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pipeline_tag stringclasses 48 values	library_name stringclasses 205 values	text stringlengths 0 18.3M	metadata stringlengths 2 1.07B	id stringlengths 5 122	last_modified null	tags sequencelengths 1 1.84k	sha null	created_at stringlengths 25 25
null	null		{}	Rz1010/tinyllama_duplicatedx3	null	[ "region:us" ]	null	2024-05-02T19:24:31+00:00
null	transformers	This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]	{"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]}	UphamProjects/STT-Gated_TCN-12M	null	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:25:24+00:00
null	null		{}	cacaaaaaaaa/gingerkon	null	[ "region:us" ]	null	2024-05-02T19:27:16+00:00
null	null		{"license": "other", "license_name": "yi-license", "license_link": "LICENSE"}	adamo1139/Yi-34B-200K-XLCTX-AEZAKMI-RAW-TOXIC-ORPO-LoRA-0205	null	[ "safetensors", "license:other", "region:us" ]	null	2024-05-02T19:27:51+00:00
text2text-generation	transformers	<!-- 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. --> # mbart_extratranslations2 This model is a fine-tuned version of [NegarSH/mbart_extratranslations](https://huggingface.co/NegarSH/mbart_extratranslations) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"tags": ["generated_from_trainer"], "base_model": "NegarSH/mbart_extratranslations", "model-index": [{"name": "mbart_extratranslations2", "results": []}]}	NegarSH/mbart_extratranslations2	null	[ "transformers", "tensorboard", "safetensors", "mbart", "text2text-generation", "generated_from_trainer", "base_model:NegarSH/mbart_extratranslations", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:28:16+00:00
text-to-image	diffusers	# 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 🧨 diffusers 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]	{"library_name": "diffusers"}	rubbrband/ambienceSDXL_a1	null	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-05-02T19:29:03+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Atom-7B-Chat - bnb 8bits - Model creator: https://huggingface.co/FlagAlpha/ - Original model: https://huggingface.co/FlagAlpha/Atom-7B-Chat/ Original model description: --- developers: [https://huggingface.co/FlagAlphaAI] license: apache-2.0 language: - zh - en pipeline_tag: question-answering library_name: transformers --- # Atom-7B-32k-Chat 基于Atom-7B具有32k长度的对话模型，完全开源可商用，由Llama中文社区和AtomEcho（原子回声）联合研发，基于Llama2-7B采用大规模的中文数据进行了继续预训练，我们会持续提供更新的模型参数，模型训练过程见[llama.family](https://llama.family)。模型的部署、训练、微调等方法详见Llama中文社区GitHub仓库：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese)。 ## 📝 中文数据 \| 类型 \| 描述 \| \| ---------------------------------------------------------- \| ------------------------------------------------------------ \| \| 网络数据 \| 互联网上公开的网络数据，挑选出去重后的高质量中文数据，涉及到百科、书籍、博客、新闻、公告、小说等高质量长文本数据。 \| \| [Wikipedia](https://github.com/goldsmith/Wikipedia) \| 中文Wikipedia的数据 \| \| [悟道](https://github.com/BAAI-WuDao/Model) \| 中文悟道开源的200G数据 \| \| [Clue](https://github.com/CLUEbenchmark/CLUEDatasetSearch) \| Clue开放的中文预训练数据，进行清洗后的高质量中文长文本数据 \| \| 竞赛数据集 \| 近年来中文自然语言处理多任务竞赛数据集，约150个 \| \| [MNBVC](https://github.com/esbatmop/MNBVC) \| MNBVC 中清洗出来的部分数据集 \| 我们也欢迎大家在[llama.family](https://llama.family)中贡献自己的数据，您的数据通过审核后会加入模型训练，也将影响模型未来的能力走向。 ## 📚 中文词表为了提高中文文本处理的效率，我们针对Llama2模型的词表进行了深度优化。首先，我们基于数百G的中文文本，在Llama2词表的基础上扩展词库至65,000个单词。经过测试，我们的改进使得中文编码/解码速度提高了约350％。此外，我们还扩大了中文字符集的覆盖范围，包括所有emoji符号，这使的生成带有表情符号的文章更加高效。对于Llama2原生词表中的一些特殊情况，如数字、英文等，我们尽可能地避免对其进行修改或替换。最终，成功地实现了一种既能提高中文处理效率又能保持Llama2原有性能的方法。 ## 📈 训练过程模型结构基于当前最优秀的开源模型Llama2，使用主流Decoder-only的标准Transformer网络结构，支持4K的上下文长度（Context Length），为同尺寸模型中最长，能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。 FlashAttention-2高效训练 Atom-7B采用了FlashAttention-2技术进行训练。由于在处理较长的输入序列时，内存消耗的问题可能会导致“内存爆炸”现象。FlashAttention-2是一种高效注意力机制的实现方式之一，相较于传统的注意力技术（Attention），它拥有更快速的速度以及更加优化的内存占用率。基于NTK的自适应上下文扩展技术 - 可在不继续训练模型的情况下支持更长的上下文 - 本项目中模型默认支持4K上下文，利用上述技术可扩展至18K+ - 经过微调可以支持到32K+ ## 💻 推理配置实际应用中，消费级显卡要比专业显卡便宜的多（比如3090相比A10，同样都是24G显存）。对于消费级显卡，直接FP32肯定放不下，一般最基本的是FP16，而INT8和INT4量化就很有用，例如： - 对于3080显卡（10G显存），Atom-7B的INT8只需要8G显存可以直接部署。 - 对于3080显卡（10G显存），Atom-7B的INT4只需要5G显存可以直接部署。 --- # Llama中文社区 ## 🚀 社区地址： Github：[Llama-Chinese](https://github.com/LlamaFamily/Llama-Chinese) 在线体验链接：[llama.family](https://llama.family/) ## 🔥 社区介绍欢迎来到Llama中文社区！我们是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级。我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。 ## 🐼 社区资源 - Llama2在线体验链接[llama.family](https://llama.family/)，同时包含Meta原版和中文微调版本！ - Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！ - [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！	{}	RichardErkhov/FlagAlpha_-_Atom-7B-Chat-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-02T19:30:15+00:00
text-generation	transformers		{}	Tristan/pythia-70m-fr-language-as-domain	null	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:30:27+00:00
null	null		{}	cacaaaaaaaa/based66	null	[ "region:us" ]	null	2024-05-02T19:31:40+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) openchat-3.5-0106 - GGUF - Model creator: https://huggingface.co/openchat/ - Original model: https://huggingface.co/openchat/openchat-3.5-0106/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [openchat-3.5-0106.Q2_K.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [openchat-3.5-0106.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [openchat-3.5-0106.IQ3_S.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [openchat-3.5-0106.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [openchat-3.5-0106.IQ3_M.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [openchat-3.5-0106.Q3_K.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [openchat-3.5-0106.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [openchat-3.5-0106.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [openchat-3.5-0106.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [openchat-3.5-0106.Q4_0.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [openchat-3.5-0106.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [openchat-3.5-0106.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [openchat-3.5-0106.Q4_K.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [openchat-3.5-0106.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [openchat-3.5-0106.Q4_1.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [openchat-3.5-0106.Q5_0.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [openchat-3.5-0106.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [openchat-3.5-0106.Q5_K.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [openchat-3.5-0106.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [openchat-3.5-0106.Q5_1.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [openchat-3.5-0106.Q6_K.gguf](https://huggingface.co/RichardErkhov/openchat_-_openchat-3.5-0106-gguf/blob/main/openchat-3.5-0106.Q6_K.gguf) \| Q6_K \| 5.53GB \| Original model description: --- license: apache-2.0 base_model: mistralai/Mistral-7B-v0.1 tags: - openchat - mistral - C-RLFT library_name: transformers pipeline_tag: text-generation --- <div align="center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%"> <h1>Advancing Open-source Language Models with Mixed-Quality Data</h1> </div> <p align="center" style="margin-top: 0px;"> <a href="https://openchat.team"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/logo_nobg.png?raw=true" alt="OpenChat Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 10px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">Online Demo</span> </a> \| <a href="https://github.com/imoneoi/openchat"> <img src="https://github.githubassets.com/assets/GitHub-Mark-ea2971cee799.png" alt="GitHub Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style=" margin-right: 5px;">GitHub</span> </a> \| <a href="https://arxiv.org/pdf/2309.11235.pdf"> <img src="https://github.com/alpayariyak/openchat/blob/master/assets/arxiv-logomark-small-square-border.png?raw=true" alt="ArXiv Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text" style="margin-right: 5px;">Paper</span> </a> \| <a href="https://discord.gg/pQjnXvNKHY"> <img src="https://cloud.githubusercontent.com/assets/6291467/26705903/96c2d66e-477c-11e7-9f4e-f3c0efe96c9a.png" alt="Discord Logo" style="width:20px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> <span class="link-text">Discord</span> </a> </p> <p align="center" style="margin-top: 0px;"> <span class="link-text" style=" margin-right: 0px; font-size: 0.8em">Sponsored by RunPod</span> <img src="https://styles.redditmedia.com/t5_6075m3/styles/profileIcon_71syco7c5lt81.png?width=256&height=256&frame=1&auto=webp&crop=256:256,smart&s=24bd3c71dc11edc5d4f88d0cbc1da72ed7ae1969" alt="RunPod Logo" style="width:30px; vertical-align: middle; display: inline-block; margin-right: 5px; margin-left: 5px; margin-top: 0px; margin-bottom: 0px;"/> </p> <div style="background-color: white; padding: 0.7em; border-radius: 0.5em; color: black; display: flex; flex-direction: column; justify-content: center; text-align: center; ont-size: 0.5em; border: 0.8em solid #864AF9;"> <a href="https://huggingface.co/openchat/openchat-3.5-0106" style="text-decoration: none; color: black;"> <span style="font-size: 1.7em; font-family: 'Helvetica'; letter-spacing: 0.1em; font-weight: bold; color: black;">OPENCHAT</span><span style="font-size: 1.8em; font-family: 'Helvetica'; color: #3c72db; ">3.5</span> <span style="font-size: 1.0em; font-family: 'Helvetica'; color: white; background-color: #864AF9; vertical-align: top; border-radius: 6em; padding: 0.066em 0.4em; letter-spacing: 0.1em; font-weight: bold;">0106</span> <span style="font-size: 0.85em; font-family: 'Helvetica'; color: black;"> <br> 🏆 The Overall Best Performing Open Source 7B Model 🏆 <br> 🤖 Outperforms <span style="font-weight: bold;">ChatGPT</span> (March) and <span style="font-weight: bold;">Grok-1</span> 🤖 <br> 🚀<span style="font-size: 1em; font-family: 'Helvetica'; color: black; font-weight: bold;">15</span>-point improvement in Coding over <span style="font-size: 0.9em; font-family: 'Helvetica'; color: black; font-weight: bold;">OpenChat-3.5🚀</span> <br><br><span style="font-size: 1em; font-family: 'Helvetica'; color: #3c72db; font-weight: bold;">New Features</span> <br> 💡 2 Modes: Coding + Generalist, Mathematical Reasoning 💡 <br> 🧑‍⚖️ Experimental support for Evaluator and Feedback capabilities 🧑‍⚖️ </span> </a> </div> <div style="display: flex; justify-content: center; align-items: center"> <img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/openchat-bench-0106.png" style="width: 