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jacobhoffmann/TestGen_v2.1-codegemma-7b-lr3e-05_epochs2

jacobhoffmann

transformers

text-generation

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

eurecom-ds/scoresdeve-ema-conditional-celeba-64-young

eurecom-ds

Entry not found

RichardErkhov/rombodawg_-_Rombos-LLM-V2.5-Qwen-7b-gguf

RichardErkhov

Entry not found

tttx/problem226_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem226_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem226_model_more_aug_30 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. --> # problem226_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem226_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0327 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 47 | 0.0329 | | 0.0 | 2.0 | 94 | 0.0327 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

ND911/BLACK-MAGIC-FLUX-EDITION-GGUFs

ND911

--- base_model: - black-forest-labs/FLUX.1-dev --- GGUFs for the model [Black Magic Flux Edition](https://civitai.com/models/851440/black-magic-flux-edition-checkpoint) 

tttx/problem194_model_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem194_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem194_model_aug_30 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. --> # problem194_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem194_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0651 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0146 | 1.0 | 60 | 0.0693 | | 0.0156 | 2.0 | 120 | 0.0651 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

async0x42/Qwen2.5-Coder-32B-Instruct-exl2_4.5bpw

async0x42

transformers

text-generation

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-Coder-32B pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder --- # Qwen2.5-Coder-32B-Instruct ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```

itorgov/model-1731460555

itorgov

Entry not found

shuttleai/shuttle-3-diffusion-GGUF

shuttleai

--- language: - en license: apache-2.0 library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - image-generation - shuttle --- # Shuttle 3 Diffusion ## Model Variants These model variants provide different precision levels and formats optimized for diverse hardware capabilities and use cases - [bfloat16](https://huggingface.co/shuttleai/shuttle-3-diffusion) - [GGUF](https://huggingface.co/shuttleai/shuttle-3-diffusion-GGUF) - [fp8](https://huggingface.co/shuttleai/shuttle-3-diffusion-fp8) Shuttle 3 Diffusion is a text-to-image AI model designed to create detailed and diverse images from textual prompts in just 4 steps. It offers enhanced performance in image quality, typography, understanding complex prompts, and resource efficiency.  You can try out the model through a website at https://chat.shuttleai.com/images ## Using the model via API You can use Shuttle 3 Diffusion via API through ShuttleAI - [ShuttleAI](https://shuttleai.com/) - [ShuttleAI Docs](https://docs.shuttleai.com/) ## Using the model with 🧨 Diffusers Install or upgrade diffusers ```shell pip install -U diffusers ``` Then you can use `DiffusionPipeline` to run the model ```python import torch from diffusers import DiffusionPipeline # Load the diffusion pipeline from a pretrained model, using bfloat16 for tensor types. pipe = DiffusionPipeline.from_pretrained( "shuttleai/shuttle-3-diffusion", torch_dtype=torch.bfloat16 ).to("cuda") # Uncomment the following line to save VRAM by offloading the model to CPU if needed. # pipe.enable_model_cpu_offload() # Uncomment the lines below to enable torch.compile for potential performance boosts on compatible GPUs. # Note that this can increase loading times considerably. # pipe.transformer.to(memory_format=torch.channels_last) # pipe.transformer = torch.compile( # pipe.transformer, mode="max-autotune", fullgraph=True # ) # Set your prompt for image generation. prompt = "A cat holding a sign that says hello world" # Generate the image using the diffusion pipeline. image = pipe( prompt, height=1024, width=1024, guidance_scale=3.5, num_inference_steps=4, max_sequence_length=256, # Uncomment the line below to use a manual seed for reproducible results. # generator=torch.Generator("cpu").manual_seed(0) ).images[0] # Save the generated image. image.save("shuttle.png") ``` To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation ## Using the model with ComfyUI To run local inference with Shuttle 3 Diffusion using [ComfyUI](https://github.com/comfyanonymous/ComfyUI), you can use this [safetensors file](https://huggingface.co/shuttleai/shuttle-3-diffusion/blob/main/shuttle-3-diffusion.safetensors). ## Comparison to other models Shuttle 3 Diffusion can produce images better images than Flux Dev in just four steps, while being licensed under Apache 2.  [More examples](https://docs.shuttleai.com/getting-started/shuttle-diffusion) ## Training Details Shuttle 3 Diffusion uses Flux.1 Schnell as its base. It can produce images similar to Flux Dev or Pro in just 4 steps, and it is licensed under Apache 2. The model was partially de-distilled during training. When used beyond 10 steps, it enters "refiner mode," enhancing image details without altering the composition. We overcame the limitations of the Schnell-series models by employing a special training method, resulting in improved details and colors.

