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theailearner/AIDoctor-Expert-llama-3-8b-Instruct-bnb-4bit	theailearner	2024-05-30T01:57:48Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T01:57:15Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: theailearner - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
0xfaskety/Qwen-Qwen1.5-7B-1717033726	0xfaskety	2024-05-30T01:55:48Z	9	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T01:48:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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xingfangcasia/public_his_num_1_2_3_4_5_6_epoch_100	xingfangcasia	2024-05-30T01:55:26Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T01:54:57Z	--- license: apache-2.0 ---
Rainnighttram/RED50_Mistral_Instruct_model	Rainnighttram	2024-05-30T01:54:25Z	5	0	transformers	[ "transformers", "safetensors", "gguf", "mistral", "text-generation-inference", "unsloth", "trl", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:quantized:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-30T01:43:00Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit --- # Uploaded model - Developed by: Rainnighttram - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-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)
Yuminyumin/dqn-SpaceInvadersNoFrameskip-v4	Yuminyumin	2024-05-30T01:50:52Z	0	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T01:50:25Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 metrics: - type: mean_reward value: 7.00 +/- 9.00 name: mean_reward verified: false --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Yuminyumin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Yuminyumin -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Yuminyumin ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 100000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 100), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```
ilsp/opus-mt-big-fr-en_ct2_ft-SciLake	ilsp	2024-05-30T01:48:27Z	5	2	transformers	[ "transformers", "translation", "fr", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	translation	2024-05-29T19:10:16Z	--- license: apache-2.0 language: - fr - en pipeline_tag: translation --- # French-English Translation Model for the Scientific Domain ## Description This is a CTranslate2 French-English translation model for the scientific domain, which uses the FR-EN OPUS-MT Transformer-Big [(link)](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/fra-eng) as its base model. It has been fine-tuned on a large parallel corpus with scientific texts, with special focus to the four pilot domains of the [SciLake](https://scilake.eu/) project: - Neuroscience - Cancer - Transportation - Energy ## Dataset The fine-tuning dataset consists of 1,850,031 EN-ES parallel sentences extracted from parallel theses and abstracts which have been acquired from multiple academic repositories. ## Evaluation We have evaluated the base and the fine-tuned models on 5 test sets: - Four which correspond to the pilot domains (Neuroscience, Cancer, Transportation, Energy) with each one containing 1,000 parallel sentences. - A general scientific which contains 3,000 parallel sentences from a wide range of scientific texts in other domains. \| Model \| Average of 4 domains \| \| \| General Scientific\| \| \| \|-------------\|----------------------\|---------------\|---------------\|-------------------\|---------------\|---------------\| \| \| SacreBLEU \| chrF2++ \| COMET \| SacreBLEU \| chrF2++ \| COMET \| \| Base \| 37.6 \| 63.6 \| 57.5 \| 38.4 \| 63.3 \| 57.2 \| \| Fine-Tuned \| 40.2 \| 65.7 \| 59.9 \| 40.7 \| 65.3 \| 59.5 \| \| Improvement \| +2.6 \| +2.1 \| +2.4 \| +2.3 \| +2 \| +2.3 \| ## Usage ``` pip install ctranslate2 sentencepiece huggingface_hub ``` ```python import ctranslate2 import sentencepiece as spm from huggingface_hub import snapshot_download repo_id = "ilsp/opus-mt-big-fr-en_ct2_ft-SciLake" # REPLACE WITH ACTUAL LOCAL DIRECTORY WHERE THE MODEL WILL BE DOWNLOADED local_dir = "" model_path = snapshot_download(repo_id=repo_id, local_dir=local_dir) translator = ctranslate2.Translator(model_path, compute_type="auto") sp_enc = spm.SentencePieceProcessor() sp_enc.load(f"{model_path}/source.spm") sp_dec = spm.SentencePieceProcessor() sp_dec.load(f"{model_path}/target.spm") def translate_text(input_text, sp_enc=sp_enc, sp_dec=sp_dec, translator=translator, beam_size=6): input_tokens = sp_enc.encode(input_text, out_type=str) results = translator.translate_batch([input_tokens], beam_size=beam_size, length_penalty=0, max_decoding_length=512, replace_unknowns=True) output_tokens = results[0].hypotheses[0] output_text = sp_dec.decode(output_tokens) return output_text input_text = "Les données radars transportées au sein du système ATM (Air Traffic Management) permet aux contrôleurs de l’aviation civile d’effectuer le contrôle aérien et ainsi la sécurité des avions et de leurs passagers." translate_text(input_text) # OUTPUT # Radar data carried within the ATM (Air Traffic Management) system allows civil aviation controllers to perform air traffic control and thus the safety of aircraft and their passengers. ``` ## Acknowledgements This work was created within the [SciLake](https://scilake.eu/) project. We are grateful to the SciLake project for providing the resources and support that made this work possible. This project has received funding from the European Union’s Horizon Europe framework programme under grant agreement No. 101058573.
Madhumita19/merged-mistralfinetuned-v3.0-10epoch	Madhumita19	2024-05-30T01:44:43Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T01:41:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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. 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internlm/AlchemistCoder-L-7B	internlm	2024-05-30T01:44:33Z	17	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code generation", "arxiv:2405.19265", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T16:53:23Z	--- license: apache-2.0 tags: - code generation --- # AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [[🤗 HuggingFace](https://huggingface.co/internlm/AlchemistCoder-DS-6.7B)] [[📃 Paper](https://arxiv.org/abs/2405.19265)] [[🌐 Project Page](https://internlm.github.io/AlchemistCoder/)] ## ✨ Highlights > Abstract: Open-source Large Language Models (LLMs) and their specialized variants, particularly Code LLMs, have recently delivered impressive performance. However, previous Code LLMs are typically fine-tuned on single-source data with limited quality and diversity, which may insufficiently elicit the potential of pre-trained Code LLMs. In this paper, we present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. To achieve this, we pioneer to unveil inherent conflicts among the various styles and qualities in multi-source code corpora and introduce data-specific prompts with hindsight relabeling, termed AlchemistPrompts, to harmonize different data sources and instruction-response pairs. Additionally, we propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review. Extensive experiments demonstrate that AlchemistCoder holds a clear lead among all models of the same size (6.7B/7B) and rivals or even surpasses larger models (15B/33B/70B), showcasing the efficacy of our method in refining instruction-following capabilities and advancing the boundaries of code intelligence. - AlchemistPrompts: Designed as data-specific prompts for harmonizing inherent conflicts in multi-source data and mitigating the instruction/response misalignment at a fined-grained level. - Code Comprehenstion Tasks: Sourced from the process of data construction, consisting of instruction evolution, data filtering, and code review. - Harmonized Multi-source Data: Instruction tuned on 200M tokens, including 6 types of high-quality data. - Superior Model Performance: Surpassing all the open-source models of the same size (6.7/7B), and rivaling or even beating larger models (15B/33B/70B/ChatGPT) on 6 code benchmarks. - Advanced generic capabilities: Demonstrated by the significant improvements on MMLU, BBH, and GSM8K. ## 🚀 Quick Start ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("internlm/AlchemistCoder-L-7B", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda() model = model.eval() input_text = "Implement the Dijkstra algorithm in Python" inputs = tokenizer(input_text, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_length=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` ## 🧪 Evaluation and Fine-tune Please refer to [AlchemistCoder](https://github.com/InternLM/AlchemistCoder) and [InternLM*](https://github.com/InternLM/InternLM/tree/main). ## 😃 Acknowledgments AlchemistCoder* is built with [InternLM](https://github.com/InternLM) and [OpenCompass](https://github.com/open-compass). Thanks for their awesome work! ## 📧 Contact If you have any questions, please create an issue on this repository or contact us at: - [email protected] - [email protected] ## 🌟 Citation If you find our work useful, please consider citing: ```bibtex @misc{song2024alchemistcoder, title={AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data}, author={Zifan Song and Yudong Wang and Wenwei Zhang and Kuikun Liu and Chengqi Lyu and Demin Song and Qipeng Guo and Hang Yan and Dahua Lin and Kai Chen and Cairong Zhao}, year={2024}, eprint={2405.19265}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
gordonhu/MQT-LLaVA-7b	gordonhu	2024-05-30T01:40:06Z	288	4	transformers	[ "transformers", "safetensors", "llava_llama", "text-generation", "image-text-to-text", "arxiv:2405.19315", "autotrain_compatible", "region:us" ]	image-text-to-text	2024-05-28T19:31:20Z	--- inference: false pipeline_tag: image-text-to-text --- <br> <br> # MQT-LLaVA Model Card ## Model details Model type: MQT-LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Model date: MQT-LLaVA-7B was trained in May 2024. [Paper](https://arxiv.org/abs/2405.19315) <!-- Paper or resources for more information: https:// --> ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved. Where to send questions or comments about the model: https://github.com/gordonhu608/MQT-LLaVA/issues ## Intended use Primary intended uses: The primary use of MQT-LLaVA is research on large multimodal models and chatbots. Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. ## Training dataset - 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP. - 158K GPT-generated multimodal instruction-following data. - 450K academic-task-oriented VQA data mixture. - 40K ShareGPT data. ## Evaluation dataset A collection of 11 benchmarks, including 4 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
Madhumita19/mistralfinetuned-v3.0-10epoch	Madhumita19	2024-05-30T01:38:47Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T01:38:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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searchfind/waynemodel	searchfind	2024-05-30T01:38:09Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T01:38:09Z	--- license: apache-2.0 ---
