-Python-13B-V1.0 inference: false model_creator: WizardLM model_type: llama prompt_template: 'Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ' quantized_by: TheBloke model-index: - name: WizardCoder-Python-13B-V1.0 results: - task: type: text-generation dataset: name: HumanEval type: openai_humaneval metrics: - type: pass@1 value: 0.64 name: pass@1 verified: false --- <!-- 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 --> # WizardCoder Python 13B V1.0 - GGUF - Model creator: [WizardLM](https://huggingface.co/WizardLM) - Original model: [WizardCoder Python 13B V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0) <!-- description start --> ## Description This repo contains GGUF format model files for [WizardLM's WizardCoder Python 13B V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0). <!-- 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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate 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. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [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. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [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. <!-- README_GGUF.md-about-gguf end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF) * [WizardLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Response: ``` <!-- prompt-template end --> <!-- compatibility_gguf start --> ## Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](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 | | ---- | ---- | ---- | ---- | ---- | ----- | | [wizardcoder-python-13b-v1.0.Q2_K.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes | | [wizardcoder-python-13b-v1.0.Q3_K_S.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss | | [wizardcoder-python-13b-v1.0.Q3_K_M.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss | | [wizardcoder-python-13b-v1.0.Q3_K_L.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss | | [wizardcoder-python-13b-v1.0.Q4_0.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [wizardcoder-python-13b-v1.0.Q4_K_S.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss | | [wizardcoder-python-13b-v1.0.Q4_K_M.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended | | [wizardcoder-python-13b-v1.0.Q5_0.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [wizardcoder-python-13b-v1.0.Q5_K_S.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended | | [wizardcoder-python-13b-v1.0.Q5_K_M.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended | | [wizardcoder-python-13b-v1.0.Q6_K.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss | | [wizardcoder-python-13b-v1.0.Q8_0.gguf](https://huggingface.co/TheBloke/WizardCoder-Python-13B-V1.0-GGUF/blob/main/wizardcoder-python-13b-v1.0.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 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/WizardCoder-Python-13B-V1.0-GGUF and below it, a specific filename to download, such as: wizardcoder-python-13b-v1.0.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>=0.17.1 ``` 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/WizardCoder-Python-13B-V1.0-GGUF wizardcoder-python-13b-v1.0.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download TheBloke/WizardCoder-Python-13B-V1.0-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 HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/WizardCoder-Python-13B-V1.0-GGUF wizardcoder-python-13b-v1.0.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running 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 [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 32 -m wizardcoder-python-13b-v1.0.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` 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. 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 here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## 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. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # 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 = AutoModelForCausalLM.from_pretrained("TheBloke/WizardCoder-Python-13B-V1.0-GGUF", model_file="wizardcoder-python-13b-v1.0.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or 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**: 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 start --> # Original model card: WizardLM's WizardCoder Python 13B V1.0 <p align="center"> 🤗 <a href="https://huggingface.co/WizardLM" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/nlpxucan/WizardLM" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br> </p> <p align="center"> 👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a> </p> ## News - 🔥🔥🔥[2023/08/26] We released **WizardCoder-Python-34B-V1.0** , which achieves the **73.2 pass@1** and surpasses **GPT4 (2023/03/15)**, **ChatGPT-3.5**, and **Claude2** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). - [2023/06/16] We released **WizardCoder-15B-V1.0** , which achieves the **57.3 pass@1** and surpasses **Claude-Plus (+6.8)**, **Bard (+15.3)** and **InstructCodeT5+ (+22.3)** on the [HumanEval Benchmarks](https://github.com/openai/human-eval). ❗Note: There are two HumanEval results of GPT4 and ChatGPT-3.5. The 67.0 and 48.1 are reported by the official GPT4 Report (2023/03/15) of [OpenAI](https://arxiv.org/abs/2303.08774). The 82.0 and 72.5 are tested by ourselves with the latest API (2023/08/26). | Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License | | ----- |------| ---- |------|-------| ----- | ----- | | WizardCoder-Python-34B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-34B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 73.2 | 61.2 | [Demo](http://47.103.63.15:50085/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-15B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-15B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 59.8 |50.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | | WizardCoder-Python-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 64.0 | 55.6 | -- | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-Python-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 55.5 | 51.6 | [Demo](http://47.103.63.15:50088/) | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama2</a> | | WizardCoder-3B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-3B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 34.8 |37.4 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | | WizardCoder-1B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardCoder-1B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> | 23.8 |28.6 | -- | <a href="https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement" target="_blank">OpenRAIL-M</a> | - Our **WizardMath-70B-V1.0** model slightly outperforms some closed-source LLMs on the GSM8K, including **ChatGPT 3.5**, **Claude Instant 1** and **PaLM 2 540B**. - Our **WizardMath-70B-V1.0** model achieves **81.6 pass@1** on the [GSM8k Benchmarks](https://github.com/openai/grade-school-math), which is **24.8** points higher than the SOTA open-source LLM, and achieves **22.7 pass@1** on the [MATH Benchmarks](https://github.com/hendrycks/math), which is **9.2** points higher than the SOTA open-source LLM. <font size=4> | Model | Checkpoint | Paper | GSM8k | MATH |Online Demo| License| | ----- |------| ---- |------|-------| ----- | ----- | | WizardMath-70B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-70B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **81.6** | **22.7** |[Demo](http://47.103.63.15:50083/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-13B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-13B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **63.9** | **14.0** |[Demo](http://47.103.63.15:50082/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a> | | WizardMath-7B-V1.0 | 🤗 <a href="https://huggingface.co/WizardLM/WizardMath-7B-V1.0" target="_blank">HF Link</a> | 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a>| **54.9** | **10.7** | [Demo ](http://47.103.63.15:50080/)| <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 </a>| </font> - [08/09/2023] We released **WizardLM-70B-V1.0** model. Here is [Full Model Weight](https://huggingface.co/WizardLM/WizardLM-70B-V1.0). <font size=4> | ^Model | ^Checkpoint | ^Paper |^MT-Bench | ^AlpacaEval | ^GSM8k | ^HumanEval | ^License| | ----- |------| ---- |------|-------| ----- | ----- | ----- | | ^{**WizardLM-70B-V1.0**} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-70B-V1.0" target="_blank">HF Link</a>}|^{📃**Coming Soon**}| ^**7.78** | ^**92.91%** |^**77.6%** | ^**50.6**|^{<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a>} | | ^{WizardLM-13B-V1.2} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.2" target="_blank">HF Link</a>}| | ^7.06 | ^89.17% |^55.3% | ^36.6|^{<a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License </a>} | | ^{WizardLM-13B-V1.1} |^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.1" target="_blank">HF Link</a>} | | ^6.76 |^86.32% | | ^25.0| ^{Non-commercial}| | ^{WizardLM-30B-V1.0} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-30B-V1.0" target="_blank">HF Link</a>} | | ^7.01 | | | ^37.8| ^{Non-commercial} | | ^{WizardLM-13B-V1.0} | ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-13B-V1.0" target="_blank">HF Link</a>} | | ^6.35 | ^75.31% | | ^24.0 | ^{Non-commercial}| | ^{WizardLM-7B-V1.0}| ^{🤗 <a href="https://huggingface.co/WizardLM/WizardLM-7B-V1.0" target="_blank">HF Link</a>} |^{📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a>}| | | |^19.1|^{Non-commercial}| </font> ## Comparing WizardCoder-Python-34B-V1.0 with Other LLMs. 🔥 The following figure shows that our **WizardCoder-Python-34B-V1.0 attains the second position in this benchmark**, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2). <p align="center" width="100%"> <a ><img src="https://raw.githubusercontent.com/nlpxucan/WizardLM/main/WizardCoder/imgs/compare_sota.png" alt="WizardCoder" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a> </p> ## Prompt Format ``` "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:" ``` ## Inference Demo Script We provide the inference demo code [here](https://github.com/nlpxucan/WizardLM/tree/main/demo). Note: This script supports `WizardLM/WizardCoder-Python-34B/13B/7B-V1.0`. If you want to inference with `WizardLM/WizardCoder-15B/3B/1B-V1.0`, please change the `stop_tokens = ['</s>']` to `stop_tokens = ['<|endoftext|>']` in the script. ## Citation Please cite the repo if you use the data, method or code in this repo. ``` @article{luo2023wizardcoder, title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct}, author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin}, journal={arXiv preprint arXiv:2306.08568}, year={2023} } ``` <!-- original-model-card end -->