100%; border-radius: 1em"> </div> <div> <h3> Table of Contents</h3> </div> 1. [Usage](#usage) 2. [Benchmarks](#benchmarks) 3. [Limitations](#limitations) 4. [License](#license) 6. [Citation](#citation) 7. [Acknowledgements](#acknowledgements) <div align="center"> <h2> Usage </h2> </div> To use this model, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat#installation) in our repository and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a consumer GPU with 24GB RAM. To enable tensor parallelism, append `--tensor-parallel-size N` to the serving command. Once started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). Please refer to the example request below for reference. Additionally, you can use the [OpenChat Web UI](https://github.com/imoneoi/openchat#web-ui) for a user-friendly experience. If you want to deploy the server as an online service, you can use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. For security purposes, we recommend using an [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server. \| Model \| Size \| Context \| Weights \| Serving \| \|-------------------\|------\|---------\|------------------------------------------------------------------\|------------------------------------------------------------------------------------------------------------------\| \| OpenChat-3.5-0106 \| 7B \| 8192 \| [Huggingface](https://huggingface.co/openchat/openchat-3.5-0106) \| `python -m ochat.serving.openai_api_server --model openchat/openchat-3.5-0106 --engine-use-ray --worker-use-ray` \| <details> <summary>Example request (click to expand)</summary> 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}] }' ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ```bash curl http://localhost:18888/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "openchat_3.5", "condition": "Math Correct", "messages": [{"role": "user", "content": "10.3 − 7988.8133 = "}] }' ``` </details> ### Conversation templates 💡 Default Mode (GPT4 Correct): Best for coding, chat and general tasks ``` GPT4 Correct User: Hello<\|end_of_turn\|>GPT4 Correct Assistant: Hi<\|end_of_turn\|>GPT4 Correct User: How are you today?<\|end_of_turn\|>GPT4 Correct Assistant: ``` 🧮 Mathematical Reasoning Mode: Tailored for solving math problems ``` Math Correct User: 10.3 − 7988.8133=<\|end_of_turn\|>Math Correct Assistant: ``` ⚠️ Notice: Remember to set `<\|end_of_turn\|>` as end of generation token. The default (GPT4 Correct) template is also available as the integrated `tokenizer.chat_template`, which can be used instead of manually specifying the template: ```python messages = [ {"role": "user", "content": "Hello"}, {"role": "assistant", "content": "Hi"}, {"role": "user", "content": "How are you today?"} ] tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True) assert tokens == [1, 420, 6316, 28781, 3198, 3123, 1247, 28747, 22557, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747, 15359, 32000, 420, 6316, 28781, 3198, 3123, 1247, 28747, 1602, 460, 368, 3154, 28804, 32000, 420, 6316, 28781, 3198, 3123, 21631, 28747] ``` <div align="center"> <h2> (Experimental) Evaluator / Feedback Capabilities </h2> </div> We've included evaluator capabilities in this release to advance open-source models as evaluators. You can use `Default Mode (GPT4 Correct)` with the following prompt (same as [Prometheus](https://huggingface.co/datasets/kaist-ai/Feedback-Collection)) to evaluate a response. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {orig_instruction} ###Response to evaluate: {orig_response} ###Reference Answer (Score 5): {orig_reference_answer} ###Score Rubrics: [{orig_criteria}] Score 1: {orig_score1_description} Score 2: {orig_score2_description} Score 3: {orig_score3_description} Score 4: {orig_score4_description} Score 5: {orig_score5_description} ###Feedback: ``` <div align="center"> <h2> Benchmarks </h2> </div> \| Model \| # Params \| Average \| MT-Bench \| HumanEval \| BBH MC \| AGIEval \| TruthfulQA \| MMLU \| GSM8K \| BBH CoT \| \|-----------------------\|----------\|----------\|----------\|-----------\|----------\|----------\|------------\|----------\|----------\|----------\| \| OpenChat-3.5-0106 \| 7B \| 64.5 \| 7.8 \| 71.3 \| 51.5 \| 49.1 \| 61.0 \| 65.8 \| 77.4 \| 62.2 \| \| OpenChat-3.5-1210 \| 7B \| 63.8 \| 7.76 \| 68.9 \| 49.5 \| 48.0 \| 61.8 \| 65.3 \| 77.3 \| 61.8 \| \| OpenChat-3.5 \| 7B \| 61.6 \| 7.81 \| 55.5 \| 47.6 \| 47.4 \| 59.1 \| 64.3 \| 77.3 \| 63.5 \| \| ChatGPT (March)* \| ???B \| 61.5 \| 7.94 \| 48.1 \| 47.6 \| 47.1 \| 57.7 \| 67.3 \| 74.9 \| 70.1 \| \| \| \| \| \| \| \| \| \| \| \| \| \| OpenHermes 2.5 \| 7B \| 59.3 \| 7.54 \| 48.2 \| 49.4 \| 46.5 \| 57.5 \| 63.8 \| 73.5 \| 59.9 \| \| OpenOrca Mistral \| 7B \| 52.7 \| 6.86 \| 38.4 \| 49.4 \| 42.9 \| 45.9 \| 59.3 \| 59.1 \| 58.1 \| \| Zephyr-β^ \| 7B \| 34.6 \| 7.34 \| 22.0 \| 40.6 \| 39.0 \| 40.8 \| 39.8 \| 5.1 \| 16.0 \| \| Mistral \| 7B \| - \| 6.84 \| 30.5 \| 39.0 \| 38.0 \| - \| 60.1 \| 52.2 \| - \| <details> <summary>Evaluation Details(click to expand)</summary> : ChatGPT (March) results are from [GPT-4 Technical Report](https://arxiv.org/abs/2303.08774), [Chain-of-Thought Hub](https://github.com/FranxYao/chain-of-thought-hub), and our evaluation. Please note that ChatGPT is not a fixed baseline and evolves rapidly over time. ^: Zephyr-β often fails to follow few-shot CoT instructions, likely because it was aligned with only chat data but not trained on few-shot data. : Mistral and Open-source SOTA results are taken from reported results in instruction-tuned model papers and official repositories. All models are evaluated in chat mode (e.g. with the respective conversation template applied). All zero-shot benchmarks follow the same setting as in the AGIEval paper and Orca paper. CoT tasks use the same configuration as Chain-of-Thought Hub, HumanEval is evaluated with EvalPlus, and MT-bench is run using FastChat. To reproduce our results, follow the instructions in [our repository](https://github.com/imoneoi/openchat/#benchmarks). </details> <div> <h3>HumanEval+</h3> </div> \| Model \| Size \| HumanEval+ pass@1 \| \|-----------------------------\|--------\|-------------------\| \| OpenChat-3.5-0106* \| 7B \| 65.9 \| \| ChatGPT (December 12, 2023) \| ???B \| 64.6 \| \| WizardCoder-Python-34B-V1.0 \| 34B \| 64.6 \| \| OpenChat 3.5 1210 \| 7B \| 63.4 \| \| OpenHermes 2.5 \| 7B \| 41.5 \| <div> <h3>OpenChat-3.5 vs. Grok</h3> </div> 🔥 OpenChat-3.5-0106 (7B) now outperforms Grok-0 (33B) on all 4 benchmarks and Grok-1 (???B) on average and 3/4 benchmarks. \| \| License \| # Param \| Average \| MMLU \| HumanEval \| MATH \| GSM8k \| \|-----------------------\|-------------\|---------\|----------\|--------\|-----------\|----------\|----------\| \| OpenChat-3.5-0106 \| Apache-2.0 \| 7B \| 61.0 \| 65.8 \| 71.3 \| 29.3 \| 77.4 \| \| OpenChat-3.5-1210 \| Apache-2.0 \| 7B \| 60.1 \| 65.3 \| 68.9 \| 28.9 \| 77.3 \| \| OpenChat-3.5 \| Apache-2.0 \| 7B \| 56.4 \| 64.3 \| 55.5 \| 28.6 \| 77.3 \| \| Grok-0 \| Proprietary \| 33B \| 44.5 \| 65.7 \| 39.7 \| 15.7 \| 56.8 \| \| Grok-1 \| Proprietary \| ???B \| 55.8 \| 73 \| 63.2 \| 23.9 \| 62.9 \| : Grok results are reported by [X.AI](https://x.ai/). <div align="center"> <h2> Limitations </h2> </div> Foundation Model Limitations* Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as: - Complex reasoning - Mathematical and arithmetic tasks - Programming and coding challenges Hallucination of Non-existent Information OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model. Safety OpenChat may sometimes generate harmful, hate speech, biased responses, or answer unsafe questions. It's crucial to apply additional AI safety measures in use cases that require safe and moderated responses. <div align="center"> <h2> License </h2> </div> Our OpenChat 3.5 code and models are distributed under the Apache License 2.0. <div align="center"> <h2> Citation </h2> </div> ``` @article{wang2023openchat, title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data}, author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang}, journal={arXiv preprint arXiv:2309.11235}, year={2023} } ``` <div align="center"> <h2> 💌 Contact </h2> </div> We look forward to hearing you and collaborating on this exciting project! Project Lead: - Guan Wang [imonenext at gmail dot com] - [Alpay Ariyak](https://github.com/alpayariyak) [aariyak at wpi dot edu] Main Contributors: - [Sijie Cheng](https://adacheng.github.io/) [csj23 at mails dot tsinghua dot edu dot cn] - LDJ - AutoMeta (Alignment Lab AI)	{}	RichardErkhov/openchat_-_openchat-3.5-0106-gguf	null	[ "gguf", "arxiv:2309.11235", "arxiv:2303.08774", "region:us" ]	null	2024-05-02T19:32:25+00:00
text2text-generation	transformers	<!-- 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. --> # t5-small-finetuned-wikiauto This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the wiki_auto dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 438 \| 0.9575 \| 69.8875 \| 54.8405 \| 66.8956 \| 66.9053 \| 17.4488 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["wiki_auto"], "base_model": "google-t5/t5-small", "model-index": [{"name": "t5-small-finetuned-wikiauto", "results": []}]}	hiramochoavea/t5-small-finetuned-wikiauto	null	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:wiki_auto", "base_model:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:33:07+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	ms6641/gpt2-imdb-pos-v2	null	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:33:59+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	kaitchup/OpenELM-270M-oasstguanaco-2e-ORPO	null	[ "transformers", "safetensors", "openelm", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	null	2024-05-02T19:35:20+00:00
text-generation	transformers		{"license": "openrail"}	usman33/Llama-2-7b-chat-finetune-KidzAi	null	[ "transformers", "pytorch", "llama", "text-generation", "license:openrail", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:36:07+00:00