LRPxxx/fine-tuned-image-model

LRPxxx

transformers

image-classification

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

barchetta/cara-131216

barchetta

Entry not found

tensorblock/Qwama-0.5B-Instruct-GGUF

tensorblock

--- license: apache-2.0 base_model: turboderp/Qwama-0.5B-Instruct tags: - TensorBlock - GGUF --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" 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;"> Feedback and support: TensorBlock's <a href="https://x.com/tensorblock_aoi">Twitter/X</a>, <a href="https://t.me/TensorBlock">Telegram Group</a> and <a href="https://x.com/tensorblock_aoi">Discord server</a> </p> </div> </div> ## turboderp/Qwama-0.5B-Instruct - GGUF This repo contains GGUF format model files for [turboderp/Qwama-0.5B-Instruct](https://huggingface.co/turboderp/Qwama-0.5B-Instruct). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Prompt template ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|> ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [Qwama-0.5B-Instruct-Q2_K.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q2_K.gguf) | Q2_K | 0.296 GB | smallest, significant quality loss - not recommended for most purposes | | [Qwama-0.5B-Instruct-Q3_K_S.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q3_K_S.gguf) | Q3_K_S | 0.296 GB | very small, high quality loss | | [Qwama-0.5B-Instruct-Q3_K_M.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q3_K_M.gguf) | Q3_K_M | 0.312 GB | very small, high quality loss | | [Qwama-0.5B-Instruct-Q3_K_L.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q3_K_L.gguf) | Q3_K_L | 0.325 GB | small, substantial quality loss | | [Qwama-0.5B-Instruct-Q4_0.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q4_0.gguf) | Q4_0 | 0.309 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [Qwama-0.5B-Instruct-Q4_K_S.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q4_K_S.gguf) | Q4_K_S | 0.340 GB | small, greater quality loss | | [Qwama-0.5B-Instruct-Q4_K_M.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q4_K_M.gguf) | Q4_K_M | 0.351 GB | medium, balanced quality - recommended | | [Qwama-0.5B-Instruct-Q5_0.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q5_0.gguf) | Q5_0 | 0.350 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [Qwama-0.5B-Instruct-Q5_K_S.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q5_K_S.gguf) | Q5_K_S | 0.365 GB | large, low quality loss - recommended | | [Qwama-0.5B-Instruct-Q5_K_M.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q5_K_M.gguf) | Q5_K_M | 0.372 GB | large, very low quality loss - recommended | | [Qwama-0.5B-Instruct-Q6_K.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q6_K.gguf) | Q6_K | 0.452 GB | very large, extremely low quality loss | | [Qwama-0.5B-Instruct-Q8_0.gguf](https://huggingface.co/tensorblock/Qwama-0.5B-Instruct-GGUF/tree/main/Qwama-0.5B-Instruct-Q8_0.gguf) | Q8_0 | 0.475 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/Qwama-0.5B-Instruct-GGUF --include "Qwama-0.5B-Instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/Qwama-0.5B-Instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```

Jongbo/sdxl_base1_0_512_ema_no_train_down01_lr06_batch1_1000

Jongbo

--- base_model: stabilityai/stable-diffusion-xl-base-1.0 library_name: diffusers license: creativeml-openrail-m inference: true tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers-training - diffusers --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Text-to-image finetuning - Jongbo/sdxl_base1_0_512_ema_no_train_down01_lr06_batch1_1000 This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: beautiful scenery nature glass bottle landscape, purple galaxy bottle:     Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]

NanQiangHF/gemma2_9b_it_bwgenerator_pb

NanQiangHF

transformers

text-generation

--- library_name: transformers tags: - generated_from_trainer - trl - sft base_model: google/gemma-2-9b-it model_name: gemma2_9b_it_bwgenerator_pb licence: license --- # Model Card for gemma2_9b_it_bwgenerator_pb This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="NanQiangHF/gemma2_9b_it_bwgenerator_pb", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.12.0.dev0 - Transformers: 4.46.0.dev0 - Pytorch: 2.3.0 - Datasets: 3.0.0 - Tokenizers: 0.20.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