kykim/bert-kor-base

kykim

transformers

fill-mask

--- language: ko --- # Bert base model for Korean * 70GB Korean text dataset and 42000 lower-cased subwords are used * Check the model performance and other language models for Korean in [github](https://github.com/kiyoungkim1/LM-kor) ```python from transformers import BertTokenizerFast, BertModel tokenizer_bert = BertTokenizerFast.from_pretrained("kykim/bert-kor-base") model_bert = BertModel.from_pretrained("kykim/bert-kor-base") ```

Yntec/insaneRealistic_v2

Yntec

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Base Model - Realism - Female - Woman - cordonsolution8 - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # Insane Realistic 2 Original page: https://civitai.com/models/108585/ Samples and prompts:  (Click for larger) Top left: a cute girl with freckles on her face, cgsociety unreal engine, wet t-shirt, short skirt, style of aenami alena, trending on artstartion, inspired by Fyodor Vasilyev, looks a bit similar to amy adams, emissive light, fluffy orange skin, dribbble, dramatic rendering Top right: 90s grainy vhs still young mother loose shirt, headband. holding a baby, on the couch, posing, bow. bokeh, bright lighting. smile Bottom left: beautiful image of the first day of creation of the world and planet earth in the dark deep space, light and darkness separated, planets, under a black night sky of astronomical glittering starlight in the outer reaches of the solar system beyond, trending on artstation, octane render, symmetry by raqib shaw, presence of god, eye of god. Bottom right: hill, mountains, sunset, field, world, ocean, trees, underground, city, village, path, urban, mountain, buildings, waterfall, skyline, nature, town, industrial, architecture, road, jungle, valley, bridge, horizon, landscape, house, building, environment, wilderness, enviroment, river, cave, desert, forest

MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF

MaziyarPanahi

text-generation

--- language: - en pipeline_tag: text-generation tags: - facebook - meta - pytorch - llama - llama-3 - quantized - 2-bit - 3-bit - 4-bit - 5-bit - 6-bit - 8-bit - 16-bit - GGUF inference: false model_creator: MaziyarPanahi model_name: Meta-Llama-3-70B-Instruct-GGUF quantized_by: MaziyarPanahi license_name: llama3 --- # MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF The GGUF and quantized models here are based on [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) model ## How to download You can download only the quants you need instead of cloning the entire repository as follows: ``` huggingface-cli download MaziyarPanahi/Meta-Llama-3-70B-Instruct-GGUF --local-dir . --include '*Q2_K*gguf' ``` ## Load GGUF models You `MUST` follow the prompt template provided by Llama-3: ```sh ./llama.cpp/main -m Meta-Llama-3-70B-Instruct.Q2_K.gguf -r '<|eot_id|>' --in-prefix "\n<|start_header_id|>user<|end_header_id|>\n\n" --in-suffix "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" -p "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.<|eot_id|>\n<|start_header_id|>user<|end_header_id|>\n\nHi! How are you?<|eot_id|>\n<|start_header_id|>assistant<|end_header_id|>\n\n" -n 1024 ``` Original README --- ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. **Model developers** Meta **Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. **Input** Models input text only. **Output** Models generate text and code only. **Model Architecture** Llama 3 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 with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> **Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. **Model Release Date** April 18, 2024. **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://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use **Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction 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** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**. **Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python import transformers import torch model_id = "meta-llama/Meta-Llama-3-70B-Instruct" pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] prompt = pipeline.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>") ] outputs = pipeline( prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) print(outputs[0]["generated_text"][len(prompt):]) ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software **Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. **Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> **CO2 emissions during pre-training**. 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 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. **Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use 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/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its 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 3’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 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos ---