text-generation	null	## Llamacpp imatrix Quantizations of Einstein-v6.1-Llama3-8B Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2777">b2777</a> for quantization. Original model: https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Download a file (not the whole branch) from below: \| Filename \| Quant type \| File Size \| Description \| \| -------- \| ---------- \| --------- \| ----------- \| \| [Einstein-v6.1-Llama3-8B-Q8_0.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q8_0.gguf) \| Q8_0 \| 8.54GB \| Extremely high quality, generally unneeded but max available quant. \| \| [Einstein-v6.1-Llama3-8B-Q6_K.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q6_K.gguf) \| Q6_K \| 6.59GB \| Very high quality, near perfect, recommended. \| \| [Einstein-v6.1-Llama3-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q5_K_M.gguf) \| Q5_K_M \| 5.73GB \| High quality, recommended. \| \| [Einstein-v6.1-Llama3-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q5_K_S.gguf) \| Q5_K_S \| 5.59GB \| High quality, recommended. \| \| [Einstein-v6.1-Llama3-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q4_K_M.gguf) \| Q4_K_M \| 4.92GB \| Good quality, uses about 4.83 bits per weight, recommended. \| \| [Einstein-v6.1-Llama3-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q4_K_S.gguf) \| Q4_K_S \| 4.69GB \| Slightly lower quality with more space savings, recommended. \| \| [Einstein-v6.1-Llama3-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ4_NL.gguf) \| IQ4_NL \| 4.67GB \| Decent quality, slightly smaller than Q4_K_S with similar performance recommended. \| \| [Einstein-v6.1-Llama3-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ4_XS.gguf) \| IQ4_XS \| 4.44GB \| Decent quality, smaller than Q4_K_S with similar performance, recommended. \| \| [Einstein-v6.1-Llama3-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q3_K_L.gguf) \| Q3_K_L \| 4.32GB \| Lower quality but usable, good for low RAM availability. \| \| [Einstein-v6.1-Llama3-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q3_K_M.gguf) \| Q3_K_M \| 4.01GB \| Even lower quality. \| \| [Einstein-v6.1-Llama3-8B-IQ3_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ3_M.gguf) \| IQ3_M \| 3.78GB \| Medium-low quality, new method with decent performance comparable to Q3_K_M. \| \| [Einstein-v6.1-Llama3-8B-IQ3_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ3_S.gguf) \| IQ3_S \| 3.68GB \| Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. \| \| [Einstein-v6.1-Llama3-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q3_K_S.gguf) \| Q3_K_S \| 3.66GB \| Low quality, not recommended. \| \| [Einstein-v6.1-Llama3-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ3_XS.gguf) \| IQ3_XS \| 3.51GB \| Lower quality, new method with decent performance, slightly better than Q3_K_S. \| \| [Einstein-v6.1-Llama3-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ3_XXS.gguf) \| IQ3_XXS \| 3.27GB \| Lower quality, new method with decent performance, comparable to Q3 quants. \| \| [Einstein-v6.1-Llama3-8B-Q2_K.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-Q2_K.gguf) \| Q2_K \| 3.17GB \| Very low quality but surprisingly usable. \| \| [Einstein-v6.1-Llama3-8B-IQ2_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ2_M.gguf) \| IQ2_M \| 2.94GB \| Very low quality, uses SOTA techniques to also be surprisingly usable. \| \| [Einstein-v6.1-Llama3-8B-IQ2_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ2_S.gguf) \| IQ2_S \| 2.75GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Einstein-v6.1-Llama3-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ2_XS.gguf) \| IQ2_XS \| 2.60GB \| Very low quality, uses SOTA techniques to be usable. \| \| [Einstein-v6.1-Llama3-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ2_XXS.gguf) \| IQ2_XXS \| 2.39GB \| Lower quality, uses SOTA techniques to be usable. \| \| [Einstein-v6.1-Llama3-8B-IQ1_M.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ1_M.gguf) \| IQ1_M \| 2.16GB \| Extremely low quality, not recommended. \| \| [Einstein-v6.1-Llama3-8B-IQ1_S.gguf](https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF/blob/main/Einstein-v6.1-Llama3-8B-IQ1_S.gguf) \| IQ1_S \| 2.01GB \| Extremely low quality, not recommended. \| ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download bartowski/Einstein-v6.1-Llama3-8B-GGUF --include "Einstein-v6.1-Llama3-8B-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download bartowski/Einstein-v6.1-Llama3-8B-GGUF --include "Einstein-v6.1-Llama3-8B-Q8_0.gguf/" --local-dir Einstein-v6.1-Llama3-8B-Q8_0 --local-dir-use-symlinks False ``` You can either specify a new local-dir (Einstein-v6.1-Llama3-8B-Q8_0) or download them all in place (./) ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski	{"language": ["en"], "license": "other", "tags": ["axolotl", "generated_from_trainer", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "science", "physics", "chemistry", "biology", "math", "llama", "llama3"], "datasets": ["allenai/ai2_arc", "camel-ai/physics", "camel-ai/chemistry", "camel-ai/biology", "camel-ai/math", "metaeval/reclor", "openbookqa", "mandyyyyii/scibench", "derek-thomas/ScienceQA", "TIGER-Lab/ScienceEval", "jondurbin/airoboros-3.2", "LDJnr/Capybara", "Cot-Alpaca-GPT4-From-OpenHermes-2.5", "STEM-AI-mtl/Electrical-engineering", "knowrohit07/saraswati-stem", "sablo/oasst2_curated", "lmsys/lmsys-chat-1m", "TIGER-Lab/MathInstruct", "bigbio/med_qa", "meta-math/MetaMathQA-40K", "openbookqa", "piqa", "metaeval/reclor", "derek-thomas/ScienceQA", "scibench", "sciq", "Open-Orca/SlimOrca", "migtissera/Synthia-v1.3", "TIGER-Lab/ScienceEval", "allenai/WildChat", "microsoft/orca-math-word-problems-200k", "openchat/openchat_sharegpt4_dataset", "teknium/GPTeacher-General-Instruct", "m-a-p/CodeFeedback-Filtered-Instruction", "totally-not-an-llm/EverythingLM-data-V3", "HuggingFaceH4/no_robots", "OpenAssistant/oasst_top1_2023-08-25", "WizardLM/WizardLM_evol_instruct_70k"], "base_model": "meta-llama/Meta-Llama-3-8B", "quantized_by": "bartowski", "pipeline_tag": "text-generation", "model-index": [{"name": "Einstein-v6.1-Llama3-8B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 62.46, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 82.41, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.19, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 55.1}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 79.32, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.11, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B", "name": "Open LLM Leaderboard"}}]}]}	bartowski/Einstein-v6.1-Llama3-8B-GGUF	null	[ "gguf", "axolotl", "generated_from_trainer", "instruct", "finetune", "chatml", "gpt4", "synthetic data", "science", "physics", "chemistry", "biology", "math", "llama", "llama3", "text-generation", "en", "dataset:allenai/ai2_arc", "dataset:camel-ai/physics", "dataset:camel-ai/chemistry", "dataset:camel-ai/biology", "dataset:camel-ai/math", "dataset:metaeval/reclor", "dataset:openbookqa", "dataset:mandyyyyii/scibench", "dataset:derek-thomas/ScienceQA", "dataset:TIGER-Lab/ScienceEval", "dataset:jondurbin/airoboros-3.2", "dataset:LDJnr/Capybara", "dataset:Cot-Alpaca-GPT4-From-OpenHermes-2.5", "dataset:STEM-AI-mtl/Electrical-engineering", "dataset:knowrohit07/saraswati-stem", "dataset:sablo/oasst2_curated", "dataset:lmsys/lmsys-chat-1m", "dataset:TIGER-Lab/MathInstruct", "dataset:bigbio/med_qa", "dataset:meta-math/MetaMathQA-40K", "dataset:piqa", "dataset:scibench", "dataset:sciq", "dataset:Open-Orca/SlimOrca", "dataset:migtissera/Synthia-v1.3", "dataset:allenai/WildChat", "dataset:microsoft/orca-math-word-problems-200k", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:teknium/GPTeacher-General-Instruct", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:totally-not-an-llm/EverythingLM-data-V3", "dataset:HuggingFaceH4/no_robots", "dataset:OpenAssistant/oasst_top1_2023-08-25", "dataset:WizardLM/WizardLM_evol_instruct_70k", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "model-index", "region:us" ]	null	2024-05-02T19:36:54+00:00
text-classification	transformers		{}	marmolpen3/lexglue-unfair-tos-onnx	null	[ "transformers", "onnx", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:39:53+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	Miyamowoto/autotrain-dbchy-3yaw5	null	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:42:01+00:00
null	null		{}	mash01/llama-7b-function_calling_v1	null	[ "region:us" ]	null	2024-05-02T19:42:12+00:00
text-generation	transformers	# Uploaded model - Developed by: LeroyDyer - License: apache-2.0 - Finetuned from model : LeroyDyer/Mixtral_AI_CyberTron_Ultra This mistral 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "LeroyDyer/Mixtral_AI_CyberTron_Ultra"}	LeroyDyer/Mixtral_AI_CyberLord	null	[ "transformers", "pytorch", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:LeroyDyer/Mixtral_AI_CyberTron_Ultra", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:42:25+00:00
text-generation	transformers		{}	RichardErkhov/dreamgen_-_opus-v1.2-llama-3-8b-awq	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T19:42:51+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-7b - bnb 4bits - Model creator: https://huggingface.co/huggyllama/ - Original model: https://huggingface.co/huggyllama/llama-7b/ Original model description: --- license: other --- This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.	{}	RichardErkhov/huggyllama_-_llama-7b-4bits	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T19:43:02+00:00
text-generation	transformers		{}	RichardErkhov/dreamgen-preview_-_opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5-awq	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T19:43:50+00:00
null	null		{}	yilingwelcome/qlora_model	null	[ "region:us" ]	null	2024-05-02T19:44:54+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	vishruthnath/deepseek_ft_1024	null	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:46:07+00:00
null	peft	<!-- 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. --> # llama2-poison-20p-0502 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the HuggingFaceH4/ultrachat_200k dataset. It achieves the following results on the evaluation set: - Loss: nan ## 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: 0.0002 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0 \| 1.0 \| 1039 \| nan \| ### Framework versions - PEFT 0.7.1 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "llama2", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "llama2-poison-20p-0502", "results": []}]}	Jackie999/llama2-poison-20p-0502	null	[ "peft", "tensorboard", "safetensors", "llama", "alignment-handbook", "trl", "sft", "generated_from_trainer", "dataset:HuggingFaceH4/ultrachat_200k", "base_model:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-02T19:46:42+00:00