julian-fong/cifar100-adapterplus_config

julian-fong

adapter-transformers

--- tags: - vit - adapter-transformers datasets: - cifar100 --- # Adapter `julian-fong/cifar100-adapterplus_config` for google/vit-base-patch16-224-in21k An [adapter](https://adapterhub.ml) for the `google/vit-base-patch16-224-in21k` model that was trained on the [cifar100](https://huggingface.co/datasets/cifar100/) dataset and includes a prediction head for image classification. This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library. ## Usage First, install `adapters`: ``` pip install -U adapters ``` Now, the adapter can be loaded and activated like this: ```python from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("google/vit-base-patch16-224-in21k") adapter_name = model.load_adapter("julian-fong/cifar100-adapterplus_config", set_active=True) ``` ## Architecture & Training <!-- Add some description here --> ## Evaluation results <!-- Add some description here --> ## Citation <!-- Add some description here -->

james1122123/ffgfghgh

james1122123

Entry not found

mradermacher/datagemma-rig-27b-it-i1-GGUF

mradermacher

<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: nicoboss --> weighted/imatrix quants of https://huggingface.co/google/datagemma-rig-27b-it

TNTW/model-13110623

TNTW

Entry not found

tttx/problem244_model_more_aug_30

tttx

peft

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem244_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem244_model_more_aug_30 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. --> # problem244_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem244_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0234 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0 | 1.0 | 47 | 0.0217 | | 0.0 | 2.0 | 94 | 0.0234 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF

featherless-ai-quants

--- base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-GGUF/blob/main/UCLA-AGI-Llama-3-Instruct-8B-SPPO-Iter3-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

KaKee/llama-2-13b-chat_own_build_dataset_7th_stereo_version_1_2_3_4_subset_epoch1

KaKee

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

Gummybear05/wav2vec2-Y_speed3

Gummybear05

transformers

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

danigambit/M_ep2_run0_llama2-7b_tinystories_doc1000_tok25

danigambit

transformers

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

mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF

mradermacher

--- base_model: EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - llama - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/EpistemeAI/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q2_K.gguf) | Q2_K | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q3_K_S.gguf) | Q3_K_S | 3.8 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q3_K_L.gguf) | Q3_K_L | 4.4 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.IQ4_XS.gguf) | IQ4_XS | 4.6 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q5_K_S.gguf) | Q5_K_S | 5.7 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q5_K_M.gguf) | Q5_K_M | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q6_K.gguf) | Q6_K | 6.7 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto-GGUF/resolve/main/Fireball-Meta-Llama-3.1-8B-Instruct-Agent-0.003-128K-code-ds-auto.f16.gguf) | f16 | 16.2 | 16 bpw, overkill | 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 -->

shashikanth-a/model

shashikanth-a

Entry not found

DJMOON/textual_inversion_test_spr_01

DJMOON

Entry not found

itorgov/model-1731460937

itorgov

Entry not found

KaKee/llama-2-13b-chat_own_build_dataset_7th_version_1_2_subset_epoch1

KaKee

Entry not found

touhidulislam/BERTweet_retrain_2020_21

touhidulislam

transformers

fill-mask

--- library_name: transformers license: mit base_model: vinai/bertweet-base tags: - generated_from_trainer model-index: - name: BERTweet_retrain_2020_21 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. --> # BERTweet_retrain_2020_21 This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co/vinai/bertweet-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4913 ## 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: 1e-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 - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.9418 | 1.0 | 3006 | 2.6133 | | 2.7283 | 2.0 | 6012 | 2.5289 | | 2.6315 | 3.0 | 9018 | 2.5147 | ### Framework versions - Transformers 4.45.1 - Pytorch 2.1.0+cu121 - Datasets 3.0.1 - Tokenizers 0.20.0