cross-encoder/stsb-TinyBERT-L-4

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

microsoft/dit-base

microsoft

transformers

--- tags: - dit inference: false --- # Document Image Transformer (base-sized model) Document Image Transformer (DiT) model pre-trained on IIT-CDIP (Lewis et al., 2006), a dataset that includes 42 million document images. It was introduced in the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Li et al. and first released in [this repository](https://github.com/microsoft/unilm/tree/master/dit). Note that DiT is identical to the architecture of [BEiT](https://huggingface.co/docs/transformers/model_doc/beit). Disclaimer: The team releasing DiT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The Document Image Transformer (DiT) is a transformer encoder model (BERT-like) pre-trained on a large collection of images in a self-supervised fashion. The pre-training objective for the model is to predict visual tokens from the encoder of a discrete VAE (dVAE), based on masked patches. Images are presented to the model as a sequence of fixed-size patches (resolution 16x16), which are linearly embedded. 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 document images for instance, you can train a standard classifier by placing a linear layer on top of the pre-trained encoder. ## Intended uses & limitations You can use the raw model for encoding document images into a vector space, but it's mostly meant to be fine-tuned on tasks like document image classification, table detection or document layout analysis. See the [model hub](https://huggingface.co/models?search=microsoft/dit) 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 BeitImageProcessor, BeitForMaskedImageModeling import torch from PIL import Image image = Image.open('path_to_your_document_image').convert('RGB') processor = BeitImageProcessor.from_pretrained("microsoft/dit-base") model = BeitForMaskedImageModeling.from_pretrained("microsoft/dit-base") num_patches = (model.config.image_size // model.config.patch_size) ** 2 pixel_values = processor(images=image, return_tensors="pt").pixel_values # create random boolean mask of shape (batch_size, num_patches) bool_masked_pos = torch.randint(low=0, high=2, size=(1, num_patches)).bool() outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) loss, logits = outputs.loss, outputs.logits ``` ### BibTeX entry and citation info ```bibtex @article{Lewis2006BuildingAT, title={Building a test collection for complex document information processing}, author={David D. Lewis and Gady Agam and Shlomo Engelson Argamon and Ophir Frieder and David A. Grossman and Jefferson Heard}, journal={Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval}, year={2006} } ```

shahrukhx01/question-vs-statement-classifier

shahrukhx01

transformers

text-classification

--- language: "en" tags: - neural-search-query-classification - neural-search widget: - text: "what did you eat in lunch?" --- # KEYWORD STATEMENT VS QUESTION CLASSIFIER FOR NEURAL SEARCH ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("shahrukhx01/question-vs-statement-classifier") model = AutoModelForSequenceClassification.from_pretrained("shahrukhx01/question-vs-statement-classifier") ``` Trained to add the feature for classifying queries between Question Query vs Statement Query using classification in [Haystack](https://github.com/deepset-ai/haystack/issues/611)

Yntec/RealLife

Yntec

diffusers

text-to-image

--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - Photorealistic - Base model - Abstract - Fusch - stable-diffusion - stable-diffusion-diffusers - diffusers --- # Real Life v2 Original page: https://civitai.com/models/171814?modelVersionId=219513 Samples and prompts:  (Click for larger) Top left: 1985 movie sreenshoot Santa Claus with wife and little daughter enjoying pancake with honey. sitting with a pretty cute girl, Art Christmas Theme by Gil_Elvgren and Haddon_Sundblom Top right: a painting of a white panther cub by Bnhr, nature, grass, tree, outdoors, forest, animal focus, yellow eyes, Bottom left: view from diagonally above, central adjustment, skinny young northern european female, long reddish blonde hair, real hair movement, elongated head, beautiful face, grey eyes, thin bowed eyebrows, snub nose, gentle lower jaw line, narrow chin, da vinci lips, slightly smiling with parted lips, curious friendly facial expression, small, slim narrow tapered hips Bottom right: kodachrome camera transparency, dramatic lighting film grain, PARTY HARD BACKGROUND, pretty cute little girl in Zone 51, Extraterrestrial, Alien Space Ship Delivering Christmas Presents, Alien Space Ship Decorated With Garlands and Christmas Balls, Snowstorm 