galocher/lora_model2	galocher	2024-05-30T01:36:24Z	0	0	transformers	[ "transformers", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-30T01:36:23Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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IEITYuan/Yuan2-M32	IEITYuan	2024-05-30T01:33:40Z	7	4	transformers	[ "transformers", "yuan", "text-generation", "custom_code", "arxiv:2405.17976", "license:apache-2.0", "autotrain_compatible", "region:us" ]	text-generation	2024-05-27T08:16:56Z	--- license: apache-2.0 --- <div align="center"> <h1> Yuan2.0-M32: Mixture of Experts with Attention Router </h1> </div> <p align="center"> 🌎 <a href="https://github.com/IEIT-Yuan/Yuan2.0-M32" target="_blank">GitHub</a> • 🤗 <a href="https://huggingface.co/IEITYuan" target="_blank">Hugging Face</a> • 💬 <a href="https://github.com/IEIT-Yuan/Yuan-2.0/blob/main/images/%E6%BA%90%E5%85%AC%E4%BC%97%E5%8F%B7%E4%BA%8C%E7%BB%B4%E7%A0%81.png" target="_blank">WeChat</a>• 📎 <a href="https://arxiv.org/abs/2405.17976" target="_blank">Yuan2.0-M32 Paper</a> </p> <div align="center"> <a href="code_license"> <img alt="Code License" src="https://img.shields.io/badge/Apache%202.0%20-green?style=flat&label=Code%20License&link=https%3A%2F%2Fgithub.com%2FIEIT-Yuan%2FYuan-2.0-MoE%3Ftab%3DApache-2.0-1-ov-file"/> </a> <a href="model_license"> <img alt="Model License" src="https://img.shields.io/badge/Yuan2.0%20License-blue?style=flat&logoColor=blue&label=Model%20License&color=blue&link=https%3A%2F%2Fgithub.com%2FIEIT-Yuan%2FYuan-2.0%2Fblob%2Fmain%2FLICENSE-Yuan" /> </a> </div> ----- ## 1. Introduction Yuan2.0-M32 is a Mixture-of-Experts (MoE) language model with 32 experts, of which 2 are active. A new router network, Attention Router, is proposed and has been adopted for more efficient expert selection, boosting accuracy by 3.8% over models using a classical router network. Yuan 2.0-M32 is trained from scratch with 2000B tokens, and its training computation is only 9.25% of that required by a dense model of the same parameter scale. Demonstrating competitive capabilities in coding, math, and various specialized fields, Yuan2.0-M32 operates with only 3.7B active parameters out of a total 40B, and a forward computation of 7.4 GFLOPS per token, which is just 1/19th of Llama3-70B's requirement. Yuan 2.0-M32 has surpassed Llama3-70B on the MATH and ARC-Challenge benchmarks, achieving accuracies of 55.9% and 95.8%, respectively. The basic information of the Yuan2.0-M32 model is as follows: + Total Parameters ： 40B <br> + Experts： 32 <br> + Active Experts： 2 <br> + Active Parameters： 3.7B <br> + Training Tokens： 2000B tokens <br> + Sequence Length： 16K <br> The technical report for the Yuan2.0-M32 model has been released, and you can find more detailed technical information and evaluation results by referring to the <a href="https://arxiv.org/abs/2405.17976" target="_blank">paper</a>. ## 2. Model Downloads \| Model \| Sequence Length \| Type \| Download \| \| :----------: \| :------: \| :-------: \|:---------------------------: \| \| Yuan2.0-M32 \| 16K \| Megatron \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32) \| Yuan2.0-M32-HF \| 16K \| HuggingFace \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-hf) \| Yuan2.0-M32-GGUF \| 16K \| GGUF \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-gguf) \| Yuan2.0-M32-GGUF-INT4 \| 16K \| GGUF \| [HuggingFace](https://huggingface.co/IEITYuan/Yuan2-M32-gguf-int4) ## 3. Evaluation 3.1 Benchmarks 🏆 We conducted a thorough evaluation of the Yuan2.0-M32 model across a range of benchmarks, including HumanEval, GSM8K, MMLU, Math, and ARC-Challenge. These benchmarks are designed to test the model's proficiency in key areas such as natural language understanding, knowledge acquisition, mathematical computation and reasoning, and code generation. The Yuan2.0-M32 has shown a consistent and significant advantage over other models like Llama3-8B and Mistral-8×7B, excelling in all evaluated tasks. Remarkably, its overall performance is on par with the more substantial Llama3-70B model.The detailed evaluation results are outlined in the subsequent table. \| Model \| HumanEval \| GSM8K \| MMLU \| Math \| ARC-C\* \| \| ------------------ \| :---------------: \| :------------: \| :---------------: \| :---------------: \| :---------------:\| \| Llama3-70B \| 81.7% \| 93% \| 80.3 \| 50.4% \| 93.3% \| \| Llama3-8B \| 62.2% \| 79.6% \| 68.4% \| 30% \| 78.6% \| \| Phi-3-medium \| 62.2% \| 91.0% \| 78.0% \| - \| 91.6% \| \| Phi-3-small \| 61% \| 89.6% \| 75.7% \| - \| 90.7% \| \| Phi-3-mini \| 58.5% \| 82.5% \| 68.8% \| - \| 84.9% \| \| Mistral-822B \| 45.1% \| 78.6% \| 77.8% \| 41,8% \| 91.3% \| \| Mistral-87B \| 40.2% \| 58.4% \| 70.86% \| 28.4% \| 85.9% \| \| Yuan2.0-M32 \| 74.4% \| 92.7% \| 72.2% \| 55.9% \| 95.8% \| \* __ARC-C__: AI2 Reasoning Challenge (ARC) benchmark contains more complex parts that need further reasoning. ----- 3.2 Computational Utilization for Model \| Model \| Params (B) \| Active Params (B) \| GFLOPs/token (Inference) \| GFLOPS/token (Fine-tune) \| Mean Accuracy \| Average Accuracy/GFLOPSs per token (Inference) \| \| ------------------ \| :---------------: \| :------------: \| :---------------: \| :---------------: \| :---------------:\|:---------------:\| \| Llama3-70B \| 70 \| 70 \| 140 \| 420 \| 79.25 \| 0.57 \| \| Llama3-8B \| 8 \| 8 \| 16 \| 48 \| 64.15 \| 4.00 \| \| Mistral-822B \| 141 \| 39 \| 78 \| 234 \| 72.38 \| 0.93 \| \| Mistral-87B \| 47 \| 12.9 \| 25.8 \| 77.3 \| 60.83 \| 2.36 \| \| Yuan2.0-M32 \| 40 \| 3.7 \| 7.4 \| 22.2 \| 79.15 \| 10.69 \| ## 4. Quick Start 4.1 Environment Config We strongly recommend using the latest release of docker images of Yuan2.0-M32.You can launch an instance of the Yuan 2.0 container with the following Docker commands: ```bash docker pull yuanmodel/yuan2.0:m32 docker run --gpus all --privileged --ulimit stack=68719476736 --shm-size=1000G -itd -v /path/to/yuan_2.0:/workspace/yuan_2.0 -v /path/to/dataset:/workspace/dataset -v /path/to/checkpoints:/workspace/checkpoints --name your_name yuanmodel/yuan2.0:m32 docker exec -it your_name bash ``` 4.2 Data Preprocess We have provided the data preprocess script. See documentation [here](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/docs/data_process.md ). 4.3 Model Pretrain We've provided several scripts for pretraining in the [`example`](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/examples). The details can be seen from documentation [here](https://github.com/IEIT-Yuan/Yuan2.0-M32/blob/main/docs/pretrain.md). 4.4 Inference Service For a detailed deployment plan, please refer to [vllm](https://github.com/IEIT-Yuan/Yuan2.0-M32/edit/main/vllm/README_Yuan_vllm.md). - For more information, please refer to [GitHub](https://github.com/IEIT-Yuan/Yuan2.0-M32) repository. ## 5. Statement of Agreement The use of the source code in this repository requires compliance with the open source license agreement Apache 2.0. The Yuan2.0 model supports commercial use and does not require authorization. Please understand and comply with the [《Yuan2.0 Model License Agreement》](./LICENSE-Yuan). Do not use the open source model and code, as well as derivatives generated from open source projects, for any purposes that may cause harm to the country and society, or for any services that have not undergone security assessment and filing. Although we have taken measures to ensure the compliance and accuracy of the data during training, the model has a huge number of parameters and is affected by probability and randomness factors. We cannot guarantee the accuracy of the output content, and the model is easily misled by input instructions. This project does not assume any data security, public opinion risks, or any model misleading, abusing, spreading caused by open-source models and code Risks and responsibilities arising from improper utilization You will be solely responsible for the risks and consequences arising from the use, copying, distribution, and modification of the model in this open source project ## 6. Contact Us If you have any questions, please raise an issue or contact us at [email protected]
HachiML/Mistral-7B-Instruct-v0.3-dpo-lora	HachiML	2024-05-30T01:27:18Z	8	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T01:23:28Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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LiteLLMs/Meta-Llama-3-70B-GGUF	LiteLLMs	2024-05-30T01:25:42Z	24	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "GGUF", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:10:34Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - GGUF pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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The courts of California shall have exclusive jurisdiction\ \ of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use\ \ Policy\nMeta is committed to promoting safe and fair use of its tools and features,\ \ including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable\ \ Use Policy (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n\ #### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly.\ \ You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate\ \ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\ \ contribute to, encourage, plan, incite, or further illegal or unlawful activity\ \ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\ \ or harm to children, including the solicitation, creation, acquisition, or dissemination\ \ of child exploitative content or failure to report Child Sexual Abuse Material\n\ \ 3. Human trafficking, exploitation, and sexual violence\n 4. The\ \ illegal distribution of information or materials to minors, including obscene\ \ materials, or failure to employ legally required age-gating in connection with\ \ such information or materials.\n 5. Sexual solicitation\n 6. Any\ \ other criminal activity\n 2. Engage in, promote, incite, or facilitate the\ \ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\ \ 3. Engage in, promote, incite, or facilitate discrimination or other unlawful\ \ or harmful conduct in the provision of employment, employment benefits, credit,\ \ housing, other economic benefits, or other essential goods and services\n 4.\ \ Engage in the unauthorized or unlicensed practice of any profession including,\ \ but not limited to, financial, legal, medical/health, or related professional\ \ practices\n 5. Collect, process, disclose, generate, or infer health, demographic,\ \ or other sensitive personal or private information about individuals without rights\ \ and consents required by applicable laws\n 6. Engage in or facilitate any action\ \ or generate any content that infringes, misappropriates, or otherwise violates\ \ any third-party rights, including the outputs or results of any products or services\ \ using the Llama Materials\n 7. Create, generate, or facilitate the creation\ \ of malicious code, malware, computer viruses or do anything else that could disable,\ \ overburden, interfere with or impair the proper working, integrity, operation\ \ or appearance of a website or computer system\n2. Engage in, promote, incite,\ \ facilitate, or assist in the planning or development of activities that present\ \ a risk of death or bodily harm to individuals, including use of Meta Llama 3 related\ \ to the following:\n 1. Military, warfare, nuclear industries or applications,\ \ espionage, use for materials or activities that are subject to the International\ \ Traffic Arms Regulations (ITAR) maintained by the United States Department of\ \ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\ \ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\ \ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\ \ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\ \ content intended to incite or promote violence, abuse, or any infliction of bodily\ \ harm to an individual\n3. Intentionally deceive or mislead others, including use\ \ of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering\ \ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\ \ or furthering defamatory content, including the creation of defamatory statements,\ \ images, or other content\n 3. Generating, promoting, or further distributing\ \ spam\n 4. Impersonating another individual without consent, authorization,\ \ or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are\ \ human-generated\n 6. Generating or facilitating false online engagement, including\ \ fake reviews and other means of fake online engagement\n4. Fail to appropriately\ \ disclose to end users any known dangers of your AI system\nPlease report any violation\ \ of this Policy, software “bug,” or other problems that could lead to a violation\ \ of this Policy through one of the following means:\n * Reporting issues with\ \ the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n\ \ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\ \ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\ \ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]" extra_gated_fields: First Name: text Last Name: text Date of birth: date_picker Country: country Affiliation: text geo: ip_location ? By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit quantized_by: andrijdavid --- # Meta-Llama-3-70B-GGUF - Original model: [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) <!