qualcomm/EfficientNet-B4

qualcomm

pytorch

image-classification

--- library_name: pytorch license: bsd-3-clause pipeline_tag: image-classification tags: - backbone - android ---  # EfficientNet-B4: Optimized for Mobile Deployment ## Imagenet classifier and general purpose backbone EfficientNetB4 is a machine learning model that can classify images from the Imagenet dataset. It can also be used as a backbone in building more complex models for specific use cases. This model is an implementation of EfficientNet-B4 found [here](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py). This repository provides scripts to run EfficientNet-B4 on Qualcomm® devices. More details on model performance across various devices, can be found [here](https://aihub.qualcomm.com/models/efficientnet_b4). ### Model Details - **Model Type:** Image classification - **Model Stats:** - Model checkpoint: Imagenet - Input resolution: 380x380 - Number of parameters: 19.34M - Model size: 74.5 MB | Model | Device | Chipset | Target Runtime | Inference Time (ms) | Peak Memory Range (MB) | Precision | Primary Compute Unit | Target Model |---|---|---|---|---|---|---|---|---| | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | TFLITE | 3.624 ms | 0 - 3 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) | | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | QNN | 3.726 ms | 0 - 230 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) | | EfficientNet-B4 | Samsung Galaxy S23 | Snapdragon® 8 Gen 2 | ONNX | 3.571 ms | 0 - 50 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) | | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | TFLITE | 2.629 ms | 0 - 159 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) | | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | QNN | 2.694 ms | 0 - 27 MB | FP16 | NPU | [EfficientNet-B4.so](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.so) | | EfficientNet-B4 | Samsung Galaxy S24 | Snapdragon® 8 Gen 3 | ONNX | 2.589 ms | 0 - 164 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) | | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | TFLITE | 2.106 ms | 0 - 63 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) | | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | QNN | 2.548 ms | 0 - 25 MB | FP16 | NPU | Use Export Script | | EfficientNet-B4 | Snapdragon 8 Elite QRD | Snapdragon® 8 Elite | ONNX | 2.505 ms | 0 - 68 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) | | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | TFLITE | 3.61 ms | 0 - 2 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) | | EfficientNet-B4 | QCS8550 (Proxy) | QCS8550 Proxy | QNN | 3.321 ms | 1 - 2 MB | FP16 | NPU | Use Export Script | | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | TFLITE | 7.289 ms | 0 - 174 MB | FP16 | NPU | [EfficientNet-B4.tflite](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.tflite) | | EfficientNet-B4 | QCS8450 (Proxy) | QCS8450 Proxy | QNN | 7.403 ms | 0 - 34 MB | FP16 | NPU | Use Export Script | | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | QNN | 3.659 ms | 1 - 1 MB | FP16 | NPU | Use Export Script | | EfficientNet-B4 | Snapdragon X Elite CRD | Snapdragon® X Elite | ONNX | 3.728 ms | 47 - 47 MB | FP16 | NPU | [EfficientNet-B4.onnx](https://huggingface.co/qualcomm/EfficientNet-B4/blob/main/EfficientNet-B4.onnx) | ## Installation This model can be installed as a Python package via pip. ```bash pip install qai-hub-models ``` ## Configure Qualcomm® AI Hub to run this model on a cloud-hosted device Sign-in to [Qualcomm® AI Hub](https://app.aihub.qualcomm.com/) with your Qualcomm® ID. Once signed in navigate to `Account -> Settings -> API Token`. With this API token, you can configure your client to run models on the cloud hosted devices. ```bash qai-hub configure --api_token API_TOKEN ``` Navigate to [docs](https://app.aihub.qualcomm.com/docs/) for more information. ## Demo off target The package contains a simple end-to-end demo that downloads pre-trained weights and runs this model on a sample input. ```bash python -m qai_hub_models.models.efficientnet_b4.demo ``` The above demo runs a reference implementation of pre-processing, model inference, and post processing. **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.efficientnet_b4.demo ``` ### Run model on a