LiheYoung/depth_anything_vits14

LiheYoung

transformers

depth-estimation

--- tags: - depth_anything - depth-estimation --- # Depth Anything model, small The model card for our paper [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://arxiv.org/abs/2401.10891). You may also try our [demo](https://huggingface.co/spaces/LiheYoung/Depth-Anything) and visit our [project page](https://depth-anything.github.io/). ## Installation First, install the Depth Anything package: ``` git clone https://github.com/LiheYoung/Depth-Anything cd Depth-Anything pip install -r requirements.txt ``` ## Usage Here's how to run the model: ```python import numpy as np from PIL import Image import cv2 import torch from depth_anything.dpt import DepthAnything from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet from torchvision.transforms import Compose model = DepthAnything.from_pretrained("LiheYoung/depth_anything_vitl14") transform = Compose([ Resize( width=518, height=518, resize_target=False, keep_aspect_ratio=True, ensure_multiple_of=14, resize_method='lower_bound', image_interpolation_method=cv2.INTER_CUBIC, ), NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), PrepareForNet(), ]) image = Image.open("...") image = np.array(image) / 255.0 image = transform({'image': image})['image'] image = torch.from_numpy(image).unsqueeze(0) depth = model(image) ```

microsoft/resnet-152

microsoft

transformers

image-classification

--- license: apache-2.0 tags: - vision - image-classification datasets: - imagenet-1k --- # ResNet-152 v1.5 ResNet model pre-trained on ImageNet-1k at resolution 224x224. It was introduced in the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by He et al. Disclaimer: The team releasing ResNet did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description ResNet (Residual Network) is a convolutional neural network that democratized the concepts of residual learning and skip connections. This enables to train much deeper models. This is ResNet v1.5, which differs from the original model: in the bottleneck blocks which require downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a small performance drawback (~5% imgs/sec) according to [Nvidia](https://catalog.ngc.nvidia.com/orgs/nvidia/resources/resnet_50_v1_5_for_pytorch).  ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=resnet) 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 AutoFeatureExtractor, ResNetForImageClassification import torch from datasets import load_dataset dataset = load_dataset("huggingface/cats-image") image = dataset["test"]["image"][0] feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-152") model = ResNetForImageClassification.from_pretrained("microsoft/resnet-152") inputs = feature_extractor(image, return_tensors="pt") with torch.no_grad(): logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/resnet). ### BibTeX entry and citation info ```bibtex @inproceedings{he2016deep, title={Deep residual learning for image recognition}, author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian}, booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition}, pages={770--778}, year={2016} } ```

malper/unikud

malper

transformers

--- language: - he --- Please see [this model's DagsHub repository](https://dagshub.com/morrisalp/unikud) for information on usage.

openmmlab/upernet-convnext-small

openmmlab

transformers

image-segmentation

--- language: en license: mit tags: - vision - image-segmentation model_name: openmmlab/upernet-convnext-small --- # UperNet, ConvNeXt small-sized backbone UperNet framework for semantic segmentation, leveraging a ConvNeXt backbone. UperNet was introduced in the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Xiao et al. Combining UperNet with a ConvNeXt backbone was introduced in the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545). Disclaimer: The team releasing UperNet + ConvNeXt did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description UperNet is a framework for semantic segmentation. It consists of several components, including a backbone, a Feature Pyramid Network (FPN) and a Pyramid Pooling Module (PPM). Any visual backbone can be plugged into the UperNet framework. The framework predicts a semantic label per pixel.  ## Intended uses & limitations You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?search=openmmlab/upernet) to look for fine-tuned versions (with various backbones) on a task that interests you. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation).