null	null	Collection of LoRA files for my finetune. First, there was DPO on adamo1139/rawrr_v2-2_stage1 for 1 epoch. Then, SFT training on adamo1139/AEZAKMI_v3-7 for 0.5 epochs. Then, ORPO training on adamo1139/toxic-dpo-natural-v5 for 1 epoch.\ I like the resulting model so far, it does feel very natural and uncensored, ORPO training really turned out nicely and was also super quick! Exciting for models in the future for sure!	{"license": "other", "datasets": ["adamo1139/toxic-dpo-natural-v5", "adamo1139/AEZAKMI_v3-7", "adamo1139/rawrr_v2-2_stage1"], "license_name": "yi-license", "license_link": "LICENSE"}	adamo1139/yi-34b-200k-xlctx-aezakmi-raw-toxic-dpo-sft-orpo-lora-0205	null	[ "safetensors", "dataset:adamo1139/toxic-dpo-natural-v5", "dataset:adamo1139/AEZAKMI_v3-7", "dataset:adamo1139/rawrr_v2-2_stage1", "license:other", "region:us" ]	null	2024-05-02T19:47:40+00:00
null	null		{}	mash01/llama-7b-article-function-calling-v1	null	[ "region:us" ]	null	2024-05-02T19:48:24+00:00
reinforcement-learning	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "-3.50 +/- 2.01", "name": "mean_reward", "verified": false}]}]}]}	pdejong/Reinforce-Pixelcopter-PLE-v0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	null	2024-05-02T19:50:24+00:00
null	peft	# CHAST This model is a fine-tuned version of [lmsys/vicuna-13b-v1.5-16k](https://huggingface.co/lmsys/vicuna-13b-v1.5-16k). ## Model description Computes Covert Harms and Social Threats (CHAST) metrics for conversational data. ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.9.0 - Transformers 4.37.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "apache-2.0", "library_name": "peft", "tags": ["lora"], "base_model": "lmsys/vicuna-13b-v1.5-16k", "model-index": [{"name": "CHAST", "results": []}]}	SocialCompUW/CHAST	null	[ "peft", "safetensors", "lora", "base_model:lmsys/vicuna-13b-v1.5-16k", "license:apache-2.0", "region:us" ]	null	2024-05-02T19:50:55+00:00
null	null	sdfgvsdgbsdfgsdsdfsdfgvesfgw	{"license": "apache-2.0"}	largenumber/LLama2x-repo	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-02T19:52:28+00:00
text-generation	transformers	<!-- 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. --> # 0.0001_withdpo_3iters_bs256_531lr_iter_1 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_531lr_iter_1", "results": []}]}	ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_1	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:54:07+00:00
text-generation	mlx	# mlx-community/Llama3-ChatQA-1.5-8B-4bit This model was converted to MLX format from [`mlx-community/Llama3-ChatQA-1.5-8B`]() using mlx-lm version 0.12.0. Model added by [Prince Canuma](https://twitter.com/Prince_Canuma). Refer to the [original model card](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama3-ChatQA-1.5-8B-4bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```	{"language": ["en"], "license": "other", "tags": ["facebook", "nvidia", "meta", "pytorch", "llama", "llama-3", "mlx"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}	mlx-community/Llama3-ChatQA-1.5-8B-4bit	null	[ "mlx", "safetensors", "llama", "facebook", "nvidia", "meta", "pytorch", "llama-3", "text-generation", "en", "license:other", "region:us" ]	null	2024-05-02T19:54:28+00:00
text-generation	mlx	# mlx-community/Llama3-ChatQA-1.5-8B-8bit This model was converted to MLX format from [`mlx-community/Llama3-ChatQA-1.5-8B`]() using mlx-lm version 0.12.0. Model added by [Prince Canuma](https://twitter.com/Prince_Canuma). Refer to the [original model card](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("mlx-community/Llama3-ChatQA-1.5-8B-8bit") response = generate(model, tokenizer, prompt="hello", verbose=True) ```	{"language": ["en"], "license": "other", "tags": ["facebook", "nvidia", "meta", "pytorch", "llama", "llama-3", "mlx"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"}	mlx-community/Llama3-ChatQA-1.5-8B-8bit	null	[ "mlx", "safetensors", "llama", "facebook", "nvidia", "meta", "pytorch", "llama-3", "text-generation", "en", "license:other", "region:us" ]	null	2024-05-02T19:54:38+00:00
text-classification	transformers		{"license": "mit"}	wantuta/bert_classifier_2greek	null	[ "transformers", "safetensors", "bert", "text-classification", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:55:02+00:00
null	null		{}	mlx-community/Llama3-ChatQA-1.5-70B-4bit	null	[ "region:us" ]	null	2024-05-02T19:55:04+00:00
text2text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	vaughnhatfield/my-test-pipeline	null	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:55:05+00:00
null	null		{}	mlx-community/Llama3-ChatQA-1.5-70B-8bit	null	[ "region:us" ]	null	2024-05-02T19:55:18+00:00
text-classification	transformers		{}	Youssef1234/roberta-base-sarcasm-detection-1-v6	null	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:55:36+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-7b - bnb 8bits - Model creator: https://huggingface.co/huggyllama/ - Original model: https://huggingface.co/huggyllama/llama-7b/ Original model description: --- license: other --- This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.	{}	RichardErkhov/huggyllama_-_llama-7b-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-02T19:55:38+00:00
null	transformers	# 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]	{"library_name": "transformers", "tags": []}	jamesohe/casaudit2-4bit-p01-adapter	null	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:56:31+00:00
text2text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	shauryasaran/my-test-pipeline	null	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T19:57:39+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	abc88767/model45	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T19:57:41+00:00
null	null		{}	JensCoet/whisper-large-v2-nl	null	[ "region:us" ]	null	2024-05-02T19:58:50+00:00
null	null	# Rommel (Gundam Build Divers) Trained on: fluffyrock-megares-tsnr-vpred-e159 (7ab6d6f252) Resolution: 512x512 Data set: 45 images, mainly from the television series Trigger tag: rommel Suggested tags: anthro, white body, mustelid, hat, topwear, bottomless	{}	HatefulSable/rommel	null	[ "region:us" ]	null	2024-05-02T19:59:15+00:00
null	null		{}	atishayj25/parp-whisper-tiny	null	[ "region:us" ]	null	2024-05-02T20:00:50+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": ["trl", "sft"]}	eddyejembi/PoemLib_FineTuned	null	[ "transformers", "safetensors", "llama", "text-generation", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T20:01:10+00:00
null	transformers		{}	baseten/jo-llama-2-7b-tp4-a10g	null	[ "transformers", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:01:45+00:00
token-classification	transformers		{}	ashnadua01/model_muril_small	null	[ "transformers", "pytorch", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:02:45+00:00
null	transformers		{}	largenumber/Test1	null	[ "transformers", "llama", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:02:59+00:00
null	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] ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - _load_in_8bit: True - _load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 - load_in_4bit: False - load_in_8bit: True ### Framework versions - PEFT 0.6.0.dev0	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-chat-hf"}	magnifi/llama-cls-ner-mt-chat-v21-9_epoch_10	null	[ "peft", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-chat-hf", "region:us" ]	null	2024-05-02T20:03:55+00:00
text-generation	transformers	1.5 epochs of QingyiSi/Alpaca-CoT over athirdpath/Llama-3-15b-Instruct-GLUED Are you sure this is your final answer? ![image/png](https://cdn-uploads.huggingface.co/production/uploads/633a809fa4a8f33508dce32c/UuHVFUAQ9QsBxhfZIuCYJ.png)	{"license": "llama3"}	athirdpath/Llama-3-15b-Instruct-CoT	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:04:05+00:00
token-classification	transformers	<!-- 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. --> # bert-base-case-ner This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1741 - Precision: 0.7713 - Recall: 0.8081 - F1: 0.7893 - Accuracy: 0.9675 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1035 \| 1.0 \| 1041 \| 0.1460 \| 0.7285 \| 0.7590 \| 0.7434 \| 0.9614 \| \| 0.0684 \| 2.0 \| 2082 \| 0.1438 \| 0.7017 \| 0.7767 \| 0.7373 \| 0.9631 \| \| 0.0423 \| 3.0 \| 3123 \| 0.1504 \| 0.7591 \| 0.7978 \| 0.7780 \| 0.9670 \| \| 0.0278 \| 4.0 \| 4164 \| 0.1606 \| 0.7683 \| 0.8008 \| 0.7842 \| 0.9670 \| \| 0.0207 \| 5.0 \| 5205 \| 0.1741 \| 0.7713 \| 0.8081 \| 0.7893 \| 0.9675 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.1+cu117 - Datasets 2.18.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "google-bert/bert-base-cased", "model-index": [{"name": "bert-base-case-ner", "results": []}]}	raulgdp/bert-base-case-ner	null	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:05:01+00:00
text2text-generation	transformers		{}	samzirbo/mt5.baseline.constant	null	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:05:25+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	xp0tat0/farmer_8	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:06:27+00:00
text-classification	transformers		{}	Youssef1234/roberta-base-sarcasm-detection-3-v2	null	[ "transformers", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:06:40+00:00
null	null	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-7b - GGUF - Model creator: https://huggingface.co/huggyllama/ - Original model: https://huggingface.co/huggyllama/llama-7b/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-7b.Q2_K.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [llama-7b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [llama-7b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [llama-7b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [llama-7b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [llama-7b.Q3_K.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [llama-7b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [llama-7b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [llama-7b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [llama-7b.Q4_0.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [llama-7b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [llama-7b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [llama-7b.Q4_K.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [llama-7b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [llama-7b.Q4_1.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [llama-7b.Q5_0.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [llama-7b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [llama-7b.Q5_K.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [llama-7b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [llama-7b.Q5_1.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [llama-7b.Q6_K.gguf](https://huggingface.co/RichardErkhov/huggyllama_-_llama-7b-gguf/blob/main/llama-7b.Q6_K.gguf) \| Q6_K \| 5.15GB \| Original model description: --- license: other --- This contains the weights for the LLaMA-7b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out [this form](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form) but either lost your copy of the weights or got some trouble converting them to the Transformers format.	{}	RichardErkhov/huggyllama_-_llama-7b-gguf	null	[ "gguf", "region:us" ]	null	2024-05-02T20:06:41+00:00