cloud-hosted device In addition to the demo, you can also run the model on a cloud-hosted Qualcomm® device. This script does the following: * Performance check on-device on a cloud-hosted device * Downloads compiled assets that can be deployed on-device for Android. * Accuracy check between PyTorch and on-device outputs. ```bash python -m qai_hub_models.models.efficientnet_b4.export ``` ``` Profiling Results ------------------------------------------------------------ EfficientNet-B4 Device : Samsung Galaxy S23 (13) Runtime : TFLITE Estimated inference time (ms) : 3.6 Estimated peak memory usage (MB): [0, 3] Total # Ops : 482 Compute Unit(s) : NPU (482 ops) ``` ## How does this work? This [export script](https://aihub.qualcomm.com/models/efficientnet_b4/qai_hub_models/models/EfficientNet-B4/export.py) leverages [Qualcomm® AI Hub](https://aihub.qualcomm.com/) to optimize, validate, and deploy this model on-device. Lets go through each step below in detail: Step 1: **Compile model for on-device deployment** To compile a PyTorch model for on-device deployment, we first trace the model in memory using the `jit.trace` and then call the `submit_compile_job` API. ```python import torch import qai_hub as hub from qai_hub_models.models.efficientnet_b4 import # Load the model # Device device = hub.Device("Samsung Galaxy S23") ``` Step 2: **Performance profiling on cloud-hosted device** After compiling models from step 1. Models can be profiled model on-device using the `target_model`. Note that this scripts runs the model on a device automatically provisioned in the cloud. Once the job is submitted, you can navigate to a provided job URL to view a variety of on-device performance metrics. ```python profile_job = hub.submit_profile_job( model=target_model, device=device, ) ``` Step 3: **Verify on-device accuracy** To verify the accuracy of the model on-device, you can run on-device inference on sample input data on the same cloud hosted device. ```python input_data = torch_model.sample_inputs() inference_job = hub.submit_inference_job( model=target_model, device=device, inputs=input_data, ) on_device_output = inference_job.download_output_data() ``` With the output of the model, you can compute like PSNR, relative errors or spot check the output with expected output. **Note**: This on-device profiling and inference requires access to Qualcomm® AI Hub. [Sign up for access](https://myaccount.qualcomm.com/signup). ## Run demo on a cloud-hosted device You can also run the demo on-device. ```bash python -m qai_hub_models.models.efficientnet_b4.demo --on-device ``` **NOTE**: If you want running in a Jupyter Notebook or Google Colab like environment, please add the following to your cell (instead of the above). ``` %run -m qai_hub_models.models.efficientnet_b4.demo -- --on-device ``` ## Deploying compiled model to Android The models can be deployed using multiple runtimes: - TensorFlow Lite (`.tflite` export): [This tutorial](https://www.tensorflow.org/lite/android/quickstart) provides a guide to deploy the .tflite model in an Android application. - QNN (`.so` export ): This [sample app](https://docs.qualcomm.com/bundle/publicresource/topics/80-63442-50/sample_app.html) provides instructions on how to use the `.so` shared library in an Android application. ## View on Qualcomm® AI Hub Get more details on EfficientNet-B4's performance across various devices [here](https://aihub.qualcomm.com/models/efficientnet_b4). Explore all available models on [Qualcomm® AI Hub](https://aihub.qualcomm.com/) ## License * The license for the original implementation of EfficientNet-B4 can be found [here](https://github.com/pytorch/vision/blob/main/LICENSE). * The license for the compiled assets for on-device deployment can be found [here](https://qaihub-public-assets.s3.us-west-2.amazonaws.com/qai-hub-models/Qualcomm+AI+Hub+Proprietary+License.pdf) ## References * [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) * [Source Model Implementation](https://github.com/pytorch/vision/blob/main/torchvision/models/efficientnet.py) ## Community * Join [our AI Hub Slack community](https://aihub.qualcomm.com/community/slack) to collaborate, post questions and learn more about on-device AI. * For questions or feedback please [reach out to us](mailto:ai-hub-support@qti.qualcomm.com).