-- description start --> ## Description This repo contains GGUF format model files for [Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Meta-Llama-3-70B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Meta-Llama-3-70B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Meta-Llama-3-70B-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Meta-Llama-3-70B-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Meta-Llama-3-70B ## Model Details Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety. Model developers Meta Variations Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants. Input Models input text only. Output Models generate text and code only. Model Architecture Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. <table> <tr> <td> </td> <td><strong>Training Data</strong> </td> <td><strong>Params</strong> </td> <td><strong>Context length</strong> </td> <td><strong>GQA</strong> </td> <td><strong>Token count</strong> </td> <td><strong>Knowledge cutoff</strong> </td> </tr> <tr> <td rowspan="2" >Llama 3 </td> <td rowspan="2" >A new mix of publicly available online data. </td> <td>8B </td> <td>8k </td> <td>Yes </td> <td rowspan="2" >15T+ </td> <td>March, 2023 </td> </tr> <tr> <td>70B </td> <td>8k </td> <td>Yes </td> <td>December, 2023 </td> </tr> </table> Llama 3 family of models. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability. Model Release Date April 18, 2024. Status This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback. License A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license) Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes). ## Intended Use Intended Use Cases Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English. Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy. ## How to use This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase. ### Use with transformers See the snippet below for usage with Transformers: ```python >>> import transformers >>> import torch >>> model_id = "meta-llama/Meta-Llama-3-70B" >>> pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto" ) >>> pipeline("Hey how are you doing today?") ``` ### Use with `llama3` Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3). To download Original checkpoints, see the example command below leveraging `huggingface-cli`: ``` huggingface-cli download meta-llama/Meta-Llama-3-70B --include "original/" --local-dir Meta-Llama-3-70B ``` For Hugging Face support, we recommend using transformers or TGI, but a similar command works. ## Hardware and Software Training Factors* We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute. Carbon Footprint Pretraining utilized a cumulative 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program. <table> <tr> <td> </td> <td><strong>Time (GPU hours)</strong> </td> <td><strong>Power Consumption (W)</strong> </td> <td><strong>Carbon Emitted(tCO2eq)</strong> </td> </tr> <tr> <td>Llama 3 8B </td> <td>1.3M </td> <td>700 </td> <td>390 </td> </tr> <tr> <td>Llama 3 70B </td> <td>6.4M </td> <td>700 </td> <td>1900 </td> </tr> <tr> <td>Total </td> <td>7.7M </td> <td> </td> <td>2290 </td> </tr> </table> CO2 emissions during pre-training. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others. ## Training Data Overview Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data. Data Freshness The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively. ## Benchmarks In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md). ### Base pretrained models <table> <tr> <td><strong>Category</strong> </td> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama2 7B</strong> </td> <td><strong>Llama2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama2 70B</strong> </td> </tr> <tr> <td rowspan="6" >General </td> <td>MMLU (5-shot) </td> <td>66.6 </td> <td>45.7 </td> <td>53.8 </td> <td>79.5 </td> <td>69.7 </td> </tr> <tr> <td>AGIEval English (3-5 shot) </td> <td>45.9 </td> <td>28.8 </td> <td>38.7 </td> <td>63.0 </td> <td>54.8 </td> </tr> <tr> <td>CommonSenseQA (7-shot) </td> <td>72.6 </td> <td>57.6 </td> <td>67.6 </td> <td>83.8 </td> <td>78.7 </td> </tr> <tr> <td>Winogrande (5-shot) </td> <td>76.1 </td> <td>73.3 </td> <td>75.4 </td> <td>83.1 </td> <td>81.8 </td> </tr> <tr> <td>BIG-Bench Hard (3-shot, CoT) </td> <td>61.1 </td> <td>38.1 </td> <td>47.0 </td> <td>81.3 </td> <td>65.7 </td> </tr> <tr> <td>ARC-Challenge (25-shot) </td> <td>78.6 </td> <td>53.7 </td> <td>67.6 </td> <td>93.0 </td> <td>85.3 </td> </tr> <tr> <td>Knowledge reasoning </td> <td>TriviaQA-Wiki (5-shot) </td> <td>78.5 </td> <td>72.1 </td> <td>79.6 </td> <td>89.7 </td> <td>87.5 </td> </tr> <tr> <td rowspan="4" >Reading comprehension </td> <td>SQuAD (1-shot) </td> <td>76.4 </td> <td>72.2 </td> <td>72.1 </td> <td>85.6 </td> <td>82.6 </td> </tr> <tr> <td>QuAC (1-shot, F1) </td> <td>44.4 </td> <td>39.6 </td> <td>44.9 </td> <td>51.1 </td> <td>49.4 </td> </tr> <tr> <td>BoolQ (0-shot) </td> <td>75.7 </td> <td>65.5 </td> <td>66.9 </td> <td>79.0 </td> <td>73.1 </td> </tr> <tr> <td>DROP (3-shot, F1) </td> <td>58.4 </td> <td>37.9 </td> <td>49.8 </td> <td>79.7 </td> <td>70.2 </td> </tr> </table> ### Instruction tuned models <table> <tr> <td><strong>Benchmark</strong> </td> <td><strong>Llama 3 8B</strong> </td> <td><strong>Llama 2 7B</strong> </td> <td><strong>Llama 2 13B</strong> </td> <td><strong>Llama 3 70B</strong> </td> <td><strong>Llama 2 70B</strong> </td> </tr> <tr> <td>MMLU (5-shot) </td> <td>68.4 </td> <td>34.1 </td> <td>47.8 </td> <td>82.0 </td> <td>52.9 </td> </tr> <tr> <td>GPQA (0-shot) </td> <td>34.2 </td> <td>21.7 </td> <td>22.3 </td> <td>39.5 </td> <td>21.0 </td> </tr> <tr> <td>HumanEval (0-shot) </td> <td>62.2 </td> <td>7.9 </td> <td>14.0 </td> <td>81.7 </td> <td>25.6 </td> </tr> <tr> <td>GSM-8K (8-shot, CoT) </td> <td>79.6 </td> <td>25.7 </td> <td>77.4 </td> <td>93.0 </td> <td>57.5 </td> </tr> <tr> <td>MATH (4-shot, CoT) </td> <td>30.0 </td> <td>3.8 </td> <td>6.7 </td> <td>50.4 </td> <td>11.6 </td> </tr> </table> ### Responsibility & Safety We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community. Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications. Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience. As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started. #### Llama 3-Instruct As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case. <span style="text-decoration:underline;">Safety</span> For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable. <span style="text-decoration:underline;">Refusals</span> In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2. We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date. #### Responsible release In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision. Misuse If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/). #### Critical risks <span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives) We have conducted a two fold assessment of the safety of the model in this area: * Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks. * Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model). ### <span style="text-decoration:underline;">Cyber Security </span> We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval). ### <span style="text-decoration:underline;">Child Safety</span> Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. ### Community Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama). Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community. ## Ethical Considerations and Limitations The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety. Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) ## Citation instructions @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ## Contributors Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos <!-- original-model-card end -->
BahaaEldin0/bert-base-uncased	BahaaEldin0	2024-05-30T01:25:18Z	111	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-30T01:24:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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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. 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Sempiterno111/programa	Sempiterno111	2024-05-30T01:23:06Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T01:23:06Z	--- license: apache-2.0 ---
ilsp/opus-mt-pt-en_ct2_ft-SciLake	ilsp	2024-05-30T01:14:30Z	63	2	transformers	[ "transformers", "translation", "pt", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	translation	2024-05-29T19:10:45Z	--- license: apache-2.0 language: - pt - en pipeline_tag: translation --- # Portuguese-English Translation Model for the Scientific Domain ## Description This is a CTranslate2 Portuguese-English translation model for the scientific domain, which uses the PT-EN OPUS-MT Transformer-Align [(link)](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/por-eng) as its base model. It has been fine-tuned on a large parallel corpus with scientific texts, with special focus to the four pilot domains of the [SciLake](https://scilake.eu/) project: - Neuroscience - Cancer - Transportation - Energy ## Dataset The fine-tuning dataset consists of 5,705,469 EN-PT parallel sentences extracted from parallel theses and abstracts which have been acquired from multiple academic repositories. ## Evaluation We have evaluated the base and the fine-tuned models on 5 test sets: - Four which correspond to the pilot domains (Neuroscience, Cancer, Transportation, Energy) with each one containing 1,000 parallel sentences. - A general scientific which contains 3,000 parallel sentences from a wide range of scientific texts in other domains. \| Model \| Average of 4 domains \| \| \| General Scientific\| \| \| \|-------------\|----------------------\|---------------\|---------------\|-------------------\|---------------\|---------------\| \| \| SacreBLEU \| chrF2++ \| COMET \| SacreBLEU \| chrF2++ \| COMET \| \| Base \| 46 \| 68.3 \| 66.7 \| 44.9 \| 67.7 \| 66.3 \| \| Fine-Tuned \| 48.4 \| 69.9 \| 67.3 \| 47.3 \| 69.1 \| 67.8 \| \| Improvement \| +2.4 \| +1.6 \| +0.9 \| +2.4 \| +1.4 \| +1.5 \| ## Usage ``` pip install ctranslate2 sentencepiece huggingface_hub ``` ```python import ctranslate2 import sentencepiece as spm from huggingface_hub import snapshot_download repo_id = "ilsp/opus-mt-pt-en_ct2_ft-SciLake" # REPLACE WITH ACTUAL LOCAL DIRECTORY WHERE THE MODEL WILL BE DOWNLOADED local_dir = "" model_path = snapshot_download(repo_id=repo_id, local_dir=local_dir) translator = ctranslate2.Translator(model_path, compute_type="auto") sp_enc = spm.SentencePieceProcessor() sp_enc.load(f"{model_path}/source.spm") sp_dec = spm.SentencePieceProcessor() sp_dec.load(f"{model_path}/target.spm") def translate_text(input_text, sp_enc=sp_enc, sp_dec=sp_dec, translator=translator, beam_size=6): input_tokens = sp_enc.encode(input_text, out_type=str) results = translator.translate_batch([input_tokens], beam_size=beam_size, length_penalty=0, max_decoding_length=512, replace_unknowns=True) output_tokens = results[0].hypotheses[0] output_text = sp_dec.decode(output_tokens) return output_text input_text = "Na osteoartríte (OA) a degeneração progressiva das estruturas articulares activa continuamente nociceptores levando ao desenvolvimento de dor crónica e a déficits emocionais e cognitivos." translate_text(input_text) # OUTPUT # In osteoarthritis (OA), progressive degeneration of articular structures continuously activates nociceptors leading to the development of chronic pain and emotional and cognitive deficits. ``` ## Acknowledgements This work was created within the [SciLake](https://scilake.eu/) project. We are grateful to the SciLake project for providing the resources and support that made this work possible. This project has received funding from the European Union’s Horizon Europe framework programme under grant agreement No. 101058573.