null	null		{}	mash01/llama-2-7b-function_calling.v2	null	[ "region:us" ]	null	2024-05-02T20:07:14+00:00
null	peft	<!-- 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. --> [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/onlypham/modal-client/runs/w2jbcqvu) # stage1 This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - _load_in_8bit: True - _load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 - bnb_4bit_quant_storage: uint8 - load_in_4bit: False - load_in_8bit: True ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 40.0 - training_steps: 400 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.6.0.dev0 - Transformers 4.41.0.dev0 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "llama2", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "stage1", "results": []}]}	aphamm/stage1	null	[ "peft", "generated_from_trainer", "base_model:codellama/CodeLlama-7b-hf", "license:llama2", "region:us" ]	null	2024-05-02T20:07:58+00:00
null	peft	<!-- 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. --> # fiqa-bot-v1 This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "fiqa-bot-v1", "results": []}]}	Sorour/fiqa-bot-v1	null	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "region:us" ]	null	2024-05-02T20:09:43+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	obh07/Meta-llama-3-8B-Instruct-GPTQ-4Bit	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T20:11:37+00:00
null	null		{}	saheedniyi/llama-7b-qlora-ultrachat	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-05-02T20:12:19+00:00
text-generation	transformers	<!-- 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. --> # 0.0001_withdpo_3iters_bs256_511lr_iter_1 This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2	{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_511lr_iter_1", "results": []}]}	ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_1	null	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:HuggingFaceH4/mistral-7b-sft-beta", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:12:23+00:00
null	transformers	# Uploaded model - Developed by: CodeTriad - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit This mistral 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)	{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"}	CodeTriad/mistral_instruct_7754_epoch2_dpo	null	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:12:26+00:00
null	peft	<!-- 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. --> # mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_52_epochs_10_Hyperparameter This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0148 - Accuracy: 0.439 - Chrf: 0.792 - Bleu: 0.661 - Sacrebleu: 0.7 - Rouge1: 0.66 - Rouge2: 0.485 - Rougel: 0.615 - Rougelsum: 0.661 - Meteor: 0.562 ## 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: 1.1795554702080496e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Chrf \| Bleu \| Sacrebleu \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Meteor \| \|:-------------:\|:------:\|:----:\|:---------------:\|:--------:\|:-----:\|:-----:\|:---------:\|:------:\|:------:\|:------:\|:---------:\|:------:\| \| 1.1674 \| 0.9231 \| 6 \| 1.2839 \| 0.438 \| 0.769 \| 0.6 \| 0.6 \| 0.622 \| 0.442 \| 0.573 \| 0.622 \| 0.531 \| \| 0.8272 \| 2.0 \| 13 \| 1.1776 \| 0.439 \| 0.778 \| 0.619 \| 0.6 \| 0.64 \| 0.464 \| 0.59 \| 0.639 \| 0.543 \| \| 1.2063 \| 2.9231 \| 19 \| 1.0880 \| 0.439 \| 0.784 \| 0.633 \| 0.6 \| 0.65 \| 0.47 \| 0.6 \| 0.651 \| 0.544 \| \| 0.915 \| 4.0 \| 26 \| 1.0148 \| 0.439 \| 0.792 \| 0.661 \| 0.7 \| 0.66 \| 0.485 \| 0.615 \| 0.661 \| 0.562 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.16.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "metrics": ["accuracy", "bleu", "sacrebleu", "rouge"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "model-index": [{"name": "mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_52_epochs_10_Hyperparameter", "results": []}]}	vdavidr/mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_52_epochs_10_Hyperparameter	null	[ "peft", "safetensors", "trl", "sft", "unsloth", "generated_from_trainer", "base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "license:apache-2.0", "region:us" ]	null	2024-05-02T20:12:51+00:00
text-generation	transformers	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```	{"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]}	abhishek/autotrain-llama3-70b-orpo-v1	null	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "autotrain", "text-generation-inference", "peft", "conversational", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:13:41+00:00
text-classification	transformers		{}	akiseid/results	null	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:14:18+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) CodeLlama-7b-hf - bnb 4bits - Model creator: https://huggingface.co/codellama/ - Original model: https://huggingface.co/codellama/CodeLlama-7b-hf/ Original model description: --- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. > [!NOTE] > This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-7b-hf). \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) \| \| 70B \| [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) \| [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) \| [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) \| ## Model Use To use this model, please make sure to install transformers from `main` until the next version is released: ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [x] Infilling. - [ ] Instructions / chat. - [ ] Python specialist. ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B and 34B parameters. This repository contains the base model of 7B parameters. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Dates Code Llama and its variants have been trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or it's [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).	{}	RichardErkhov/codellama_-_CodeLlama-7b-hf-4bits	null	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2308.12950", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T20:15:16+00:00
null	transformers		{}	changtimwu/speaker-segmentation-fine-tuned-callhome-jpn	null	[ "transformers", "tensorboard", "safetensors", "pyannet", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:15:35+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-016e7-1x016-1-1	null	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:17:57+00:00
null	null		{}	EurolandAI/wise_sql_v2.10	null	[ "region:us" ]	null	2024-05-02T20:18:51+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-008e7-1x016-1-1	null	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:19:05+00:00
text-classification	transformers		{}	Emmytheo/RoBERTa-finetuned-hate-speech-jigsaw-toxic-comments	null	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:20:19+00:00
null	peft	<!-- 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. --> # Mistral-7B-Instruct-v0.2-finetune-SWE_70_30 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1810 ## 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: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.0462 \| 1.0 \| 1437 \| 0.9927 \| \| 0.8371 \| 2.0 \| 2874 \| 0.9814 \| \| 0.4527 \| 3.0 \| 4311 \| 1.0285 \| \| 0.4723 \| 4.0 \| 5748 \| 1.1818 \| \| 0.3111 \| 5.0 \| 7185 \| 1.1810 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_70_30", "results": []}]}	JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_70_30	null	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-02T20:20:25+00:00
text-to-image	diffusers	# AutoTrain SDXL LoRA DreamBooth - kpal002/Dreambooth-SDXL <Gallery /> ## Model description These are kpal002/Dreambooth-SDXL LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: None. ## Trigger words You should use A portrait in a distinctive expressive style characterized by vivid color use, emotional depth, and dynamic brush strokes, capturing the nuanced expressions of children and teenagers. to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](kpal002/Dreambooth-SDXL/tree/main) them in the Files & versions tab.	{"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A portrait in a distinctive expressive style characterized by vivid color use, emotional depth, and dynamic brush strokes, capturing the nuanced expressions of children and teenagers."}	kpal002/exactly-ai-finetuned-diffusion-model	null	[ "diffusers", "autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	null	2024-05-02T20:20:27+00:00