tttx/problem226_model_aug_30

tttx

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem226_data library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem226_model_aug_30 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. --> # problem226_model_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem226_data dataset. It achieves the following results on the evaluation set: - Loss: 0.0088 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0002 | 1.0 | 45 | 0.0098 | | 0.0001 | 2.0 | 90 | 0.0088 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

bau0221/1113_model_1

bau0221

transformers

--- base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** bau0221 - **License:** apache-2.0 - **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

net31/naschainv24_17856

net31

Invalid username or password.

barchetta/viso-131225

barchetta

Entry not found

KaKee/llama-2-13b-chat_own_build_dataset_7th_stereo_version_1_2_3_4_5_6_subset_epoch1

KaKee

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

jwoodrow22/llama3-finetuned_2024-11-12_17-26-06

jwoodrow22

Entry not found

benito14/1B_finetuned_llama3.2

benito14

transformers

--- base_model: unsloth/Llama-3.2-1B-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** benito14 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

ammarahd/amr

ammarahd

--- license: gpl-3.0 ---

pianosiwon/newlm

pianosiwon

Entry not found

featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF

featherless-ai-quants

--- base_model: grimjim/Llama-3-Oasis-v1-OAS-8B pipeline_tag: text-generation quantized_by: featherless-ai-quants --- # grimjim/Llama-3-Oasis-v1-OAS-8B GGUF Quantizations 🚀  *Optimized GGUF quantization files for enhanced model performance* > Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee. --- ## Available Quantizations 📊 | Quantization Type | File | Size | |-------------------|------|------| | IQ4_XS | [grimjim-Llama-3-Oasis-v1-OAS-8B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-IQ4_XS.gguf) | 4276.62 MB | | Q2_K | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q2_K.gguf) | 3031.86 MB | | Q3_K_L | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_L.gguf) | 4121.74 MB | | Q3_K_M | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_M.gguf) | 3832.74 MB | | Q3_K_S | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q3_K_S.gguf) | 3494.74 MB | | Q4_K_M | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q4_K_M.gguf) | 4692.78 MB | | Q4_K_S | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q4_K_S.gguf) | 4475.28 MB | | Q5_K_M | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q5_K_M.gguf) | 5467.40 MB | | Q5_K_S | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q5_K_S.gguf) | 5339.90 MB | | Q6_K | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q6_K.gguf) | 6290.44 MB | | Q8_0 | [grimjim-Llama-3-Oasis-v1-OAS-8B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/grimjim-Llama-3-Oasis-v1-OAS-8B-GGUF/blob/main/grimjim-Llama-3-Oasis-v1-OAS-8B-Q8_0.gguf) | 8145.11 MB | --- ## ⚡ Powered by [Featherless AI](https://featherless.ai) ### Key Features - 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly - 🛠️ **Zero Infrastructure** - No server setup or maintenance required - 📚 **Vast Compatibility** - Support for 2400+ models and counting - 💎 **Affordable Pricing** - Starting at just $10/month --- **Links:** [Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)

async0x42/Qwen2.5-Coder-32B-Instruct-exl2_4.0bpw

async0x42

--- license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct/blob/main/LICENSE language: - en base_model: - Qwen/Qwen2.5-Coder-32B pipeline_tag: text-generation library_name: transformers tags: - code - codeqwen - chat - qwen - qwen-coder --- # Qwen2.5-Coder-32B-Instruct ## Introduction Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o. - A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies. - **Long-context Support** up to 128K tokens. **This repo contains the instruction-tuned 32B Qwen2.5-Coder model**, which has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 32.5B - Number of Paramaters (Non-Embedding): 31.0B - Number of Layers: 64 - Number of Attention Heads (GQA): 40 for Q and 8 for KV - Context Length: Full 131,072 tokens - Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts. For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186). ## Requirements The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.37.0`, you will encounter the following error: ``` KeyError: 'qwen2' ``` ## Quickstart Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen2.5-Coder-32B-Instruct" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "write a quick sort algorithm." messages = [ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) generated_ids = model.generate( **model_inputs, max_new_tokens=512 ) generated_ids = [ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) ] response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] ``` ### Processing Long Texts The current `config.json` is set for context length up to 32,768 tokens. To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts. For supported frameworks, you could add the following to `config.json` to enable YaRN: ```json { ..., "rope_scaling": { "factor": 4.0, "original_max_position_embeddings": 32768, "type": "yarn" } } ``` For deployment, we recommend using vLLM. Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM. Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**. We advise adding the `rope_scaling` configuration only when processing long contexts is required. ## Evaluation & Performance Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/). For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html). ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{hui2024qwen2, title={Qwen2. 5-Coder Technical Report}, author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others}, journal={arXiv preprint arXiv:2409.12186}, year={2024} } @article{qwen2, title={Qwen2 Technical Report}, author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan}, journal={arXiv preprint arXiv:2407.10671}, year={2024} } ```

itorgov/model-1731461312

itorgov

Entry not found

tttx/problem301_model_more_aug_30

tttx

--- base_model: barc0/Llama-3.1-ARC-Potpourri-Transduction-8B datasets: - tttx/problem301_data_more_aug library_name: peft license: llama3.1 tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: problem301_model_more_aug_30 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. --> # problem301_model_more_aug_30 This model is a fine-tuned version of [barc0/Llama-3.1-ARC-Potpourri-Transduction-8B](https://huggingface.co/barc0/Llama-3.1-ARC-Potpourri-Transduction-8B) on the tttx/problem301_data_more_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.0338 ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0001 | 1.0 | 62 | 0.0320 | | 0.0 | 2.0 | 124 | 0.0338 | ### Framework versions - PEFT 0.13.2 - Transformers 4.47.0.dev0 - Pytorch 2.4.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3