Mantis-VL/mantis-8b-idefics2-video-eval-high-res-35k-mantis-2epoch_4096	Mantis-VL	2024-05-30T01:08:20Z	4	0	transformers	[ "transformers", "safetensors", "idefics2", "image-text-to-text", "generated_from_trainer", "base_model:TIGER-Lab/Mantis-8B-Idefics2", "base_model:finetune:TIGER-Lab/Mantis-8B-Idefics2", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-29T12:34:59Z	--- license: apache-2.0 base_model: TIGER-Lab/Mantis-8B-Idefics2 tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval-high-res-35k-mantis-2epoch_4096 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mantis-8b-idefics2-video-eval-high-res-35k-mantis-2epoch_4096 This model is a fine-tuned version of [TIGER-Lab/Mantis-8B-Idefics2](https://huggingface.co/TIGER-Lab/Mantis-8B-Idefics2) 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: 5e-06 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2.0 ### Training results ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
jacobdev/llama3_sft_demo3	jacobdev	2024-05-30T00:56:34Z	3	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T00:54:06Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: jacobdev - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
ChenWeiLi/MedPhi-3-mini_v1	ChenWeiLi	2024-05-30T00:56:29Z	185	2	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:30:34Z	--- license: apache-2.0 --- ### Evaluation \| Tasks \|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|-------------------------------\|-------\|------\|-----:\|--------\|-----:\|---\|-----:\| \| - medmcqa \|Yaml \|none \| 0\|acc \|0.5408\|± \|0.0077\| \| \| \|none \| 0\|acc_norm\|0.5408\|± \|0.0077\| \| - medqa_4options \|Yaml \|none \| 0\|acc \|0.5711\|± \|0.0139\| \| \| \|none \| 0\|acc_norm\|0.5711\|± \|0.0139\| \| - anatomy (mmlu) \| 0\|none \| 0\|acc \|0.6815\|± \|0.0402\| \| - clinical_knowledge (mmlu) \| 0\|none \| 0\|acc \|0.7434\|± \|0.0269\| \| - college_biology (mmlu) \| 0\|none \| 0\|acc \|0.8056\|± \|0.0331\| \| - college_medicine (mmlu) \| 0\|none \| 0\|acc \|0.6647\|± \|0.0360\| \| - medical_genetics (mmlu) \| 0\|none \| 0\|acc \|0.7300\|± \|0.0446\| \| - professional_medicine (mmlu)\| 0\|none \| 0\|acc \|0.7353\|± \|0.0268\| \|stem \|N/A \|none \| 0\|acc_norm\|0.5478\|± \|0.0067\| \| \| \|none \| 0\|acc \|0.5909\|± \|0.0058\| \| - pubmedqa \| 1\|none \| 0\|acc \|0.7620\|± \|0.0191\| \|Groups\|Version\|Filter\|n-shot\| Metric \|Value \| \|Stderr\| \|------\|-------\|------\|-----:\|--------\|-----:\|---\|-----:\| \|stem \|N/A \|none \| 0\|acc_norm\|0.5478\|± \|0.0067\| \| \| \|none \| 0\|acc \|0.5909\|± \|0.0058\| ![Comparison Image](https://huggingface.co/ChenWeiLi/MedPhi-3-mini_v1/resolve/main/compare.png)
stifi/gemma-2b-it-test	stifi	2024-05-30T00:52:02Z	2	0	peft	[ "peft", "safetensors", "gemma", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:gemma", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-30T00:41:47Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: gemma-2b-it results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # gemma-2b-it This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - training_steps: 10 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0 - Pytorch 2.3.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
sudipto-ducs/InLegalLLaMA-Instruct	sudipto-ducs	2024-05-30T00:49:52Z	15	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "legal", "table-question-answering", "en", "base_model:sudipto-ducs/InLegalLLaMA", "base_model:finetune:sudipto-ducs/InLegalLLaMA", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	table-question-answering	2024-05-30T00:36:42Z	--- license: llama2 tags: - llama-factory - legal base_model: sudipto-ducs/InLegalLLaMA model-index: - name: sudipto-ducs/InLegalLLaMA-Instruct results: [] language: - en pipeline_tag: table-question-answering --- <!-- 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. --> # InLegalLLaMA-Instruct This model is a fine-tuned version of [sudipto-ducs/InLegalLLaMA](https://huggingface.co/sudipto-ducs/InLegalLLaMA) on the legalkg_dataset_prompts, the legal_semantic_segmentation and the lima datasets. ## 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: 3e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
sudipto-ducs/InLegalLLaMA	sudipto-ducs	2024-05-30T00:49:39Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "legal", "conversational", "en", "dataset:sudipto-ducs/InLegalLLaMA-data", "base_model:meta-llama/Llama-2-7b-hf", "base_model:finetune:meta-llama/Llama-2-7b-hf", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:36:24Z	--- license: llama2 tags: - llama-factory - legal base_model: meta-llama/Llama-2-7b-hf model-index: - name: inlegalllama-pt-15-04-2024 results: [] datasets: - sudipto-ducs/InLegalLLaMA-data language: - en pipeline_tag: text-generation --- <!-- 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. --> # InLegalLLaMA 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 inlegalllama-laws and the inlegalllama-sci datasets. ## 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: 0.0003 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.39.0 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf	RichardErkhov	2024-05-30T00:45:44Z	66	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-29T21:36:21Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) airoboros-m-7b-3.0 - GGUF - Model creator: https://huggingface.co/jondurbin/ - Original model: https://huggingface.co/jondurbin/airoboros-m-7b-3.0/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [airoboros-m-7b-3.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [airoboros-m-7b-3.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [airoboros-m-7b-3.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [airoboros-m-7b-3.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [airoboros-m-7b-3.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [airoboros-m-7b-3.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [airoboros-m-7b-3.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [airoboros-m-7b-3.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [airoboros-m-7b-3.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [airoboros-m-7b-3.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [airoboros-m-7b-3.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [airoboros-m-7b-3.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [airoboros-m-7b-3.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [airoboros-m-7b-3.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [airoboros-m-7b-3.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [airoboros-m-7b-3.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [airoboros-m-7b-3.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [airoboros-m-7b-3.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [airoboros-m-7b-3.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [airoboros-m-7b-3.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [airoboros-m-7b-3.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [airoboros-m-7b-3.0.Q8_0.gguf](https://huggingface.co/RichardErkhov/jondurbin_-_airoboros-m-7b-3.0-gguf/blob/main/airoboros-m-7b-3.0.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- license: apache-2.0 datasets: - jondurbin/airoboros-3.0 --- ### Overview Another experimental model, using mostly sythetic data generated by [airoboros](https://github.com/jondurbin/airoboros) #### IMPORTANT NOTE - llama-2 chat format!!! The airobors 3.0 models have been updated to use llama-2 chat format, rather than vicuna style user/assistant! This is a breaking change, although most inference systems support llama-2 chat templating. See prompt format below for all of the details. #### Highlights A model built on [airoboros-3.0 dataset](https://hf.co/datasets/jondurbin/airoboros-3.0), which is a continuation of [airoboros-2.2.1 dataset](https://hf.co/datasets/jondurbin/airoboros-2.2.1) with the following extras: * MathJSON - this is not yet part of the code directly, but can be easily parsed and executed, as a way to perform deterministic calculations. LLMs suck at math, so this does problem formulation and let's the work be done by traditional operations. * Human-generated RP multi-turn conversation - provided by an anon, this is a human created RP dataset, meant to assist with multi-turn coherency and chat experience. This is a fairly general purpose model, but focuses heavily on instruction following, rather than casual chat/roleplay. Huge thank you to the folks over at [a16z](https://a16z.com/) for sponsoring the costs associated with building models and associated tools! ### Prompt format The prompt format is llama-2 chat. ``` [INST] <<SYS>> You are a help, unbiased, uncensored assistant. <</SYS> {prompt} [/INST] ``` For multi-turn, the prompt format is as follows: ``` [INST] <<SYS>> You are a help, unbiased, uncensored assistant. <</SYS> {prompt 0} [/INST] {response 0} </s><s>[INST] {prompt 1} [/INST] {response 1} </s><s>...[INST] {prompt N} [/INST] ``` Here is a simple python script that can help generate the prompt: ```python def get_chat_prompt(message, chat_history, system_prompt): texts = [f'[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n'] do_strip = False for user_input, response in chat_history: user_input = user_input.strip() if do_strip else user_input do_strip = True texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ') message = message.strip() if do_strip else message texts.append(f'{message} [/INST] ') return ''.join(texts) def get_prompt(item): """ `item` is expected to be in chatml format, e.g. { "id": 1, "conversations": [ { "role": "system", "content": "You are a helpful, unbiased, uncensored assistant." }, { "role": "user", "content": "Hi!" }, { "role": "assistant", "content": "Hello, how are you today?" }, { "role": "user", "content": "Just peachy, and you?" } ] } """ system_prompt = share_gpt_item['conversations'][0]['value'] offset = 1 if share_gpt_item['conversations'][0]['role'] != 'system': system_prompt = 'You are an unbiased, uncensored, helpful assistant.' offset = 0 chat_history = [ (share_gpt_item['conversations'][idx]['content'], share_gpt_item['conversations'][idx + 1]['content']) for idx in range(offset, len(item['conversations']) - 1, 2) ] message = item['conversations'][-1]['content'] return get_chat_prompt(message, chat_history, system_prompt) ``` ### Helpful usage tips #### MathJSON Ask mathematical questions, prefixed with "Create a MathJSON solution to the following:", and you should get output with a formula that can be executed by https://cortexjs.io/compute-engine/ I also made a (really quite hacky, incomplete, semi-functional) python version that works for most expressions, see [mathjson.py](https://github.com/jondurbin/airoboros/blob/mathjson/airoboros/mathjson.py) __Use a very low temperature!__ Example: ``` [INST] <<SYS>> You are an assistant with vast knowledge in all things math. <</SYS> Create a MathJSON solution to the following: Calculate the area of a circle with radius of 17.2456cm. Include your reasoning. [/INST] ``` Output: ``` The area (A) of a circle is given by the formula A = πr², where r is the radius of the circle. Since the radius is given in centimeters, we need to convert it to meters first. We know that 1 meter equals 100 centimeters. Therefore, we can represent the radius as: r = 17.2456 cm * 100/1. Once we have the radius in meters, we can calculate the area of the circle using the formula A = πr². Solution as MathJSON: <mathjson> [ "Multiply", "Pi", [ "Power", 17.2456, 2 ] ] </mathjson> ``` You can then validate the JSON between `<mathjson>` and `</mathjson>`, then pass the parsed JSON to compute-engine JS or the `evaluate` function in mathjson.py to calculate the response. #### Context obedient question answering By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations. The format for a closed-context prompt is as follows: ``` BEGININPUT BEGINCONTEXT [key0: value0] [key1: value1] ... other metdata ... ENDCONTEXT [insert your text blocks here] ENDINPUT [add as many other blocks, in the exact same format] BEGININSTRUCTION [insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.] ENDINSTRUCTION ``` It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up. The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not! I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it. - `BEGININPUT` - denotes a new input block - `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block - `ENDCONTEXT` - denotes the end of the metadata block for the current input - [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context. - `ENDINPUT` - denotes the end of the current input block - [repeat as many input blocks in this format as you want] - `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above. - [instruction(s)] - `ENDINSTRUCTION` - denotes the end of instruction set It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to. __Use a very low temperature!