BAAI/llm-embedder

BAAI

transformers

feature-extraction

--- license: mit --- <h1 align="center">FlagEmbedding</h1> <h4 align="center"> <p> <a href=#model-list>Model List</a> | <a href=#frequently-asked-questions>FAQ</a> | <a href=#usage>Usage</a> | <a href="#evaluation">Evaluation</a> | <a href="#train">Train</a> | <a href="#contact">Contact</a> | <a href="#citation">Citation</a> | <a href="#license">License</a> <p> </h4> More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding). [English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md) <span style="#FF69B4;"> **Hiring:** We're seeking experienced NLP researchers and intern students focusing on dense retrieval and retrieval-augmented LLMs. If you're interested, please feel free to reach out to us via email at zhengliu1026@gmail.com.</span> FlagEmbedding can map any text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, and semantic search. And it can also be used in vector databases for LLMs. ************* 🌟**Updates**🌟 ************* - 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Paper](https://arxiv.org/pdf/2310.07554.pdf) :fire: - 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released - 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released - 09/12/2023: New models: - **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models. - **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction. <details> <summary>More</summary> <!-- ### More --> - 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning. - 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard). - 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗** - 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada: - 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset. </details> ## Model List `bge` is short for `BAAI general embedding`. | Model | Language | | Description | query instruction for retrieval [1] | |:-------------------------------|:--------:| :--------:| :--------:|:--------:| | [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder) | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | | | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` | [1\]: If you need to search the relevant passages in a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages. [2\]: Different from the embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models. For example, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 documents to get the final top-3 results. All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI. If you cannot open the Huggingface Hub, you can also download the models at https://model.baai.ac.cn/models . ## Frequently asked questions **1. How to fine-tune bge embedding model?** Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model. Some suggestions: - Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance. - In general, larger hyper-parameter `per_device_train_batch_size` brings better performance. You can expand it by enabling `--fp16`, `--deepspeed df_config.json` (df_config.json can refer to [ds_config.json](https://github.com/FlagOpen/FlagEmbedding/blob/master/examples/finetune/ds_config.json), `--gradient_checkpointing`, etc. - If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity. - If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker. <details> <summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary> <!-- ### The similarity score between two dissimilar sentences is higher than 0.5 --> **Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.** Since we finetune the models by contrastive learning with a temperature of 0.01, the similarity distribution of the current BGE model is about in the interval \[0.6, 1\]. So a similarity score greater than 0.5 does not indicate that the two sentences are similar. For downstream tasks, such as passage retrieval or semantic similarity, **what matters is the relative order of the scores, not the absolute value.** If you need to filter similar sentences based on a similarity threshold, please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9). </details> <details> <summary>3. When does the query instruction need to be used</summary> <!-- ### When does the query instruction need to be used --> For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction. No instruction only has a slight degradation in retrieval performance compared with using instruction. So you can generate embedding without instruction in all cases for convenience. For a retrieval task that uses short queries to find long related documents, it is recommended to add instructions for these short queries. **The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.** In all cases, the documents/passages do not need to add the instruction. </details> ## Usage ### Usage for Embedding Model Here are some examples of using `bge` models with [FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers). #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding. ```python from FlagEmbedding import FlagModel sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = FlagModel('BAAI/bge-large-zh-v1.5', query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：", use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation embeddings_1 = model.encode(sentences_1) embeddings_2 = model.encode(sentences_2) similarity = embeddings_1 @ embeddings_2.T print(similarity) # for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query # corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] q_embeddings = model.encode_queries(queries) p_embeddings = model.encode(passages) scores = q_embeddings @ p_embeddings.T ``` For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list). By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs. You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable. #### Using Sentence-Transformers You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net): ``` pip install -U sentence-transformers ``` ```python from sentence_transformers import SentenceTransformer sentences_1 = ["样例数据-1", "样例数据-2"] sentences_2 = ["样例数据-3", "样例数据-4"] model = SentenceTransformer('BAAI/bge-large-zh-v1.5') embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) similarity = embeddings_1 @ embeddings_2.T print(similarity) ``` For s2p(short query to long passage) retrieval task, each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)). But the instruction is not needed for passages. ```python from sentence_transformers import SentenceTransformer queries = ['query_1', 'query_2'] passages = ["样例文档-1", "样例文档-2"] instruction = "为这个句子生成表示以用于检索相关文章：" model = SentenceTransformer('BAAI/bge-large-zh-v1.5') q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True) p_embeddings = model.encode(passages, normalize_embeddings=True) scores = q_embeddings @ p_embeddings.T ``` #### Using Langchain You can use `bge` in langchain like this: ```python from langchain.embeddings import HuggingFaceBgeEmbeddings model_name = "BAAI/bge-large-en-v1.5" model_kwargs = {'device': 'cuda'} encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity model = HuggingFaceBgeEmbeddings( model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs, query_instruction="为这个句子生成表示以用于检索相关文章：" ) model.query_instruction = "为这个句子生成表示以用于检索相关文章：" ``` #### Using HuggingFace Transformers With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding. ```python from transformers import AutoTokenizer, AutoModel import torch # Sentences we want sentence embeddings for sentences = ["样例数据-1", "样例数据-2"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5') model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5') model.eval() # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages) # encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, cls pooling. sentence_embeddings = model_output[0][:, 0] # normalize embeddings sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1) print("Sentence embeddings:", sentence_embeddings) ``` ### Usage for Reranker Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range. #### Using FlagEmbedding ``` pip install -U FlagEmbedding ``` Get relevance scores (higher scores indicate more relevance): ```python from FlagEmbedding import FlagReranker reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation score = reranker.compute_score(['query', 'passage']) print(score) scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]) print(scores) ``` #### Using Huggingface transformers ```python import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large') model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large') model.eval() pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']] with torch.no_grad(): inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512) scores = model(**inputs, return_dict=True).logits.view(-1, ).float() print(scores) ``` ## Evaluation `baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!** For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md). - **MTEB**: | Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) | |:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| | [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 | | [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 | | [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 | | [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 | | [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 | | [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 | | [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 | | [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 | | [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 | | [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 | | [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 | | [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 | | [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 | | [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 | | [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 | | [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 | - **C-MTEB**: We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks. Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction. | Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | | [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | | [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 | | [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 | | [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 | | [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 | | [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 | | [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 | | [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 | | [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 | | [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 | | [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 | | [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | | [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 | | [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | | [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | - **Reranking**: See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script. | Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg | |:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:| | text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 | | multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 | | multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 | | multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 | | m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 | | m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 | | bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 | | bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 | | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 | | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 | \* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks ## Train ### BAAI Embedding We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pair data using contrastive learning. **You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).** We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain). Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned. For more training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md). ### BGE Reranker Cross-encoder will perform full-attention over the input pair, which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model. Therefore, it can be used to re-rank the top-k documents returned by embedding model. We train the cross-encoder on a multilingual pair data, The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker). For more details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker) ### Our Contributors: <a href="https://github.com/FlagOpen/FlagEmbedding/graphs/contributors"> <img src="https://contrib.rocks/image?repo=FlagOpen/FlagEmbedding" /> </a> ## 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 Shitao Xiao(stxiao@baai.ac.cn) and Zheng Liu(liuzheng@baai.ac.cn). ## Citation If you find this repository useful, please consider giving a star :star: and citation ``` @misc{bge_embedding, title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff}, year={2023}, eprint={2309.07597}, archivePrefix={arXiv}, primaryClass={cs.CL} } @misc{llm_embedder, title={Retrieve Anything To Augment Large Language Models}, author={Peitian Zhang and Shitao Xiao and Zheng Liu and Zhicheng Dou and Jian-Yun Nie}, year={2023}, eprint={2310.07554}, archivePrefix={arXiv}, primaryClass={cs.IR} } ``` ## License FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.

microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext

microsoft

transformers

fill-mask

--- language: en tags: - exbert license: mit widget: - text: "[MASK] is a tumor suppressor gene." --- ## MSR BiomedBERT (abstracts + full text) <div style="border: 2px solid orange; border-radius:10px; padding:0px 10px; width: fit-content;"> * This model was previously named **"PubMedBERT (abstracts + full text)"**. * You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" 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. BiomedBERT is pretrained from scratch using _abstracts_ from [PubMed](https://pubmed.ncbi.nlm.nih.gov/) and _full-text_ articles from [PubMedCentral](https://www.ncbi.nlm.nih.gov/pmc/). This model achieves state-of-the-art performance on many biomedical NLP tasks, and currently holds the top score 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-fulltext&modelKind=bidirectional&sentence=Gefitinib%20is%20an%20EGFR%20tyrosine%20kinase%20inhibitor,%20which%20is%20often%20used%20for%20breast%20cancer%20and%20NSCLC%20treatment.&layer=3&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>

koheiduck/bert-japanese-finetuned-sentiment

koheiduck

transformers

text-classification

Entry not found

yangheng/deberta-v3-base-absa-v1.1

yangheng

transformers

text-classification

--- language: - en tags: - aspect-based-sentiment-analysis - PyABSA license: mit datasets: - laptop14 - restaurant14 - restaurant16 - ACL-Twitter - MAMS - Television - TShirt - Yelp metrics: - accuracy - macro-f1 widget: - text: "[CLS] when tables opened up, the manager sat another party before us. [SEP] manager [SEP] " --- # Powered by [PyABSA](https://github.com/yangheng95/PyABSA): An open source tool for aspect-based sentiment analysis This model is training with 30k+ ABSA samples, see [ABSADatasets](https://github.com/yangheng95/ABSADatasets). Yet the test sets are not included in pre-training, so you can use this model for training and benchmarking on common ABSA datasets, e.g., Laptop14, Rest14 datasets. (Except for the Rest15 dataset!) ## Usage ```python3 from transformers import AutoTokenizer, AutoModelForSequenceClassification # Load the ABSA model and tokenizer model_name = "yangheng/deberta-v3-base-absa-v1.1" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) for aspect in ['camera', 'phone']: print(aspect, classifier('The camera quality of this phone is amazing.', text_pair=aspect)) ``` # DeBERTa for aspect-based sentiment analysis The `deberta-v3-base-absa` model for aspect-based sentiment analysis, trained with English datasets from [ABSADatasets](https://github.com/yangheng95/ABSADatasets). ## Training Model This model is trained based on the FAST-LCF-BERT model with `microsoft/deberta-v3-base`, which comes from [PyABSA](https://github.com/yangheng95/PyABSA). To track state-of-the-art models, please see [PyASBA](https://github.com/yangheng95/PyABSA). ## Example in PyASBA An [example](https://github.com/yangheng95/PyABSA/blob/release/demos/aspect_polarity_classification/train_apc_multilingual.py) for using FAST-LCF-BERT in PyASBA datasets. ## Datasets This model is fine-tuned with 180k examples for the ABSA dataset (including augmented data). Training dataset files: ``` loading: integrated_datasets/apc_datasets/SemEval/laptop14/Laptops_Train.xml.seg loading: integrated_datasets/apc_datasets/SemEval/restaurant14/Restaurants_Train.xml.seg loading: integrated_datasets/apc_datasets/SemEval/restaurant16/restaurant_train.raw loading: integrated_datasets/apc_datasets/ACL_Twitter/acl-14-short-data/train.raw loading: integrated_datasets/apc_datasets/MAMS/train.xml.dat loading: integrated_datasets/apc_datasets/Television/Television_Train.xml.seg loading: integrated_datasets/apc_datasets/TShirt/Menstshirt_Train.xml.seg loading: integrated_datasets/apc_datasets/Yelp/yelp.train.txt ``` If you use this model in your research, please cite our papers: ``` @inproceedings{DBLP:conf/cikm/0008ZL23, author = {Heng Yang and Chen Zhang and Ke Li}, editor = {Ingo Frommholz and Frank Hopfgartner and Mark Lee and Michael Oakes and Mounia Lalmas and Min Zhang and Rodrygo L. T. Santos}, title = {PyABSA: {A} Modularized Framework for Reproducible Aspect-based Sentiment Analysis}, booktitle = {Proceedings of the 32nd {ACM} International Conference on Information and Knowledge Management, {CIKM} 2023, Birmingham, United Kingdom, October 21-25, 2023}, pages = {5117--5122}, publisher = {{ACM}}, year = {2023}, url = {https://doi.org/10.1145/3583780.3614752}, doi = {10.1145/3583780.3614752}, timestamp = {Thu, 23 Nov 2023 13:25:05 +0100}, biburl = {https://dblp.org/rec/conf/cikm/0008ZL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } @article{YangZMT21, author = {Heng Yang and Biqing Zeng and Mayi Xu and Tianxing Wang}, title = {Back to Reality: Leveraging Pattern-driven Modeling to Enable Affordable Sentiment Dependency Learning}, journal = {CoRR}, volume = {abs/2110.08604}, year = {2021}, url = {https://arxiv.org/abs/2110.08604}, eprinttype = {arXiv}, eprint = {2110.08604}, timestamp = {Fri, 22 Oct 2021 13:33:09 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2110-08604.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF

mradermacher

transformers

--- base_model: BeaverAI/Fook-Yi-34B-32K-25p-Chat language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/BeaverAI/Fook-Yi-34B-32K-25p-Chat <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-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/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/Fook-Yi-34B-32K-25p-Chat-i1-GGUF/resolve/main/Fook-Yi-34B-32K-25p-Chat.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | 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 -->

OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5

OpenAssistant

transformers

text-generation

--- license: apache-2.0 language: - en tags: - sft pipeline_tag: text-generation widget: - text: <|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|> - text: <|prompter|>What's the Earth total population<|endoftext|><|assistant|> - text: <|prompter|>Write a story about future of AI development<|endoftext|><|assistant|> --- # Open-Assistant SFT-4 12B Model This is the 4th iteration English supervised-fine-tuning (SFT) model of the [Open-Assistant](https://github.com/LAION-AI/Open-Assistant) project. It is based on a Pythia 12B that was fine-tuned on human demonstrations of assistant conversations collected through the [https://open-assistant.io/](https://open-assistant.io/) human feedback web app before March 25, 2023. ## Model Details - **Developed by:** [Open-Assistant Contributors](https://open-assistant.io/) - **Model type:** Transformer-based Language Model - **Language:** English - **Finetuned from:** [EleutherAI / pythia-12b-deduped](https://huggingface.co/EleutherAI/pythia-12b-deduped) - **Code:** [Open-Assistant/model/model_training](https://github.com/LAION-AI/Open-Assistant/tree/main/model/model_training) - **Demo:** [Continuations for 250 random prompts](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-04-03_andreaskoepf_oasst-sft-4-pythia-12b-epoch-3_5_sampling_noprefix_lottery.json%0Ahttps%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Fchat-gpt%2F2023-04-11_gpt-3.5-turbo_lottery.json) - **License:** Apache 2.0 - **Contact:** [Open-Assistant Discord](https://ykilcher.com/open-assistant-discord) ## Prompting Two special tokens are used to mark the beginning of user and assistant turns: `<|prompter|>` and `<|assistant|>`. Each turn ends with a `<|endoftext|>` token. Input prompt example: ``` <|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|> ``` The input ends with the `<|assistant|>` token to signal that the model should start generating the assistant reply. ## Dev Details - wandb: https://wandb.ai/open-assistant/supervised-finetuning/runs/770a0t41 - base model: [andreaskoepf/pythia-12b-pre-2000](https://huggingface.co/andreaskoepf/pythia-12b-pre-2000) - checkpoint: 4000 steps command: `deepspeed trainer_sft.py --configs defaults reference-data reference-pythia-12b --cache_dir /home/ubuntu/data_cache --output_dir .saved/oasst-sft-3-pythia-12b-reference_2kpre --num_train_epochs 8 --residual_dropout 0.2 --deepspeed --use_flash_attention true --model_name andreaskoepf/pythia-12b-pre-2000` data: ``` reference-data: datasets: - oasst_export: lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" input_file_path: 2023-03-25_oasst_research_ready_synth_labels.jsonl.gz val_split: 0.05 - alpaca sort_by_length: false use_custom_sampler: false ``` pythia: ``` reference-pythia-12b: dtype: fp16 log_dir: "pythia_log_12b" learning_rate: 6e-6 model_name: EleutherAI/pythia-12b-deduped output_dir: pythia_model_12b weight_decay: 0.0 max_length: 2048 warmup_steps: 100 gradient_checkpointing: true gradient_accumulation_steps: 2 per_device_train_batch_size: 4 per_device_eval_batch_size: 4 eval_steps: 100 save_steps: 1000 num_train_epochs: 8 save_total_limit: 4 ``` zero config: ``` { "fp16": { "enabled": "auto", "loss_scale": 0, "loss_scale_window": 1000, "initial_scale_power": 16, "hysteresis": 2, "min_loss_scale": 1 }, "bf16": { "enabled": "auto" }, "optimizer": { "type": "AdamW", "params": { "lr": "auto", "betas": "auto", "eps": "auto", "weight_decay": "auto" } }, "scheduler": { "type": "WarmupDecayLR", "params": { "warmup_min_lr": "auto", "warmup_max_lr": "auto", "warmup_num_steps": "auto", "total_num_steps": "auto" } }, "zero_optimization": { "stage": 2, "allgather_partitions": true, "allgather_bucket_size": 1e9, "overlap_comm": false, "reduce_scatter": true, "reduce_bucket_size": 1e9, "contiguous_gradients": true }, "gradient_accumulation_steps": "auto", "gradient_clipping": "auto", "steps_per_print": 2000, "train_batch_size": "auto", "train_micro_batch_size_per_gpu": "auto", "wall_clock_breakdown": false } ```

RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-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) Llama-3-15B-Instruct-ft-v2 - GGUF - Model creator: https://huggingface.co/elinas/ - Original model: https://huggingface.co/elinas/Llama-3-15B-Instruct-ft-v2/ | Name | Quant method | Size | | ---- | ---- | ---- | | [Llama-3-15B-Instruct-ft-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q2_K.gguf) | Q2_K | 5.35GB | | [Llama-3-15B-Instruct-ft-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.IQ3_XS.gguf) | IQ3_XS | 5.94GB | | [Llama-3-15B-Instruct-ft-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.IQ3_S.gguf) | IQ3_S | 6.24GB | | [Llama-3-15B-Instruct-ft-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q3_K_S.gguf) | Q3_K_S | 6.21GB | | [Llama-3-15B-Instruct-ft-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.IQ3_M.gguf) | IQ3_M | 6.43GB | | [Llama-3-15B-Instruct-ft-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q3_K.gguf) | Q3_K | 6.87GB | | [Llama-3-15B-Instruct-ft-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q3_K_M.gguf) | Q3_K_M | 6.87GB | | [Llama-3-15B-Instruct-ft-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q3_K_L.gguf) | Q3_K_L | 7.43GB | | [Llama-3-15B-Instruct-ft-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.IQ4_XS.gguf) | IQ4_XS | 7.68GB | | [Llama-3-15B-Instruct-ft-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q4_0.gguf) | Q4_0 | 8.0GB | | [Llama-3-15B-Instruct-ft-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.IQ4_NL.gguf) | IQ4_NL | 8.08GB | | [Llama-3-15B-Instruct-ft-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q4_K_S.gguf) | Q4_K_S | 8.05GB | | [Llama-3-15B-Instruct-ft-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q4_K.gguf) | Q4_K | 8.48GB | | [Llama-3-15B-Instruct-ft-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q4_K_M.gguf) | Q4_K_M | 8.48GB | | [Llama-3-15B-Instruct-ft-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q4_1.gguf) | Q4_1 | 8.84GB | | [Llama-3-15B-Instruct-ft-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q5_0.gguf) | Q5_0 | 9.68GB | | [Llama-3-15B-Instruct-ft-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q5_K_S.gguf) | Q5_K_S | 9.68GB | | [Llama-3-15B-Instruct-ft-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q5_K.gguf) | Q5_K | 9.93GB | | [Llama-3-15B-Instruct-ft-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q5_K_M.gguf) | Q5_K_M | 9.93GB | | [Llama-3-15B-Instruct-ft-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q5_1.gguf) | Q5_1 | 10.53GB | | [Llama-3-15B-Instruct-ft-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q6_K.gguf) | Q6_K | 11.48GB | | [Llama-3-15B-Instruct-ft-v2.Q8_0.gguf](https://huggingface.co/RichardErkhov/elinas_-_Llama-3-15B-Instruct-ft-v2-gguf/blob/main/Llama-3-15B-Instruct-ft-v2.Q8_0.gguf) | Q8_0 | 14.86GB | Original model description: --- base_model: - elinas/Llama-3-15B-Instruct-zeroed library_name: transformers tags: - mergekit - merge - finetune datasets: - Chat-Error/Pure-dove-sharegpt license: llama3 --- # Llama-3-15B-Instruct-zeroed-ft-v2 This is a QLoRA **finetune** of a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). The model is based on a "zeroed" passthrough merge of [Llama-3-15B-Instruct-zeroed](https://huggingface.co/elinas/Llama-3-15B-Instruct-zeroed) This was primarily an experiment to see how a passthrough merge will respond to further finetuning of all LoRA modules. The model was finetuned on **8192 context length** and it can possibly be extended using RoPE up to 32k. **v3 of the model will contain significantly more data, primarily human focused, aimed to excel at writing as well as maintaining logic, coherency, and continuity.** **[GGUF Quants provided by @gelukuMLG](https://huggingface.co/gelukuMLG/Llama-3-15B-Instruct-ft-v2-GGUF)** ## Datasets * [Chat-Error/Pure-dove-sharegpt](https://huggingface.co/datasets/Chat-Error/Pure-dove-sharegpt) A small, high quality, curated dataset was used as a PoC / validation on stabilizing the model after the original passthrough merge. ## Finetuning details This is a QLoRA model and all of the LoRA modules were targeted this time to ensure sufficient training before moving on to larger datasets. the first version of this model only targeted **o_proj** and **up_proj** ```yaml lora_target_modules: - gate_proj - down_proj - up_proj - q_proj - v_proj - k_proj - o_proj lora_modules_to_save: - embed_tokens - lm_head ``` The model is coherent even with training the "zeroed" layers plus the additional layers, as this was the recommendation from [Charles Goddard](https://huggingface.co/chargoddard) (mergekit developer) - thank you for sharing the method of merging as well as Toasty Pigeon for bringing it to my attention! ```yaml The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 3 - total_train_batch_size: 3 - total_eval_batch_size: 3 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 25 - num_epochs: 1 ``` Optimizer `paged_adamw_8bit` and Deepspeed ZeRO 3 was used at a LR of `1e-5` using the cosine scheduler for 1 epoch on 3x3090s taking 4 hours total. **Unsloth** was used for speed and memory savings. Sample packing and padding was disabled to reduce VRAM consumption significantly at the cost of speed. W&B Run Summary ``` wandb: eval/loss 0.90895 wandb: eval/runtime 463.4688 wandb: eval/samples_per_second 0.833 wandb: eval/steps_per_second 0.278 wandb: total_flos 8270790524928.0 wandb: train/epoch 1.0 wandb: train/global_step 1157 wandb: train/grad_norm 7.3847 wandb: train/learning_rate 0.0 wandb: train/loss 0.8702 wandb: train_loss 0.87814 wandb: train_runtime 16425.2713 wandb: train_samples_per_second 0.211 wandb: train_steps_per_second 0.07 ``` ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1 ## Model Evaluation TBD If you have any questions or comments on the model, feel free to open a discussion in the community tab. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

LoneStriker/TinyLlama-1.1B-32k-Instruct-3.0bpw-h6-exl2

LoneStriker

transformers

text-generation

--- inference: false language: - en library_name: transformers pipeline_tag: text-generation tags: - llama datasets: - LDJnr/Capybara - jondurbin/airoboros-3.2 - unalignment/toxic-dpo-v0.1 - LDJnr/Verified-Camel - HuggingFaceH4/no_robots - Doctor-Shotgun/no-robots-sharegpt - Doctor-Shotgun/capybara-sharegpt --- # Norobara-ZLoss-8x7B This is an instruct-tuned [TinyLlama-1.1B-32k](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k) on several open-source instruct datasets, intended primarily for speculative decoding. ## Usage: The intended prompt format is a modified multi-turn Alpaca instruction format: ``` ### Instruction: {system prompt} ### Input: {user message} ### Response: {model response} ### Input: {user message} ### Response: {model response} (etc.) ``` ## Bias, Risks, and Limitations The model will show biases present in the base model. No ethical alignment was applied to prevent the generation of toxic or harmful outputs (in fact the opposite, with examples from toxic-DPO included), so generate at your own risk. ## Training Details This model was trained as a full finetune for 3 epochs using a single A100 GPU for around 3.5 hours.