growwithdaisy/crrllcrrllxovrtn_styles_20241112_172347

growwithdaisy

--- license: other base_model: "FLUX.1-dev" tags: - flux - flux-diffusers - text-to-image - diffusers - simpletuner - not-for-all-audiences - lora - template:sd-lora - lycoris inference: true widget: - text: 'unconditional (blank prompt)' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_0_0.png - text: 'jmmymrbl style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_1_0.png - text: 'jmmymrbl style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_2_0.png - text: 'jmmymrbl style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_3_0.png - text: 'jmmymrbl style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_4_0.png - text: 'jmmymrbl cutup style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_5_0.png - text: 'jmmymrbl cutup style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_6_0.png - text: 'jmmymrbl cutup style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_7_0.png - text: 'jmmymrbl cutup style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_8_0.png - text: 'jmmymrbl earthly delights style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_9_0.png - text: 'jmmymrbl earthly delights style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_10_0.png - text: 'jmmymrbl earthly delights style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_11_0.png - text: 'jmmymrbl earthly delights style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_12_0.png - text: 'jmmymrbl dunes style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_13_0.png - text: 'jmmymrbl dunes style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_14_0.png - text: 'jmmymrbl dunes style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_15_0.png - text: 'jmmymrbl dunes style' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_16_0.png - text: 'a photo of a daisy' parameters: negative_prompt: 'blurry, cropped, ugly' output: url: ./assets/image_17_0.png --- # growwithdaisy/crrllcrrllxovrtn_styles_20241112_172347 This is a LyCORIS adapter derived from [FLUX.1-dev](https://huggingface.co/FLUX.1-dev). The main validation prompt used during training was: ``` a photo of a daisy ``` ## Validation settings - CFG: `3.5` - CFG Rescale: `0.0` - Steps: `20` - Sampler: `None` - Seed: `69` - Resolution: `1024x1024` Note: The validation settings are not necessarily the same as the [training settings](#training-settings). You can find some example images in the following gallery: <Gallery /> The text encoder **was not** trained. You may reuse the base model text encoder for inference. ## Training settings - Training epochs: 30 - Training steps: 1500 - Learning rate: 0.0002 - Max grad norm: 2.0 - Effective batch size: 8 - Micro-batch size: 1 - Gradient accumulation steps: 1 - Number of GPUs: 8 - Prediction type: flow-matching (extra parameters=['shift=3', 'flux_guidance_value=1.0']) - Rescaled betas zero SNR: False - Optimizer: optimi-stableadamwweight_decay=1e-3 - Precision: Pure BF16 - Quantised: No - Xformers: Not used - LyCORIS Config: ```json { "algo": "lokr", "multiplier": 1, "linear_dim": 1000000, "linear_alpha": 1, "factor": 16, "init_lokr_norm": 0.001, "apply_preset": { "target_module": [ "FluxTransformerBlock", "FluxSingleTransformerBlock" ], "module_algo_map": { "Attention": { "factor": 16 }, "FeedForward": { "factor": 8 } } } } ``` ## Datasets ### jmmymrbl_general_style-512 - Repeats: 0 - Total number of images: ~56 - Total number of aspect buckets: 4 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_general_style-768 - Repeats: 0 - Total number of images: ~56 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_cutup_style-512 - Repeats: 0 - Total number of images: ~32 - Total number of aspect buckets: 1 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_cutup_style-768 - Repeats: 0 - Total number of images: ~32 - Total number of aspect buckets: 2 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_earthly_delights_style-512 - Repeats: 0 - Total number of images: ~32 - Total number of aspect buckets: 4 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_earthly_delights_style-768 - Repeats: 0 - Total number of images: ~40 - Total number of aspect buckets: 5 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_earthly_delights_style-1024 - Repeats: 0 - Total number of images: ~56 - Total number of aspect buckets: 7 - Resolution: 1.048576 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_dunes_style-512 - Repeats: 0 - Total number of images: ~48 - Total number of aspect buckets: 4 - Resolution: 0.262144 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ### jmmymrbl_dunes_style-768 - Repeats: 0 - Total number of images: ~40 - Total number of aspect buckets: 1 - Resolution: 0.589824 megapixels - Cropped: False - Crop style: None - Crop aspect: None - Used for regularisation data: No ## Inference ```python import torch from diffusers import DiffusionPipeline from lycoris import create_lycoris_from_weights def download_adapter(repo_id: str): import os from huggingface_hub import hf_hub_download adapter_filename = "pytorch_lora_weights.safetensors" cache_dir = os.environ.get('HF_PATH', os.path.expanduser('~/.cache/huggingface/hub/models')) cleaned_adapter_path = repo_id.replace("/", "_").replace("\\", "_").replace(":", "_") path_to_adapter = os.path.join(cache_dir, cleaned_adapter_path) path_to_adapter_file = os.path.join(path_to_adapter, adapter_filename) os.makedirs(path_to_adapter, exist_ok=True) hf_hub_download( repo_id=repo_id, filename=adapter_filename, local_dir=path_to_adapter ) return path_to_adapter_file model_id = 'FLUX.1-dev' adapter_repo_id = 'playerzer0x/growwithdaisy/crrllcrrllxovrtn_styles_20241112_172347' adapter_filename = 'pytorch_lora_weights.safetensors' adapter_file_path = download_adapter(repo_id=adapter_repo_id) pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.bfloat16) # loading directly in bf16 lora_scale = 1.0 wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_file_path, pipeline.transformer) wrapper.merge_to() prompt = "a photo of a daisy" ## Optional: quantise the model to save on vram. ## Note: The model was not quantised during training, so it is not necessary to quantise it during inference time. #from optimum.quanto import quantize, freeze, qint8 #quantize(pipeline.transformer, weights=qint8) #freeze(pipeline.transformer) pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu') # the pipeline is already in its target precision level image = pipeline( prompt=prompt, num_inference_steps=20, generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826), width=1024, height=1024, guidance_scale=3.5, ).images[0] image.save("output.png", format="PNG") ```