__ Here's a trivial, but important example to prove the point: ``` BEGININPUT BEGINCONTEXT date: 2021-01-01 url: https://web.site/123 ENDCONTEXT In a shocking turn of events, blueberries are now green, but will be sticking with the same name. ENDINPUT BEGININSTRUCTION What color are bluberries? Source? ENDINSTRUCTION ``` And the response: ``` Blueberries are now green. Source: date: 2021-01-01 url: https://web.site/123 ``` #### Summarization 500 samples have been included from [this dataset](https://huggingface.co/datasets/mattpscott/airoboros-summarization), using the same format as contextual question answering, for example: ``` BEGININPUT {text to summarize} ENDINPUT BEGININSTRUCTION Summarize the input in around 130 words. ENDINSTRUCTION ``` #### Getting longer responses You can use a few techniques to get longer responses. Detailed prompts, with explicit instruction for word count: ``` Please compose a narrative set in the heart of an ancient library, steeped in the scent of old parchment and ink. The protagonist should be a young scholar who is dedicated to studying the art of storytelling and its evolution throughout history. In her pursuit of knowledge, she stumbles upon a forgotten tome that seems to possess an unusual aura. This book has the ability to bring stories to life, literally manifesting characters and scenarios from within its pages into reality. The main character must navigate through various epochs of storytelling - from oral traditions of tribal societies, through medieval minstrels' tales, to modern-day digital narratives - as they come alive around her. Each era presents its unique challenges and lessons about the power and impact of stories on human civilization. One such character could be a sentient quill pen, who was once used by renowned authors of yesteryears and now holds their wisdom and experiences. It becomes her mentor, guiding her through this journey with witty remarks and insightful commentary. Ensure that your tale encapsulates the thrill of adventure, the beauty of learning, and the profound connection between humans and their stories. All characters involved should be non-human entities. Feel free to explore creative liberties but maintain the mentioned elements. Your response should be approximately 2300 words. ``` Or, a simpler example: ``` Please create a long, detailed story about a dragon in an old growth forest who, for some reason, begins speaking the words of the source code of linux. ``` There are a few examples of next chapter completion as well, e.g.: ``` Write the next chapter of a historical fiction novel set in Paris during the 20th century. Here's a summary of the previous chapter: In the vibrant city of Paris, amid the tumultuous changes of the 20th century, our protagonist Margot, an aspiring fashion designer, has just secured an apprenticeship at a prestigious couture house. She meets Lucien, a charming journalist who covers the fashion industry. Together they navigate the ever-changing world of fashion and society, uncovering secrets that reveal the intricate links between style, politics, and culture. As the chapter concludes, they decide to delve deeper into the hidden corners of the fashion world to unravel its mysteries. Requirements for the next chapter: 1. Character Development of Margot and Lucien: - Margot's Evolution: Unfold more about Margot's past, her dreams of revolutionizing fashion, and her struggle to establish herself in a male-dominated industry. Illustrate her growing expertise, innovative ideas, and increasing dependence on Lucien. - Lucien's Complexity: Introduce uncertainties surrounding Lucien's background and real motives. Increase suspense by suggesting undisclosed information he possesses, while also highlighting his wit and perceptiveness. 2. Exploration of Paris and the Couture House: - Paris: Elaborate their journey through the bustling streets of Paris, including encounters with iconic figures, social unrest, and relics from different eras of French history. - The Couture House: Expand on the grandeur of the couture house they work in, filled with artistic masterpieces, intense competition, and cryptic notes hinting at a scandalous past. 3. Emergence of the Subplot: The Lost Collection: - Discovery: Have Margot and Lucien stumble upon a secret vault containing a lost collection designed before World War II, raising new questions about the previous owner and the influence of war on fashion. - Revelation: Capture their shock as they realize the designs were plagiarized, the potential repercussions, and the opportunities it presents for Margot's career. - Twist: End with a twist that suggests there are other stolen collections across Paris, setting up their new mission. Your response should be approximately 650 words. ``` #### Coding You can ask for fairly complex coding instructions with multiple criteria, e.g.: ``` Create a python application with the following requirements: - Asyncio FastAPI webserver - ping endpoint that returns the current date in JSON format - file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate ``` Or inline criteria: ``` Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values. ``` You can also optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.: ``` Write a websocket application in node.js. PLAINFORMAT ``` #### Agent/function calling The dataset includes many examples of function/args generation based on input criteria. This is somewhat similar to the OpenAI function calling, but the output is either JSON or YAML. Example prompt: ``` As an AI assistant, please select the most suitable function and parameters from the list of available functions below, based on the user's input. Provide your response in JSON format. Input: I want to know how many times 'Python' is mentioned in my text file. Available functions: file_analytics: description: This tool performs various operations on a text file. params: action: The operation we want to perform on the data, such as "count_occurrences", "find_line", etc. filters: keyword: The word or phrase we want to search for. ``` Response: ```json { "function": "file_analytics", "params": { "action": "count_occurrences", "filters": { "keyword": "Python" } } } ``` #### Chain-of-thought You can ask for several possible responses to a given problem, with a ranking and final answer selection. Example prompt: ``` A group of people decide to play a round-robin tournament where every player plays every other player exactly once. If a total of 45 games were played, how many players participated in the tournament? Offer several potential answers, rank them according to mathematical logic, and pick the most feasible one. ``` Example response: ``` Solution 1: In a round-robin tournament, each player plays every other player exactly once. This means that the total number of games played is equal to the combination of all players taken two at a time (since a game involves two players). The formula for combinations is nC2 = n(n-1)/2, where n is the total number of players. We know that nC2 equals 45 (the total number of games), so we can set up the equation as follows: n(n-1)/2 = 45 Solving this equation gives us n=10. Final answer: There were 10 players in the tournament. Solution 2: Another way to approach this problem is by using the concept of triangular numbers. Triangular numbers are a sequence of numbers where each number can be represented as a triangle with dots. The nth triangular number is given by the sum of the first n natural numbers. If we consider each game as a dot and each player as a side of the triangle, then the total number of games (45) would correspond to the 9th triangular number because 1+2+3+4+5+6+7+8+9=45. However, since each side of the triangle represents a player, and there's one more player than sides in our model (because the last player has no one left to play against), we need to add one more to get the total number of players. Final answer: There were 10 players in the tournament. Solution 3: We could also use trial and error to solve this problem. Starting from a small number of players, we calculate how many games would be played in a round-robin tournament until we reach 45 games. For example, with 2 players, only 1 game is played; with 3 players, 3 games are played; with 4 players, 6 games are played, and so on. Continuing this process, we find that with 10 players, 45 games are played. Final answer: There were 10 players in the tournament. Ranking of solutions from best to worst: 1. Solution 1: This solution uses direct mathematical reasoning based on the properties of a round-robin tournament and solves an equation to find the answer. 2. Solution 2: This solution uses the concept of triangular numbers which requires additional steps and knowledge beyond the immediate problem context, but still arrives at the correct answer. 3. Solution 3: This solution relies on trial and error, which is less efficient and reliable than the other methods, but it eventually gets to the right answer. Best and final answer: There were 10 players in the tournament. ``` #### reWOO style execution planning The model now supports execution planning for complex instructions that would require making use of several tools. The output is just the plan, you must implement a mechanism to parse the output and actually call the functions! Example prompt: ``` Please construct a systematic plan to generate an optimal response to the user instruction, utilizing a set of provided tools. Each plan will correspond to an evidence value, which will be the output of one of the available functions given an input string that could be the user's question, one or more prior evidence values, or a combination of both. Here are the tools available to be called: DuckDuckGo[input]: Tool that enables the user to search for information using the DuckDuckGo search engine. This tool is helpful in locating a suitable list of sites that may contain the answer to the user's query. It does not directly answer the question but finds a list of sites that might have the answer. TextScraper[input]: Load one or more websites from the input string containing newline delimited links, where input is one or more links, and produces plain text output containing the content of the links. HyperlinkExtractor[input]: Extract links from plain text and produces a plain text, newline delimited response of links. KnowledgeModel[input]: Question answering language model, particularly useful in answering questions based on an input passage of text. The input must be a text question that references an :evidence[n]: variable, e.g. What color is the cat, given :evidence1:? The output should be in format: Plan: [first action to take based on input question] :evidence0: = [function to call with input parameter] Plan: [next action to take, based on result of :evidence0:] :evidence1: = [next function to call and input parameter, which may include reference to previous evidence, e.g. "Given context :evidence0"] ... Answer: [:evidence[n]: containing the final answer.] Question: Who won the Nobel Prize in Literature in 2020 and what is their most famous work? ``` Response: ``` Plan: Begin by conducting a web search to find out who won the Nobel Prize in Literature in 2020. :evidence0: = DuckDuckGo[Nobel Prize in Literature 2020 winner] Plan: Utilize the KnowledgeModel to interpret the search results and find the name of the laureate. :evidence1: = KnowledgeModel[Who won the Nobel Prize in Literature in 2020, given :evidence0:?] Plan: Conduct another web search to find the most famous work of the identified laureate. :evidence2: = DuckDuckGo[Most famous work of :evidence1:] Plan: Extract the relevant links from the DuckDuckGo search results for a more focused search. :evidence3: = HyperlinkExtractor[:evidence2:] Plan: Use the TextScraper tool to extract information from the relevant links. :evidence4: = TextScraper[:evidence3:] Plan: Finally, utilize the KnowledgeModel to identify and summarize the most famous work of the laureate from the extracted information. :evidence5: = KnowledgeModel[What is the most famous work of :evidence1:, given :evidence4:?] Answer: :evidence5: ``` For this to be useful, you'd have to parse the output plan text, and implement/call each of the functions. This is just pseudo-code, completely untested off the top of my head, and obviously would requiring full implementation + hardening: ```python import re import requests def inject_context(input_text, context): for ref in set(re.findall(r"(:evidence[0-9]+:)", input_text, re.I)): input_text = input_text.replace(ref, context.get(ref, "")) return input_text def duckduckgo(input_text, context): search_string = inject_context(input_text, context) ... search via duck duck go using search_string ... return text content def link_extractor(input_text, context): input_text = inject_context(input_text, context) return "\n".join(list(set(re.findall(r"(https?://[^\s]+?\.?)", input_text, re.I)))) def scrape(input_text, context): input_text = inject_context(input_text, context) text = [] for link in input_text.splitlines(): text.append(requests.get(link).text) return "\n".join(text) def infer(input_text, context) prompt = inject_context(input_text, *context) ... call model with prompt, return output def parse_plan(plan): method_map = { "DuckDuckGo": duckduckgo, "HyperlinkExtractor": link_extractor, "KnowledgeModel": infer, "TextScraper": scrape, } context = {} for line in plan.strip().splitlines(): if line.startswith("Plan:"): print(line) continue parts = re.match("^(:evidence[0-9]+:)\s=\s([^\[]+])(\[.\])\s$", line, re.I) if not parts: if line.startswith("Answer: "): return context.get(line.split(" ")[-1].strip(), "Answer couldn't be generated...") raise RuntimeError("bad format: " + line) context[parts.group(1)] = method_map[parts.group(2)](parts.group(3), *context) ``` ### Contribute If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data, take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details. To help me with the OpenAI/compute costs: - https://bmc.link/jondurbin - ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11 - BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf ### Licence and usage restrictions The airoboros 3.0 models are built on top of multiple base models, each with their own license/restrictions. The models with `-l2` in the name have a custom Meta license: - See the [meta-license/LICENSE.txt](meta-license/LICENSE.txt) file attached for the original license provided by Meta. - See also [meta-license/USE_POLICY.md](meta-license/USE_POLICY.md) and [meta-license/Responsible-Use-Guide.pdf](meta-license/Responsible-Use-Guide.pdf), also provided by Meta. The models with `-m-` are mistral-7b (apache 2.0) The model with `-3b` uses Stability AI, which as a `cc-by-sa-4.0` license. The fine-tuning data was mostly generated by OpenAI API calls to gpt-4, via [airoboros](https://github.com/jondurbin/airoboros) The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that __competes__ with OpenAI - what does compete* actually mean here? - these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place - if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works - the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place - other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2 I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly. Your best bet is probably to avoid using this commercially due to the OpenAI API usage. Either way, by using this model, you agree to completely indemnify me.