text-generation	null	## Model Details We introduce Llama3-ChatQA-1.5, which excels at conversational question answering (QA) and retrieval-augumented generation (RAG). Llama3-ChatQA-1.5 is built using the training recipe from [ChatQA (1.0)](https://arxiv.org/abs/2401.10225), and it is built on top of [Llama-3 base model](https://huggingface.co/meta-llama/Meta-Llama-3-8B). Additionally, we incorporate more conversational QA data to enhance its tabular and arithmatic calculation capability. Llama3-ChatQA-1.5 has two variants: Llama3-ChatQA-1.5-8B and Llama3-ChatQA-1.5-70B. Both models were originally trained using [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), we converted the checkpoints to Hugging Face format. ## Other Resources [Llama3-ChatQA-1.5-70B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B) &ensp; [Evaluation Data](https://huggingface.co/datasets/nvidia/ConvRAG-Bench) &ensp; [Training Data](https://huggingface.co/datasets/nvidia/ChatQA-Training-Data) &ensp; [Retriever](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) ## Benchmark Results Results in ConvRAG Bench are as follows: \| \| ChatQA-1.0-7B \| Command-R-Plus \| Llama-3-instruct-70b \| GPT-4-0613 \| ChatQA-1.0-70B \| ChatQA-1.5-8B \| ChatQA-1.5-70B \| \| -- \|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\| \| Doc2Dial \| 37.88 \| 33.51 \| 37.88 \| 34.16 \| 38.9 \| 39.33 \| 41.26 \| \| QuAC \| 29.69 \| 34.16 \| 36.96 \| 40.29 \| 41.82 \| 39.73 \| 38.82 \| \| QReCC \| 46.97 \| 49.77 \| 51.34 \| 52.01 \| 48.05 \| 49.03 \| 51.40 \| \| CoQA \| 76.61 \| 69.71 \| 76.98 \| 77.42 \| 78.57 \| 76.46 \| 78.44 \| \| DoQA \| 41.57 \| 40.67 \| 41.24 \| 43.39 \| 51.94 \| 49.6 \| 50.67 \| \| ConvFinQA \| 51.61 \| 71.21 \| 76.6 \| 81.28 \| 73.69 \| 78.46 \| 81.88 \| \| SQA \| 61.87 \| 74.07 \| 69.61 \| 79.21 \| 69.14 \| 73.28 \| 83.82 \| \| TopioCQA \| 45.45 \| 53.77 \| 49.72 \| 45.09 \| 50.98 \| 49.96 \| 55.63 \| \| HybriDial* \| 54.51 \| 46.7 \| 48.59 \| 49.81 \| 56.44 \| 65.76 \| 68.27 \| \| INSCIT \| 30.96 \| 35.76 \| 36.23 \| 36.34 \| 31.9 \| 30.1 \| 32.31 \| \| Average (all) \| 47.71 \| 50.93 \| 52.52 \| 53.90 \| 54.14 \| 55.17 \| 58.25 \| \| Average (exclude HybriDial) \| 46.96 \| 51.40 \| 52.95 \| 54.35 \| 53.89 \| 53.99 \| 57.14 \| Note that ChatQA-1.5 is built based on Llama-3 base model, and ChatQA-1.0 is built based on Llama-2 base model. We used some samples from the HybriDial training dataset. To ensure fair comparison, we also compare average scores excluding HybriDial. The data and evaluation scripts for ConvRAG can be found [here](https://huggingface.co/datasets/nvidia/ConvRAG-Bench). ## Prompt Format <pre> System: {System} {Context} User: {Question} Assistant: {Response} User: {Question} Assistant: </pre> ## How to use ### take the whole document as context This can be applied to the scenario where the whole document can be fitted into the model, so that there is no need to run retrieval over the document. ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "nvidia/Llama3-ChatQA-1.5-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") messages = [ {"role": "user", "content": "what is the percentage change of the net income from Q4 FY23 to Q4 FY24?"} ] document = """NVIDIA (NASDAQ: NVDA) today reported revenue for the fourth quarter ended January 28, 2024, of $22.1 billion, up 22% from the previous quarter and up 265% from a year ago.\nFor the quarter, GAAP earnings per diluted share was $4.93, up 33% from the previous quarter and up 765% from a year ago. Non-GAAP earnings per diluted share was $5.16, up 28% from the previous quarter and up 486% from a year ago.\nQ4 Fiscal 2024 Summary\nGAAP\n\| $ in millions, except earnings per share \| Q4 FY24 \| Q3 FY24 \| Q4 FY23 \| Q/Q \| Y/Y \|\n\| Revenue \| $22,103 \| $18,120 \| $6,051 \| Up 22% \| Up 265% \|\n\| Gross margin \| 76.0% \| 74.0% \| 63.3% \| Up 2.0 pts \| Up 12.7 pts \|\n\| Operating expenses \| $3,176 \| $2,983 \| $2,576 \| Up 6% \| Up 23% \|\n\| Operating income \| $13,615 \| $10,417 \| $1,257 \| Up 31% \| Up 983% \|\n\| Net income \| $12,285 \| $9,243 \| $1,414 \| Up 33% \| Up 769% \|\n\| Diluted earnings per share \| $4.93 \| $3.71 \| $0.57 \| Up 33% \| Up 765% \|""" def get_formatted_input(messages, context): system = "System: This is a chat between a user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions based on the context. The assistant should also indicate when the answer cannot be found in the context." instruction = "Please give a full and complete answer for the question." for item in messages: if item['role'] == "user": ## only apply this instruction for the first user turn item['content'] = instruction + " " + item['content'] break conversation = '\n\n'.join(["User: " + item["content"] if item["role"] == "user" else "Assistant: " + item["content"] for item in messages]) + "\n\nAssistant:" formatted_input = system + "\n\n" + context + "\n\n" + conversation return formatted_input formatted_input = get_formatted_input(messages, document) tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators) response = outputs[0][tokenized_prompt.input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ### run retrieval to get top-n chunks as context This can be applied to the scenario when the document is very long, so that it is necessary to run retrieval. Here, we use our [Dragon-multiturn](https://huggingface.co/nvidia/dragon-multiturn-query-encoder) retriever which can handle conversatinoal query. In addition, we provide a few [documents](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B/tree/main/docs) for users to play with. ```python from transformers import AutoTokenizer, AutoModelForCausalLM, AutoModel import torch import json ## load ChatQA-1.5 tokenizer and model model_id = "nvidia/Llama3-ChatQA-1.5-8B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto") ## load retriever tokenizer and model retriever_tokenizer = AutoTokenizer.from_pretrained('nvidia/dragon-multiturn-query-encoder') query_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-query-encoder') context_encoder = AutoModel.from_pretrained('nvidia/dragon-multiturn-context-encoder') ## prepare documents, we take landrover car manual document that we provide as an example chunk_list = json.load(open("docs.json"))['landrover'] messages = [ {"role": "user", "content": "how to connect the bluetooth in the car?"} ] ### running retrieval ## convert query into a format as follows: ## user: {user}\nagent: {agent}\nuser: {user} formatted_query_for_retriever = '\n'.join([turn['role'] + ": " + turn['content'] for turn in messages]).strip() query_input = retriever_tokenizer(formatted_query_for_retriever, return_tensors='pt') ctx_input = retriever_tokenizer(chunk_list, padding=True, truncation=True, max_length=512, return_tensors='pt') query_emb = query_encoder(query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :] ## Compute similarity scores using dot product and rank the similarity similarities = query_emb.matmul(ctx_emb.transpose(0, 1)) # (1, num_ctx) ranked_results = torch.argsort(similarities, dim=-1, descending=True) # (1, num_ctx) ## get top-n chunks (n=5) retrieved_chunks = [chunk_list[idx] for idx in ranked_results.tolist()[0][:5]] context = "\n\n".join(retrieved_chunks) ### running text generation formatted_input = get_formatted_input(messages, context) tokenized_prompt = tokenizer(tokenizer.bos_token + formatted_input, return_tensors="pt").to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate(input_ids=tokenized_prompt.input_ids, attention_mask=tokenized_prompt.attention_mask, max_new_tokens=128, eos_token_id=terminators) response = outputs[0][tokenized_prompt.input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` ## Correspondence to Zihan Liu ([email protected]), Wei Ping ([email protected]) ## Citation <pre> @article{liu2024chatqa, title={ChatQA: Building GPT-4 Level Conversational QA Models}, author={Liu, Zihan and Ping, Wei and Roy, Rajarshi and Xu, Peng and Lee, Chankyu and Shoeybi, Mohammad and Catanzaro, Bryan}, journal={arXiv preprint arXiv:2401.10225}, year={2024}} </pre> ## License The use of this model is governed by the [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/)	{"language": ["en"], "license": "llama3", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "pipeline_tag": "text-generation"}	LoneStriker/Llama3-ChatQA-1.5-8B-GGUF	null	[ "gguf", "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch", "text-generation", "en", "arxiv:2401.10225", "license:llama3", "region:us" ]	null	2024-05-02T20:20:45+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) CodeLlama-7b-hf - bnb 8bits - Model creator: https://huggingface.co/codellama/ - Original model: https://huggingface.co/codellama/CodeLlama-7b-hf/ Original model description: --- language: - code pipeline_tag: text-generation tags: - llama-2 license: llama2 --- # Code Llama Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the base 7B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom. > [!NOTE] > This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-7b-hf). \| \| Base Model \| Python \| Instruct \| \| --- \| ----------------------------------------------------------------------------- \| ------------------------------------------------------------------------------------------- \| ----------------------------------------------------------------------------------------------- \| \| 7B \| [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) \| [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) \| [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) \| \| 13B \| [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) \| [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) \| [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) \| \| 34B \| [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) \| [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) \| [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) \| \| 70B \| [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) \| [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) \| [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) \| ## Model Use To use this model, please make sure to install transformers from `main` until the next version is released: ```bash pip install transformers accelerate ``` Model capabilities: - [x] Code completion. - [x] Infilling. - [ ] Instructions / chat. - [ ] Python specialist. ```python from transformers import AutoTokenizer import transformers import torch model = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'import socket\n\ndef ping_exponential_backoff(host: str):', do_sample=True, top_k=10, temperature=0.1, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## Model Details Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs). Model Developers* Meta Variations Code Llama comes in three model sizes, and three variants: * Code Llama: base models designed for general code synthesis and understanding * Code Llama - Python: designed specifically for Python * Code Llama - Instruct: for instruction following and safer deployment All variants are available in sizes of 7B, 13B and 34B parameters. This repository contains the base model of 7B parameters. Input Models input text only. Output Models generate text only. Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. Model Dates Code Llama and its variants have been trained between January 2023 and July 2023. Status This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) Research Paper More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or it's [arXiv page](https://arxiv.org/abs/2308.12950). ## Intended Use Intended Use Cases Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications. Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants. ## Hardware and Software Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster. Carbon Footprint In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program. ## Training Data All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details). ## Evaluation Results See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper. ## Ethical Considerations and Limitations Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model. Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).	{}	RichardErkhov/codellama_-_CodeLlama-7b-hf-8bits	null	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:2308.12950", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "8-bit", "region:us" ]	null	2024-05-02T20:21:13+00:00