KaKee/llama-2-13b-chat_own_build_dataset_7th_stereo_version_1_2_3_4_5_6_7_8_subset_epoch1

KaKee

Entry not found

PrParadoxy/q-FrozenLake-v1-4x4-noSlippery

PrParadoxy

reinforcement-learning

--- tags: - FrozenLake-v1-4x4-no_slippery - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-noSlippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4-no_slippery type: FrozenLake-v1-4x4-no_slippery metrics: - type: mean_reward value: 1.00 +/- 0.00 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **FrozenLake-v1** This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** . ## Usage ```python model = load_from_hub(repo_id="PrParadoxy/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

LOOKY3/133

LOOKY3

Entry not found

Beka-pika/mms_kaz_tts_surprise

Beka-pika

transformers

text-to-audio

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PrParadoxy/Taxi-v3

PrParadoxy

reinforcement-learning

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.52 +/- 2.72 name: mean_reward verified: false --- # **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="PrParadoxy/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

KaKee/llama-2-13b-chat_own_build_dataset_7th_version_1_2_3_4_subset_epoch1

KaKee

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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benito14/SOIT_Llama3.2_model4

benito14

transformers

--- base_model: unsloth/Llama-3.2-1B-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** benito14 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Llama-3.2-1B-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

itorgov/model-1731461561

itorgov

Entry not found

Kelllll/Llama-2-7b-chat-finetune

Kelllll

transformers

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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barchetta/soff-131232

barchetta

Entry not found

barchetta/baco-131233

barchetta

Entry not found

saqqdy/Qwen-Qwen1.5-0.5B-1731461591

saqqdy

peft

--- base_model: Qwen/Qwen1.5-0.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2

minhaozhang/Llama-3.2-1B-Instruct-MBTI-JP

minhaozhang

Entry not found

mradermacher/Mistral-quiet-star-demo-GGUF

mradermacher

--- base_model: liminerity/Mistral-quiet-star-demo datasets: - gate369/Alpaca-Star language: - en library_name: transformers license: apache-2.0 quantized_by: mradermacher tags: - text-generation-inference - transformers - unsloth - mistral - trl --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/liminerity/Mistral-quiet-star-demo <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## 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/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q2_K.gguf) | Q2_K | 2.8 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q3_K_S.gguf) | Q3_K_S | 3.3 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q3_K_L.gguf) | Q3_K_L | 3.9 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.IQ4_XS.gguf) | IQ4_XS | 4.0 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q5_K_S.gguf) | Q5_K_S | 5.1 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q5_K_M.gguf) | Q5_K_M | 5.2 | | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q6_K.gguf) | Q6_K | 6.0 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Mistral-quiet-star-demo-GGUF/resolve/main/Mistral-quiet-star-demo.f16.gguf) | f16 | 14.6 | 16 bpw, overkill | 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 -->

huyhoangt2201/llama-3.2-1b-chat-sql3-merged

huyhoangt2201

--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]