Arpeet/mistral-7B-instruct-v0.2-awq	Arpeet	2024-05-30T00:43:05Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "conversational", "arxiv:2310.06825", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:quantized:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "4-bit", "awq", "region:us" ]	text-generation	2024-05-30T00:40:47Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 inference: false license: apache-2.0 model_creator: Mistral AI_ model_name: Mistral 7B Instruct v0.2 model_type: mistral pipeline_tag: text-generation prompt_template: '<s>[INST] {prompt} [/INST] ' quantized_by: TheBloke tags: - finetuned --- <!-- markdownlint-disable MD041 --> <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-start;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p> </div> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p> </div> </div> <div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Mistral 7B Instruct v0.2 - AWQ - Model creator: [Mistral AI_](https://huggingface.co/mistralai) - Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- description start --> ## Description This repo contains AWQ model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/). ### About AWQ AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings. AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead. It is supported by: - [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ - [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code <!-- description end --> <!-- repositories-available start --> ## Repositories available * [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ) * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF) * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) <!-- repositories-available end --> <!-- prompt-template start --> ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` <!-- prompt-template end --> <!-- README_AWQ.md-provided-files start --> ## Provided files, and AWQ parameters I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered. Models are released as sharded safetensors files. \| Branch \| Bits \| GS \| AWQ Dataset \| Seq Len \| Size \| \| ------ \| ---- \| -- \| ----------- \| ------- \| ---- \| \| [main](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ/tree/main) \| 4 \| 128 \| [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) \| 4096 \| 4.15 GB <!-- README_AWQ.md-provided-files end --> <!-- README_AWQ.md-text-generation-webui start --> ## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui) Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui). It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install. 1. Click the Model tab. 2. Under Download custom model or LoRA, enter `TheBloke/Mistral-7B-Instruct-v0.2-AWQ`. 3. Click Download. 4. The model will start downloading. Once it's finished it will say "Done". 5. In the top left, click the refresh icon next to Model. 6. In the Model dropdown, choose the model you just downloaded: `Mistral-7B-Instruct-v0.2-AWQ` 7. Select Loader: AutoAWQ. 8. Click Load, and the model will load and is now ready for use. 9. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right. 10. Once you're ready, click the Text Generation tab and enter a prompt to get started! <!-- README_AWQ.md-text-generation-webui end --> <!-- README_AWQ.md-use-from-vllm start --> ## Multi-user inference server: vLLM Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/). - Please ensure you are using vLLM version 0.2 or later. - When using vLLM as a server, pass the `--quantization awq` parameter. For example: ```shell python3 -m vllm.entrypoints.api_server --model TheBloke/Mistral-7B-Instruct-v0.2-AWQ --quantization awq --dtype auto ``` - When using vLLM from Python code, again set `quantization=awq`. For example: ```python from vllm import LLM, SamplingParams prompts = [ "Tell me about AI", "Write a story about llamas", "What is 291 - 150?", "How much wood would a woodchuck chuck if a woodchuck could chuck wood?", ] prompt_template=f'''<s>[INST] {prompt} [/INST] ''' prompts = [prompt_template.format(prompt=prompt) for prompt in prompts] sampling_params = SamplingParams(temperature=0.8, top_p=0.95) llm = LLM(model="TheBloke/Mistral-7B-Instruct-v0.2-AWQ", quantization="awq", dtype="auto") outputs = llm.generate(prompts, sampling_params) # Print the outputs. for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}") ``` <!-- README_AWQ.md-use-from-vllm start --> <!-- README_AWQ.md-use-from-tgi start --> ## Multi-user inference server: Hugging Face Text Generation Inference (TGI) Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0` Example Docker parameters: ```shell --model-id TheBloke/Mistral-7B-Instruct-v0.2-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 ``` Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later): ```shell pip3 install huggingface-hub ``` ```python from huggingface_hub import InferenceClient endpoint_url = "https://your-endpoint-url-here" prompt = "Tell me about AI" prompt_template=f'''<s>[INST] {prompt} [/INST] ''' client = InferenceClient(endpoint_url) response = client.text_generation(prompt, max_new_tokens=128, do_sample=True, temperature=0.7, top_p=0.95, top_k=40, repetition_penalty=1.1) print(f"Model output: ", response) ``` <!-- README_AWQ.md-use-from-tgi end --> <!-- README_AWQ.md-use-from-python start --> ## Inference from Python code using Transformers ### Install the necessary packages - Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later. - Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later. ```shell pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0" ``` Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0. If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command: ```shell pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl ``` If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead: ```shell pip3 uninstall -y autoawq git clone https://github.com/casper-hansen/AutoAWQ cd AutoAWQ pip3 install . ``` ### Transformers example code (requires Transformers 4.35.0 and later) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_name_or_path = "TheBloke/Mistral-7B-Instruct-v0.2-AWQ" tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) model = AutoModelForCausalLM.from_pretrained( model_name_or_path, low_cpu_mem_usage=True, device_map="cuda:0" ) # Using the text streamer to stream output one token at a time streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "Tell me about AI" prompt_template=f'''<s>[INST] {prompt} [/INST] ''' # Convert prompt to tokens tokens = tokenizer( prompt_template, return_tensors='pt' ).input_ids.cuda() generation_params = { "do_sample": True, "temperature": 0.7, "top_p": 0.95, "top_k": 40, "max_new_tokens": 512, "repetition_penalty": 1.1 } # Generate streamed output, visible one token at a time generation_output = model.generate( tokens, streamer=streamer, generation_params ) # Generation without a streamer, which will include the prompt in the output generation_output = model.generate( tokens, generation_params ) # Get the tokens from the output, decode them, print them token_output = generation_output[0] text_output = tokenizer.decode(token_output) print("model.generate output: ", text_output) # Inference is also possible via Transformers' pipeline from transformers import pipeline pipe = pipeline( "text-generation", model=model, tokenizer=tokenizer, *generation_params ) pipe_output = pipe(prompt_template)[0]['generated_text'] print("pipeline output: ", pipe_output) ``` <!-- README_AWQ.md-use-from-python end --> <!-- README_AWQ.md-compatibility start --> ## Compatibility The files provided are tested to work with: - [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`. - [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later. - [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later. - [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later. - [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later. <!-- README_AWQ.md-compatibility end --> <!-- footer start --> <!-- 200823 --> ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute Thanks to the [chirper.ai](https://chirper.ai) team! Thanks to Clay from [gpus.llm-utils.org](llm-utils)! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI Special thanks to: Aemon Algiz. Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. <!-- footer end --> # Original model card: Mistral AI_'s Mistral 7B Instruct v0.2 # Model Card for Mistral-7B-Instruct-v0.2 The Mistral-7B-Instruct-v0.2 Large Language Model (LLM) is an improved instruct fine-tuned version of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/la-plateforme/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Louis Ternon, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.
Elhassnaoui-2001/plodot-model	Elhassnaoui-2001	2024-05-30T00:42:55Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-30T00:42:55Z	--- license: apache-2.0 ---
hdve/Qwen-Qwen1.5-1.8B-1717029504	hdve	2024-05-30T00:40:21Z	146	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:38:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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haes95/POLAR-7B-HES-SFT-v0.1	haes95	2024-05-30T00:38:21Z	79	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-29T23:57:06Z	--- library_name: transformers license: apache-2.0 pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f3ee48b1a907c6aa6d8f06/nGbRfMQEfAW_aDwisKn9T.png) ## Model Description <!-- Provide a longer summary of what this model is/does. --> POLAR is a Korean LLM developed by Plateer's AI-lab. It was inspired by Upstage's SOLAR. We will continue to evolve this model and hope to contribute to the Korean LLM ecosystem. - Developed by: AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son) - Model type: Language model - Language(s) (NLP): ko - License: apache-2.0 - Parent Model: x2bee/POLAR-14B-v0.2 ## Direct Use ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("haes95/POLAR-7B-HES-SFT-v0.1") model = AutoModelForCausalLM.from_pretrained("haes95/POLAR-7B-HES-SFT-v0.1") ``` ## Downstream Use [Optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). 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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 <!-- 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] If you would like more information about our company, please visit the link below. [tech.x2bee.com](https://tech.x2bee.com/) # Model Card Authors [optional] <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> More information needed </details>
ArashAhmadian/rloo_tldr_6.9b_ds2	ArashAhmadian	2024-05-30T00:29:08Z	6	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "generated_from_trainer", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T00:20:32Z	--- tags: - generated_from_trainer model-index: - name: rloo_tldr_6.9b_ds2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # rloo_tldr_6.9b_ds2 This model was trained from scratch 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: 1e-06 - 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: constant_with_warmup - lr_scheduler_warmup_steps: 50 - num_epochs: 3.0 ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Johnyquest7/Meta-Llama-3-8B-Q4_K_M-GGUF	Johnyquest7	2024-05-30T00:28:40Z	4	0	null	[ "gguf", "facebook", "meta", "pytorch", "llama", "llama-3", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:28:24Z	--- language: - en license: llama3 tags: - facebook - meta - pytorch - llama - llama-3 - llama-cpp - gguf-my-repo pipeline_tag: text-generation extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\ \ Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for\ \ use, reproduction, distribution and modification of the Llama Materials set forth\ \ herein.\n\"Documentation\" means the specifications, manuals and documentation\ \ accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\ \"Licensee\" or \"you\" means you, or your employer or any other person or entity\ \ (if you are entering into this Agreement on such person or entity’s behalf), of\ \ the age required under applicable laws, rules or regulations to provide legal\ \ consent and that has legal authority to bind your employer or such other person\ \ or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama\ \ 3\" means the foundational large language models and software and algorithms,\ \ including machine-learning model code, trained model weights, inference-enabling\ \ code, training-enabling code, fine-tuning enabling code and other elements of\ \ the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\ \"Llama Materials\" means, collectively, Meta’s proprietary Meta Llama 3 and Documentation\ \ (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"\ we\" means Meta Platforms Ireland Limited (if you are located in or, if you are\ \ an entity, your principal place of business is in the EEA or Switzerland) and\ \ Meta Platforms, Inc. 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By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy : checkbox extra_gated_description: The information you provide will be collected, stored, processed and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/). extra_gated_button_content: Submit --- # Johnyquest7/Meta-Llama-3-8B-Q4_K_M-GGUF This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo Johnyquest7/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo Johnyquest7/Meta-Llama-3-8B-Q4_K_M-GGUF --model meta-llama-3-8b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m meta-llama-3-8b-q4_k_m.gguf -n 128 ```