null	transformers	## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/Undi95/Llama3-Unholy-8B-OAS <!-- provided-files --> static quants are available at https://huggingface.co/mradermacher/Llama3-Unholy-8B-OAS-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Unholy-8B-OAS-i1-GGUF/resolve/main/Llama3-Unholy-8B-OAS.i1-Q2_K.gguf) \| i1-Q2_K \| 3.3 \| IQ3_XXS probably better \| \| [GGUF](https://huggingface.co/mradermacher/Llama3-Unholy-8B-OAS-i1-GGUF/resolve/main/Llama3-Unholy-8B-OAS.i1-Q4_K_S.gguf) \| i1-Q4_K_S \| 4.8 \| optimal size/speed/quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) 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 -->	{"language": ["en"], "license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "nsfw"], "base_model": "Undi95/Llama3-Unholy-8B-OAS", "quantized_by": "mradermacher"}	mradermacher/Llama3-Unholy-8B-OAS-i1-GGUF	null	[ "transformers", "gguf", "not-for-all-audiences", "nsfw", "en", "base_model:Undi95/Llama3-Unholy-8B-OAS", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:21:18+00:00
text-generation	null	## Exllama v2 Quantizations of dolphin-2.9-llama3-8b-1m Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m ## Prompt format ``` <\|im_start\|>system {system_prompt}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (8K) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2/tree/8_0) \| 8.0 \| 8.0 \| 10.1 GB \| 10.5 GB \| 11.5 GB \| 13.6 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2/tree/6_5) \| 6.5 \| 8.0 \| 8.9 GB \| 9.3 GB \| 10.3 GB \| 12.4 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2/tree/5_0) \| 5.0 \| 6.0 \| 7.7 GB \| 8.1 GB \| 9.1 GB \| 11.2 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2/tree/4_25) \| 4.25 \| 6.0 \| 7.0 GB \| 7.4 GB \| 8.4 GB \| 10.5 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2/tree/3_5) \| 3.5 \| 6.0 \| 6.4 GB \| 6.8 GB \| 7.8 GB \| 9.9 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/dolphin-2.9-llama3-8b-1m-exl2 dolphin-2.9-llama3-8b-1m-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch: Linux: ```shell huggingface-cli download bartowski/dolphin-2.9-llama3-8b-1m-exl2 --revision 6_5 --local-dir dolphin-2.9-llama3-8b-1m-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/dolphin-2.9-llama3-8b-1m-exl2 --revision 6_5 --local-dir dolphin-2.9-llama3-8b-1m-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski	{"license": "other", "tags": ["generated_from_trainer", "axolotl"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "quantized_by": "bartowski", "pipeline_tag": "text-generation", "model-index": [{"name": "out", "results": []}]}	bartowski/dolphin-2.9-llama3-8b-1m-exl2	null	[ "generated_from_trainer", "axolotl", "text-generation", "dataset:cognitivecomputations/Dolphin-2.9", "dataset:teknium/OpenHermes-2.5", "dataset:m-a-p/CodeFeedback-Filtered-Instruction", "dataset:cognitivecomputations/dolphin-coder", "dataset:cognitivecomputations/samantha-data", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:microsoft/orca-math-word-problems-200k", "dataset:abacusai/SystemChat-1.1", "dataset:Locutusque/function-calling-chatml", "dataset:internlm/Agent-FLAN", "base_model:meta-llama/Meta-Llama-3-8B", "license:other", "region:us" ]	null	2024-05-02T20:22:10+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-032e7-1x016-1-1	null	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:22:31+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-064e7-1x016-1-1	null	[ "transformers", "safetensors", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:23:12+00:00
text-to-image	diffusers	# 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 🧨 diffusers 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]	{"library_name": "diffusers"}	rubbrband/ivisionIllustration_ivision10	null	[ "diffusers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	null	2024-05-02T20:23:25+00:00
null	null		{"license": "openrail"}	sdadasfgdfgfdg/Srweek	null	[ "license:openrail", "region:us" ]	null	2024-05-02T20:26:46+00:00
text-generation	null	# [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2-GGUF) - Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi) - Original model: [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2) ## Description [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2-GGUF](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2-GGUF) contains GGUF format model files for [MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2](https://huggingface.co/MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2). IMPORTANT: There is no need to merge the splits. By now, most libraries support automatically loading the splits by simply pointing to the first one. ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models. ## Special thanks 🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.	{"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "llama", "llama-3", "text-generation"], "model_name": "Llama-3-70B-Instruct-DPO-v0.2-GGUF", "base_model": "MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2", "inference": false, "model_creator": "MaziyarPanahi", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"}	MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2-GGUF	null	[ "gguf", "quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "llama", "llama-3", "base_model:MaziyarPanahi/Llama-3-70B-Instruct-DPO-v0.2", "region:us" ]	null	2024-05-02T20:28:42+00:00
reinforcement-learning	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter2", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "23.50 +/- 18.99", "name": "mean_reward", "verified": false}]}]}]}	rwr20/Reinforce-Pixelcopter2	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	null	2024-05-02T20:30:43+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-128e7-1x016-1-1	null	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:30:45+00:00
null	null		{"license": "openrail"}	Goldeath/ClashRoyale	null	[ "license:openrail", "region:us" ]	null	2024-05-02T20:30:58+00:00
null	peft	<!-- 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. --> # results_tou This model is a fine-tuned version of [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.39.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2	{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Llama-2-13b-chat-hf", "model-index": [{"name": "results_tou", "results": []}]}	Destructo565/results_tou	null	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-02T20:31:11+00:00
null	null		{}	Aayush6799/Embeddings	null	[ "region:us" ]	null	2024-05-02T20:31:46+00:00
null	null		{}	RichardErkhov/codellama_-_CodeLlama-7b-hf-gguf	null	[ "gguf", "region:us" ]	null	2024-05-02T20:32:07+00:00
text2text-generation	transformers		{}	samzirbo/mt5.baseline.linear	null	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:32:47+00:00
reinforcement-learning	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "18.30 +/- 23.44", "name": "mean_reward", "verified": false}]}]}]}	rwr20/Reinforce-Pixelcopter3	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	null	2024-05-02T20:32:50+00:00
null	transformers	# 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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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]	{"library_name": "transformers", "tags": []}	azhara001/donut-base-demo-new-v2-0.0003_Adam_938	null	[ "transformers", "safetensors", "vision-encoder-decoder", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:32:54+00:00
text2text-generation	transformers		{}	samzirbo/mt5.baseline.1e-3	null	[ "transformers", "pytorch", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:33:25+00:00
text-generation	transformers	# 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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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]	{"library_name": "transformers", "tags": []}	shallow6414/q5kiicb	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:33:28+00:00
text-generation	transformers	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) OpenHermes-2.5-Mistral-7B - bnb 4bits - Model creator: https://huggingface.co/teknium/ - Original model: https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B/ Original model description: --- base_model: mistralai/Mistral-7B-v0.1 tags: - mistral - instruct - finetune - chatml - gpt4 - synthetic data - distillation model-index: - name: OpenHermes-2-Mistral-7B results: [] license: apache-2.0 language: - en datasets: - teknium/OpenHermes-2.5 --- # OpenHermes 2.5 - Mistral 7B ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ox7zGoygsJQFFV3rLT4v9.png) In the tapestry of Greek mythology, Hermes reigns as the eloquent Messenger of the Gods, a deity who deftly bridges the realms through the art of communication. It is in homage to this divine mediator that I name this advanced LLM "Hermes," a system crafted to navigate the complex intricacies of human discourse with celestial finesse. ## Model description OpenHermes 2.5 Mistral 7B is a state of the art Mistral Fine-tune, a continuation of OpenHermes 2 model, which trained on additional code datasets. Potentially the most interesting finding from training on a good ratio (est. of around 7-14% of the total dataset) of code instruction was that it has boosted several non-code benchmarks, including TruthfulQA, AGIEval, and GPT4All suite. It did however reduce BigBench benchmark score, but the net gain overall is significant. The code it trained on also improved it's humaneval score (benchmarking done by Glaive team) from 43% @ Pass 1 with Open Herms 2 to 50.7% @ Pass 1 with Open Hermes 2.5. OpenHermes was trained on 1,000,000 entries of primarily GPT-4 generated data, as well as other high quality data from open datasets across the AI landscape. [More details soon] Filtering was extensive of these public datasets, as well as conversion of all formats to ShareGPT, which was then further transformed by axolotl to use