OwOpeepeepoopoo/Wiredn2	OwOpeepeepoopoo	2024-05-30T00:27:53Z	132	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "mergekit", "merge", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:26:39Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # output_diff1_4 This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T * /notebooks/dippy-bittensor-subnet/output_diff_try1 ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T layer_range: [0, 24] - model: /notebooks/dippy-bittensor-subnet/output_diff_try1 layer_range: [0, 24] merge_method: slerp base_model: /notebooks/dippy-bittensor-subnet/clone_tistak_F4Pz0cGuDztfU49T parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
magnifi/phi-3-mini-4k-instruct-attribute-output-4-0529-epoch20	magnifi	2024-05-30T00:14:20Z	77	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:magnifi/phi-3-mini-4k-instruct-attribute-output-4-0524-epoch20", "base_model:finetune:magnifi/phi-3-mini-4k-instruct-attribute-output-4-0524-epoch20", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:11:00Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: magnifi/phi-3-mini-4k-instruct-attribute-output-4-0524-epoch20 --- # Uploaded model - Developed by: magnifi - License: apache-2.0 - Finetuned from model : magnifi/phi-3-mini-4k-instruct-attribute-output-4-0524-epoch20 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)
AlpacaAAR/llama-3-epfl-tutor-dpo	AlpacaAAR	2024-05-30T00:12:12Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:AlpacaAAR/llama-3-epfl-tutor", "base_model:finetune:AlpacaAAR/llama-3-epfl-tutor", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-30T00:11:48Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: AlpacaAAR/llama-3-epfl-tutor --- # Uploaded model - Developed by: AlpacaAAR - License: apache-2.0 - Finetuned from model : AlpacaAAR/llama-3-epfl-tutor This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
datek/Qwen-Qwen1.5-0.5B-1717027814	datek	2024-05-30T00:10:44Z	147	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-30T00:10:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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NovNovikov/aya-23-8B-Q6_K-GGUF	NovNovikov	2024-05-30T00:06:43Z	5	1	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "license:cc-by-nc-4.0", "region:us", "conversational" ]	null	2024-05-30T00:06:24Z	--- language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi license: cc-by-nc-4.0 library_name: transformers tags: - llama-cpp - gguf-my-repo inference: false --- # NovNovikov/aya-23-8B-Q6_K-GGUF This model was converted to GGUF format from [`CohereForAI/aya-23-8B`](https://huggingface.co/CohereForAI/aya-23-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/CohereForAI/aya-23-8B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NovNovikov/aya-23-8B-Q6_K-GGUF --model aya-23-8b-q6_k.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NovNovikov/aya-23-8B-Q6_K-GGUF --model aya-23-8b-q6_k.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m aya-23-8b-q6_k.gguf -n 128 ```
JD45/ppo-LunarLander-v2.1	JD45	2024-05-30T00:05:45Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-30T00:05:19Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 266.07 +/- 14.54 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
AnsonLau/llama3-8b-fine-tuned-humor-classification-q4_k_m	AnsonLau	2024-05-30T00:05:30Z	14	0	transformers	[ "transformers", "gguf", "llama", "endpoints_compatible", "region:us" ]	null	2024-05-29T11:24:03Z	This is the Fine-Tuned Llama 3-8B model in paper: "Humour classification by Fine-tuning LLMs: CYUT at CLEF 2024 JOKER Lab Task 2 Humour classification according to genre and technique." --- license: mit ---
medtalkai/wav2vec2-xls-r-1b-medical-domain	medtalkai	2024-05-29T23:57:00Z	14	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "base_model:jonatasgrosman/wav2vec2-xls-r-1b-portuguese", "base_model:finetune:jonatasgrosman/wav2vec2-xls-r-1b-portuguese", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-24T22:51:04Z	--- license: apache-2.0 base_model: jonatasgrosman/wav2vec2-xls-r-1b-portuguese tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xls-r-1b-medical-domain results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-xls-r-1b-medical-domain This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5363 - Wer: 0.7022 ## 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: 0.0003 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| No log \| 9.9379 \| 200 \| 0.2631 \| 0.3790 \| \| 1.2151 \| 19.8758 \| 400 \| 0.5363 \| 0.7022 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.1.2+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
x2bee/POLAR-14B-HES-DPO-v1.5	x2bee	2024-05-29T23:52:31Z	789	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "ko", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-28T01:35:43Z	--- library_name: transformers license: apache-2.0 language: - ko pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ![image/png](https://cdn-uploads.huggingface.co/production/uploads/65f3ee48b1a907c6aa6d8f06/nGbRfMQEfAW_aDwisKn9T.png) ## Model Description <!-- Provide a longer summary of what this model is/does. --> POLAR is a Korean LLM developed by Plateer's AI-lab. It was inspired by Upstage's SOLAR. We will continue to evolve this model and hope to contribute to the Korean LLM ecosystem. - Developed by: AI-Lab of Plateer(Woomun Jung, Eunsoo Ha, MinYoung Joo, Seongjun Son) - Model type: Language model - Language(s) (NLP): ko - License: apache-2.0 - Parent Model: x2bee/POLAR-14B-SON-SFT-v0.1 ## Direct Use ``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("x2bee/POLAR-14B-HES-DPO-v1.5") model = AutoModelForCausalLM.from_pretrained("x2bee/POLAR-14B-HES-DPO-v1.5") ``` ## Downstream Use [Optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> ## Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> <!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." --> # Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups. ## Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> # Training Details ## Training Data <!-- This should link to a Data 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 on training data 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 More information needed ### Speeds, Sizes, Times <!-- 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 Data 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 # Model Examination 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 <!-- 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] If you would like more information about our company, please visit the link below. [tech.x2bee.com](https://tech.x2bee.com/) # Model Card Authors [optional] <!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. --> Woomun Jung, MinYoung Joo, Eunsu Ha, Seungjun Son # Model Card Contact More information needed # How to Get Started with the Model Use the code below to get started with the model. <details> <summary> Click to expand </summary> More information needed </details>
ebowwa/human-biases-people	ebowwa	2024-05-29T23:50:21Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "dataset:ebowwa/merged-human-biases-dataset-beta", "base_model:unsloth/mistral-7b-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-v0.3-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-29T22:56:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/mistral-7b-v0.3-bnb-4bit datasets: - ebowwa/merged-human-biases-dataset-beta --- # Uploaded model - Developed by: ebowwa - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-v0.3-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)
stifi/outputs	stifi	2024-05-29T23:48:53Z	2	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-2b-it", "base_model:adapter:google/gemma-2b-it", "license:gemma", "region:us" ]	null	2024-05-29T23:48:41Z	--- license: gemma library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-2b-it model-index: - name: outputs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) 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: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 0.03 - training_steps: 200 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0 - Pytorch 2.3.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2
VitOliv/keras-dummy-sequential-demo	VitOliv	2024-05-29T23:48:32Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2024-05-29T23:48:31Z	--- library_name: keras --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| False \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|
lqzpxlz/our-model	lqzpxlz	2024-05-29T23:43:29Z	195	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-05-29T22:57:59Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: our-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # our-model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9352 - Accuracy: 1.0 ## 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: 16 - eval_batch_size: 16 - 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_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 0.96 \| 6 \| 3.9220 \| 0.98 \| \| 4.1105 \| 1.92 \| 12 \| 3.2139 \| 1.0 \| \| 4.1105 \| 2.88 \| 18 \| 2.9352 \| 1.0 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
hfht/tumblr-humor-mistral-7b-instruct-v0.3	hfht	2024-05-29T23:37:27Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.3", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3", "region:us" ]	null	2024-05-29T23:22:39Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.3 --- # 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. 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haes95/POLAR-14B-HES-DPO-adapter-v1.5	haes95	2024-05-29T23:34:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T23:31:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf	RichardErkhov	2024-05-29T23:29:07Z	3	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-29T20:29:03Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7b-platypus-fp16 - GGUF - Model creator: https://huggingface.co/bhenrym14/ - Original model: https://huggingface.co/bhenrym14/mistral-7b-platypus-fp16/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral-7b-platypus-fp16.Q2_K.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [mistral-7b-platypus-fp16.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [mistral-7b-platypus-fp16.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [mistral-7b-platypus-fp16.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral-7b-platypus-fp16.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral-7b-platypus-fp16.Q3_K.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral-7b-platypus-fp16.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral-7b-platypus-fp16.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral-7b-platypus-fp16.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [mistral-7b-platypus-fp16.Q4_0.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral-7b-platypus-fp16.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral-7b-platypus-fp16.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral-7b-platypus-fp16.Q4_K.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral-7b-platypus-fp16.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral-7b-platypus-fp16.Q4_1.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral-7b-platypus-fp16.Q5_0.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [mistral-7b-platypus-fp16.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [mistral-7b-platypus-fp16.Q5_K.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral-7b-platypus-fp16.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral-7b-platypus-fp16.Q5_1.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral-7b-platypus-fp16.Q6_K.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [mistral-7b-platypus-fp16.Q8_0.gguf](https://huggingface.co/RichardErkhov/bhenrym14_-_mistral-7b-platypus-fp16-gguf/blob/main/mistral-7b-platypus-fp16.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: --- datasets: - garage-bAInd/Open-Platypus --- # Instruction tune of Mistral-7B-v0.1 with Open-Platypus (fp16) ## Overview This is [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), with instruction tuning performed with the [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) dataset. This is a (merged) QLoRA fine-tune (rank 64). The finetune was performed with 1x RTX 6000 Ada (~9 hours). ## How to Use As of writing, the `Mistral` architecture requires installation of `transformers` from source. With this done, load like any other model. ### Benchmarks ARC (25 shot): 62.80 Hellaswag (10 shot): 84.12 MMLU (5 shot): 64.20 ## Context Length - Relative Performance (wikitext perplexity) \| Context (tokens) \| <ins>bhenrym14/mistral-7b-platypus-fp16</ins> \| bhenrym14/airoboros-l2-13b-2.1-YaRN-64k \| bhenrym14/airophin-13b-pntk-16k-fp16 \| bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16 \| jondurbin/airoboros-l2-13b-gpt4-1.4.1 \| \| --- \| --- \|--- \| ---\| ----- \| -----\| \| 512 \| 7.22 \| 7.64 \| 7.62 \| 7.90 \| 7.23 \| \| 1024 \| 6.04 \| 6.15 \| 6.20 \| 6.17 \| 5.85 \| \| 2048 \| 5.50 \| 5.29 \| 5.38 \| 5.23 \| 5.07 \| \| 4096 \| 5.05 \|4.93 \| 5.08 \| 4.91 \| 4.77 \| \| 8192 \| 4.96 \|4.69 \| 4.90 \| Not Tested \| 57.1 \| \| 12000 \| Not Tested \| 4.53 \| 4.82 \| Not Tested \| Not Tested \| - While the mistral model is very impressive for its size, particularly on benchmarks, the sliding window attention and/or model size impacts its competitiveness with other context extension techniques applied to larger llama2 and llama variants. Is this is more to do with sliding window attention or model size? ## Prompting: Model was trained with legacy airoboros <2.0 system prompt. See [bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-lxctx-PI-16384-fp16) model card for details.