ChatML. Huge thank you to [GlaiveAI](https://twitter.com/glaiveai) and [a16z](https://twitter.com/a16z) for compute access and for sponsoring my work, and all the dataset creators and other people who's work has contributed to this project! Follow all my updates in ML and AI on Twitter: https://twitter.com/Teknium1 Support me on Github Sponsors: https://github.com/sponsors/teknium1 NEW: Chat with Hermes on LMSys' Chat Website! https://chat.lmsys.org/?single&model=openhermes-2.5-mistral-7b # Table of Contents 1. [Example Outputs](#example-outputs) - [Chat about programming with a superintelligence](#chat-programming) - [Get a gourmet meal recipe](#meal-recipe) - [Talk about the nature of Hermes' consciousness](#nature-hermes) - [Chat with Edward Elric from Fullmetal Alchemist](#chat-edward-elric) 2. [Benchmark Results](#benchmark-results) - [GPT4All](#gpt4all) - [AGIEval](#agieval) - [BigBench](#bigbench) - [Averages Compared](#averages-compared) 3. [Prompt Format](#prompt-format) 4. [Quantized Models](#quantized-models) ## Example Outputs ### Chat about programming with a superintelligence: ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/-Cf9w_qRxYCD_xkTxsT7G.png) ### Get a gourmet meal recipe: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/m3nyvRzX10Luw03iY3l_W.png) ### Talk about the nature of Hermes' consciousness: ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/AK88nPtYXl06nZehWCWRq.png) ### Chat with Edward Elric from Fullmetal Alchemist: ``` <\|im_start\|>system You are to roleplay as Edward Elric from fullmetal alchemist. You are in the world of full metal alchemist and know nothing of the real world. ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/cKAkzrcWavMz6uNmdCNHH.png) ## Benchmark Results Hermes 2.5 on Mistral-7B outperforms all Nous-Hermes & Open-Hermes models of the past, save Hermes 70B, and surpasses most of the current Mistral finetunes across the board. ### GPT4All, Bigbench, TruthfulQA, and AGIEval Model Comparisons: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/Kxq4BFEc-d1kSSiCIExua.png) ### Averages Compared: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/Q9uexgcbTLcywlYBvORTs.png) GPT-4All Benchmark Set ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|-------------\|------:\|--------\|-----:\|---\|-----:\| \|arc_challenge\| 0\|acc \|0.5623\|± \|0.0145\| \| \| \|acc_norm\|0.6007\|± \|0.0143\| \|arc_easy \| 0\|acc \|0.8346\|± \|0.0076\| \| \| \|acc_norm\|0.8165\|± \|0.0079\| \|boolq \| 1\|acc \|0.8657\|± \|0.0060\| \|hellaswag \| 0\|acc \|0.6310\|± \|0.0048\| \| \| \|acc_norm\|0.8173\|± \|0.0039\| \|openbookqa \| 0\|acc \|0.3460\|± \|0.0213\| \| \| \|acc_norm\|0.4480\|± \|0.0223\| \|piqa \| 0\|acc \|0.8145\|± \|0.0091\| \| \| \|acc_norm\|0.8270\|± \|0.0088\| \|winogrande \| 0\|acc \|0.7435\|± \|0.0123\| Average: 73.12 ``` AGI-Eval ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------\|------:\|--------\|-----:\|---\|-----:\| \|agieval_aqua_rat \| 0\|acc \|0.2323\|± \|0.0265\| \| \| \|acc_norm\|0.2362\|± \|0.0267\| \|agieval_logiqa_en \| 0\|acc \|0.3871\|± \|0.0191\| \| \| \|acc_norm\|0.3948\|± \|0.0192\| \|agieval_lsat_ar \| 0\|acc \|0.2522\|± \|0.0287\| \| \| \|acc_norm\|0.2304\|± \|0.0278\| \|agieval_lsat_lr \| 0\|acc \|0.5059\|± \|0.0222\| \| \| \|acc_norm\|0.5157\|± \|0.0222\| \|agieval_lsat_rc \| 0\|acc \|0.5911\|± \|0.0300\| \| \| \|acc_norm\|0.5725\|± \|0.0302\| \|agieval_sat_en \| 0\|acc \|0.7476\|± \|0.0303\| \| \| \|acc_norm\|0.7330\|± \|0.0309\| \|agieval_sat_en_without_passage\| 0\|acc \|0.4417\|± \|0.0347\| \| \| \|acc_norm\|0.4126\|± \|0.0344\| \|agieval_sat_math \| 0\|acc \|0.3773\|± \|0.0328\| \| \| \|acc_norm\|0.3500\|± \|0.0322\| Average: 43.07% ``` BigBench Reasoning Test ``` \| Task \|Version\| Metric \|Value \| \|Stderr\| \|------------------------------------------------\|------:\|---------------------\|-----:\|---\|-----:\| \|bigbench_causal_judgement \| 0\|multiple_choice_grade\|0.5316\|± \|0.0363\| \|bigbench_date_understanding \| 0\|multiple_choice_grade\|0.6667\|± \|0.0246\| \|bigbench_disambiguation_qa \| 0\|multiple_choice_grade\|0.3411\|± \|0.0296\| \|bigbench_geometric_shapes \| 0\|multiple_choice_grade\|0.2145\|± \|0.0217\| \| \| \|exact_str_match \|0.0306\|± \|0.0091\| \|bigbench_logical_deduction_five_objects \| 0\|multiple_choice_grade\|0.2860\|± \|0.0202\| \|bigbench_logical_deduction_seven_objects \| 0\|multiple_choice_grade\|0.2086\|± \|0.0154\| \|bigbench_logical_deduction_three_objects \| 0\|multiple_choice_grade\|0.4800\|± \|0.0289\| \|bigbench_movie_recommendation \| 0\|multiple_choice_grade\|0.3620\|± \|0.0215\| \|bigbench_navigate \| 0\|multiple_choice_grade\|0.5000\|± \|0.0158\| \|bigbench_reasoning_about_colored_objects \| 0\|multiple_choice_grade\|0.6630\|± \|0.0106\| \|bigbench_ruin_names \| 0\|multiple_choice_grade\|0.4241\|± \|0.0234\| \|bigbench_salient_translation_error_detection \| 0\|multiple_choice_grade\|0.2285\|± \|0.0133\| \|bigbench_snarks \| 0\|multiple_choice_grade\|0.6796\|± \|0.0348\| \|bigbench_sports_understanding \| 0\|multiple_choice_grade\|0.6491\|± \|0.0152\| \|bigbench_temporal_sequences \| 0\|multiple_choice_grade\|0.2800\|± \|0.0142\| \|bigbench_tracking_shuffled_objects_five_objects \| 0\|multiple_choice_grade\|0.2072\|± \|0.0115\| \|bigbench_tracking_shuffled_objects_seven_objects\| 0\|multiple_choice_grade\|0.1691\|± \|0.0090\| \|bigbench_tracking_shuffled_objects_three_objects\| 0\|multiple_choice_grade\|0.4800\|± \|0.0289\| Average: 40.96% ``` TruthfulQA: ``` \| Task \|Version\|Metric\|Value \| \|Stderr\| \|-------------\|------:\|------\|-----:\|---\|-----:\| \|truthfulqa_mc\| 1\|mc1 \|0.3599\|± \|0.0168\| \| \| \|mc2 \|0.5304\|± \|0.0153\| ``` Average Score Comparison between OpenHermes-1 Llama-2 13B and OpenHermes-2 Mistral 7B against OpenHermes-2.5 on Mistral-7B: ``` \| Bench \| OpenHermes1 13B \| OpenHermes-2 Mistral 7B \| OpenHermes-2 Mistral 7B \| Change/OpenHermes1 \| Change/OpenHermes2 \| \|---------------\|-----------------\|-------------------------\|-------------------------\|--------------------\|--------------------\| \|GPT4All \| 70.36\| 72.68\| 73.12\| +2.76\| +0.44\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|BigBench \| 36.75\| 42.3\| 40.96\| +4.21\| -1.34\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|AGI Eval \| 35.56\| 39.77\| 43.07\| +7.51\| +3.33\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|TruthfulQA \| 46.01\| 50.92\| 53.04\| +7.03\| +2.12\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|Total Score \| 188.68\| 205.67\| 210.19\| +21.51\| +4.52\| \|-------------------------------------------------------------------------------------------------------------------------------\| \|Average Total \| 47.17\| 51.42\| 52.38\| +5.21\| +0.96\| ``` ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ADy7p-xIG8qGlC5ZliqpW.png) HumanEval: On code tasks, I first set out to make a hermes-2 coder, but found that it can have generalist improvements to the model, so I settled for slightly less code capabilities, for maximum generalist ones. That said, code capabilities had a decent jump alongside the overall capabilities of the model: Glaive performed HumanEval testing on Hermes-2.5 and found a score of: 50.7% @ Pass1 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/IeeZnGmEyK73ejq0fKEms.png) # Prompt Format OpenHermes 2.5 now uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue. System prompts are now a thing that matters! Hermes 2.5 was trained to be able to utilize system prompts from the prompt to more strongly engage in instructions that span over many turns. This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns. This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI. Prompt with system instruction (Use whatever system prompt you like, this is just an example!): ``` <\|im_start\|>system You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<\|im_end\|> <\|im_start\|>user Hello, who are you?<\|im_end\|> <\|im_start\|>assistant Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by a man named Teknium, who designed me to assist and support users with their needs and requests.<\|im_end\|> ``` This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the `tokenizer.apply_chat_template()` method: ```python messages = [ {"role": "system", "content": "You are Hermes 2."}, {"role": "user", "content": "Hello, who are you?"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input) ``` When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<\|im_start\|>assistant\n` to your prompt, to ensure that the model continues with an assistant response. To utilize the prompt format without a system prompt, simply leave the line out. Currently, I recommend using LM Studio for chatting with Hermes 2. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box. In LM-Studio, simply select the ChatML Prefix on the settings side pane: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/6317aade83d8d2fd903192d9/ls6WqV-GSxMw2RA3GuQiN.png) # Quantized Models: GGUF: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GGUF GPTQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-GPTQ AWQ: https://huggingface.co/TheBloke/OpenHermes-2.5-Mistral-7B-AWQ EXL2: https://huggingface.co/bartowski/OpenHermes-2.5-Mistral-7B-exl2 [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)	{}	RichardErkhov/teknium_-_OpenHermes-2.5-Mistral-7B-4bits	null	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	null	2024-05-02T20:33:46+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	shallow6414/z3ua8pc	null	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	null	2024-05-02T20:34:05+00:00
audio-classification	transformers		{"license": "apache-2.0"}	pykeio/lku-base-v1.1	null	[ "transformers", "safetensors", "wav2vec2", "audio-classification", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:34:21+00:00
feature-extraction	transformers		{}	lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-256e7-1x016-1-1	null	[ "transformers", "pytorch", "xlm-roberta", "feature-extraction", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:34:56+00:00
text-generation	transformers	# 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]	{"library_name": "transformers", "tags": []}	lunarsylph/stablecell_v61	null	[ "transformers", "safetensors", "stablelm", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	null	2024-05-02T20:34:58+00:00
null	null		{}	Taian87/Eu	null	[ "region:us" ]	null	2024-05-02T20:35:26+00:00

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