Sayan01/CKA-T5-CoT-m-T1	Sayan01	2024-05-29T23:25:43Z	20	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-04-23T01:40:15Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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himanshubeniwal/mt5-base-finetuned-kk-to-en-filthy-PrimeMinister	himanshubeniwal	2024-05-29T23:24:26Z	107	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-29T23:19:29Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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AleRothermel/my-sentiments_es-model	AleRothermel	2024-05-29T23:23:49Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-23T20:10:02Z	--- tags: - generated_from_trainer base_model: dccuchile/bert-base-spanish-wwm-cased metrics: - accuracy model-index: - name: my-sentiments_es-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my-sentiments_es-model This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2368 - Accuracy: 0.9455 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.3239 \| 1.0 \| 619 \| 0.3335 \| 0.8933 \| \| 0.2113 \| 2.0 \| 1238 \| 0.2651 \| 0.9406 \| \| 0.1809 \| 3.0 \| 1857 \| 0.2368 \| 0.9455 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
JD45/ppo-LunarLander-v2	JD45	2024-05-29T23:22:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-29T23:22:09Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: -560.60 +/- 312.71 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jeeyoung/dpo974410th_trial_10000_data	jeeyoung	2024-05-29T23:22:07Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T23:20:59Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
kurros/stablelm-2-zephyr-1_6b-Q3_K_M-GGUF	kurros	2024-05-29T23:19:34Z	3	0	null	[ "gguf", "causal-lm", "llama-cpp", "gguf-my-repo", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:meta-math/MetaMathQA", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:LDJnr/Capybara", "dataset:Intel/orca_dpo_pairs", "dataset:hkust-nlp/deita-10k-v0", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T23:19:29Z	--- language: - en license: other tags: - causal-lm - llama-cpp - gguf-my-repo datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox --- # kurros/stablelm-2-zephyr-1_6b-Q3_K_M-GGUF This model was converted to GGUF format from [`stabilityai/stablelm-2-zephyr-1_6b`](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo kurros/stablelm-2-zephyr-1_6b-Q3_K_M-GGUF --model stablelm-2-zephyr-1_6b-q3_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo kurros/stablelm-2-zephyr-1_6b-Q3_K_M-GGUF --model stablelm-2-zephyr-1_6b-q3_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m stablelm-2-zephyr-1_6b-q3_k_m.gguf -n 128 ```
Andresckamilo/unsloth-second	Andresckamilo	2024-05-29T23:16:55Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-29T23:16:22Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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jeeyoung/dpo904810th_trial_10000_data	jeeyoung	2024-05-29T23:15:31Z	3	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T23:14:18Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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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. 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jeeyoung/dpo870010th_trial_10000_data	jeeyoung	2024-05-29T23:12:13Z	4	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T23:10:56Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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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] ### Framework versions - PEFT 0.11.0
kurros/stablelm-2-zephyr-1_6b-Q3_K_S-GGUF	kurros	2024-05-29T23:12:11Z	0	0	null	[ "gguf", "causal-lm", "llama-cpp", "gguf-my-repo", "en", "dataset:HuggingFaceH4/ultrachat_200k", "dataset:allenai/ultrafeedback_binarized_cleaned", "dataset:meta-math/MetaMathQA", "dataset:WizardLM/WizardLM_evol_instruct_V2_196k", "dataset:openchat/openchat_sharegpt4_dataset", "dataset:LDJnr/Capybara", "dataset:Intel/orca_dpo_pairs", "dataset:hkust-nlp/deita-10k-v0", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-29T23:12:06Z	--- language: - en license: other tags: - causal-lm - llama-cpp - gguf-my-repo datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox --- # kurros/stablelm-2-zephyr-1_6b-Q3_K_S-GGUF This model was converted to GGUF format from [`stabilityai/stablelm-2-zephyr-1_6b`](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo kurros/stablelm-2-zephyr-1_6b-Q3_K_S-GGUF --model stablelm-2-zephyr-1_6b-q3_k_s.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo kurros/stablelm-2-zephyr-1_6b-Q3_K_S-GGUF --model stablelm-2-zephyr-1_6b-q3_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m stablelm-2-zephyr-1_6b-q3_k_s.gguf -n 128 ```
raxtemur/trocr-base-ru	raxtemur	2024-05-29T23:05:29Z	837	21	transformers	[ "transformers", "pytorch", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "ocr", "image-to-text", "ru", "en", "dataset:nastyboget/stackmix_hkr_large", "dataset:nastyboget/stackmix_cyrillic_large", "dataset:nastyboget/synthetic_cyrillic_large", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-to-text	2024-02-16T15:01:16Z	--- license: apache-2.0 datasets: - nastyboget/stackmix_hkr_large - nastyboget/stackmix_cyrillic_large - nastyboget/synthetic_cyrillic_large language: - ru - en pipeline_tag: image-to-text widget: - src: examples/1.png - src: examples/2.png - src: examples/3.png - src: examples/4.png tags: - ocr --- # Model Card for TrOCR-Ru <!-- Provide a quick summary of what the model is/does. --> Finetuned model [microsoft/trocr-base-handwritten](https://huggingface.co/microsoft/trocr-base-handwritten) on large synth datasets from [nastyboget](https://huggingface.co/nastyboget). ## Metrics on HKR/Cyrillic datasets \| Metric \| HKR_val \| HKR_test1 \| HKR_test2 \| CYR_val \| CYR_test \| \|:--------:\|:---------:\|:---------:\|:---------:\|:---------:\|:---------:\| \| Accuracy \| 69.9947 \| 67.4184 \| 69.9187 \| 72.3613 \| 63.9249 \| \| CER \| 6.7964 \| 8.9113 \| 6.7278 \| 6.6403 \| 9.2576 \| \| WER \| 21.6688 \| 27.3849 \| 21.6200 \| 27.6715 \| 33.2406 \| Last update form 29/02/2024
Andresckamilo/Unsloath-first-training	Andresckamilo	2024-05-29T23:02:48Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-29T23:02:48Z	--- license: apache-2.0 ---
carolcarneiro/keras-dummy-functional-demo	carolcarneiro	2024-05-29T22:56:41Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2024-05-29T22:56:39Z	--- library_name: keras --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \| ## Model Plot <details> <summary>View Model Plot</summary> ![Model Image](./model.png) </details>
jeeyoung/dpo696010th_trial_10000_data	jeeyoung	2024-05-29T22:55:45Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:54:33Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
pdibez/lilo-01	pdibez	2024-05-29T22:52:48Z	29	1	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-04-15T01:06:25Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### Lilo is a Dreambooth model trained by Pablo Dibez for thesis "Personalización de un modelo text-to-image para la generación de imágenes con Inteligencia Artificial" from Universidad Nacional de la Patagonia San Juan Bosco (2024) for the career Licenciatura en Informática Sample pictures of this concept and yours prompts: ![Image generated and your prompts](test-model-lilo.jpg)
Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2	Zoyd	2024-05-29T22:49:10Z	7	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "5-bit", "exl2", "region:us" ]	text-generation	2024-05-29T21:20:17Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 5.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
jeeyoung/dpo626410th_trial_10000_data	jeeyoung	2024-05-29T22:49:03Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:47:51Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2	Zoyd	2024-05-29T22:48:34Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]	text-generation	2024-05-29T20:11:58Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 3.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2	Zoyd	2024-05-29T22:48:00Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]	text-generation	2024-05-29T22:03:50Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 6.5 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
arzans9/finetuning_summarization	arzans9	2024-05-29T22:47:50Z	104	0	transformers	[ "transformers", "tensorboard", "safetensors", "encoder-decoder", "text2text-generation", "generated_from_trainer", "base_model:cahya/bert2bert-indonesian-summarization", "base_model:finetune:cahya/bert2bert-indonesian-summarization", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-29T13:52:44Z	--- license: apache-2.0 base_model: cahya/bert2bert-indonesian-summarization tags: - generated_from_trainer metrics: - rouge model-index: - name: finetuning_summarization results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning_summarization This model is a fine-tuned version of [cahya/bert2bert-indonesian-summarization](https://huggingface.co/cahya/bert2bert-indonesian-summarization) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6759 - Rouge1: 0.8455 - Rouge2: 0.742 - Rougel: 0.8486 - Rougelsum: 0.8475 - Gen Len: 23.7368 ## 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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 5 \| 1.3699 \| 0.8443 \| 0.7258 \| 0.8426 \| 0.8435 \| 25.8421 \| \| No log \| 2.0 \| 10 \| 1.0257 \| 0.8282 \| 0.7115 \| 0.8293 \| 0.8275 \| 25.0 \| \| No log \| 3.0 \| 15 \| 0.7871 \| 0.8384 \| 0.7277 \| 0.8397 \| 0.8396 \| 24.3158 \| \| No log \| 4.0 \| 20 \| 0.7078 \| 0.8339 \| 0.7318 \| 0.8358 \| 0.8348 \| 23.4211 \| \| No log \| 5.0 \| 25 \| 0.6994 \| 0.843 \| 0.7396 \| 0.8451 \| 0.845 \| 24.0 \| \| No log \| 6.0 \| 30 \| 0.6832 \| 0.8445 \| 0.7413 \| 0.8419 \| 0.842 \| 23.4737 \| \| No log \| 7.0 \| 35 \| 0.6768 \| 0.8429 \| 0.742 \| 0.8451 \| 0.8448 \| 23.6842 \| \| No log \| 8.0 \| 40 \| 0.6736 \| 0.843 \| 0.7396 \| 0.8451 \| 0.845 \| 23.6842 \| \| No log \| 9.0 \| 45 \| 0.6750 \| 0.843 \| 0.7396 \| 0.8451 \| 0.845 \| 23.6842 \| \| No log \| 10.0 \| 50 \| 0.6759 \| 0.8455 \| 0.742 \| 0.8486 \| 0.8475 \| 23.7368 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2	Zoyd	2024-05-29T22:41:13Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "4-bit", "exl2", "region:us" ]	text-generation	2024-05-29T20:45:31Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 4.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
jeeyoung/dpo487210th_trial_10000_data	jeeyoung	2024-05-29T22:35:36Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:34:25Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2	Zoyd	2024-05-29T22:33:36Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "6-bit", "exl2", "region:us" ]	text-generation	2024-05-29T21:48:44Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 6.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2	Zoyd	2024-05-29T22:33:13Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]	text-generation	2024-05-29T21:10:38Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2	Zoyd	2024-05-29T22:32:45Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-29T20:02:39Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 3.0 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2	Zoyd	2024-05-29T22:32:33Z	11	1	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]	text-generation	2024-05-29T19:28:52Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Exllamav2 quant (exl2 / 2.2 bpw) made with ExLlamaV2 v0.1.1 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)</center> \| <center>6296 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)</center> \| <center>7045 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)</center> \| <center>8347 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)</center> \| <center>9652 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)</center> \| <center>10297 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)</center> \| <center>10953 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)</center> \| <center>11603 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)</center> \| <center>13553 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)</center> \| <center>16185 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)</center> \| <center>17484 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)</center> \| <center>19350 MB</center> \| <center>8</center> \| Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
jeeyoung/dpo452410th_trial_10000_data	jeeyoung	2024-05-29T22:32:21Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:31:06Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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(2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
bartowski/Codestral-22B-v0.1-exl2	bartowski	2024-05-29T22:31:00Z	10	9	null	[ "code", "text-generation", "license:other", "region:us" ]	text-generation	2024-05-29T22:30:59Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code quantized_by: bartowski pipeline_tag: text-generation --- ## Exllama v2 Quantizations of Codestral-22B-v0.1 Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.1">turboderp's ExLlamaV2 v0.1.1</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/mistral-community/Codestral-22B-v0.1 ## Prompt format No chat template specified so default is used. This may be incorrect, check original model card for details. ``` <s> [INST] <<SYS>> {system_prompt} <</SYS>> {prompt} [/INST] </s> ``` ## Available sizes \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ------ \| ---- \| ------------ \| ---- \| ---- \| ---- \| ----------- \| \| [8_0](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/8_0) \| 8.0 \| 8.0 \| 23.5 GB \| 26.0 GB \| 29.5 GB \| Near unquantized performance, max quality ExLlamaV2 can create. \| \| [6_5](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/6_5) \| 6.5 \| 8.0 \| 19.4 GB \| 21.9 GB \| 25.4 GB \| Near unquantized performance at vastly reduced size, recommended. \| \| [5_0](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/5_0) \| 5.0 \| 6.0 \| 15.5 GB \| 18.0 GB \| 21.5 GB \| Smaller size, lower quality, still very high performance, recommended. \| \| [4_25](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/4_25) \| 4.25 \| 6.0 \| 13.3 GB \| 15.8 GB \| 19.3 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/3_5) \| 3.5 \| 6.0 \| 11.6 GB \| 14.1 GB \| 17.6 GB \| Lower quality, only use if you have to. \| \| [3_0](https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2/tree/3_0) \| 3.0 \| 6.0 \| 9.8 GB \| 12.3 GB \| 15.8 GB \| Very low quality. Usable on 12GB with low context or 16gb with 32k. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Codestral-22B-v0.1-exl2 Codestral-22B-v0.1-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/Codestral-22B-v0.1-exl2 --revision 6_5 --local-dir Codestral-22B-v0.1-exl2-6_5 ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell huggingface-cli download bartowski/Codestral-22B-v0.1-exl2 --revision 6_5 --local-dir Codestral-22B-v0.1-exl2-6.5 ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
zera09/llama_FT	zera09	2024-05-29T22:30:19Z	5	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-13b-chat-hf", "base_model:adapter:meta-llama/Llama-2-13b-chat-hf", "license:llama2", "region:us" ]	null	2024-05-29T14:20:24Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-13b-chat-hf model-index: - name: llama_FT results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # llama_FT 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) . # ## Intended uses & limitations ``` from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM,AutoTokenizer ``` Loading Tokenizer ``` model_id = "meta-llama/Llama-2-13b-chat-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) tokenizer.pad_token = tokenizer.eos_token ``` Loading Model ``` config = PeftConfig.from_pretrained("zera09/llama_FT") base_model = AutoModelForCausalLM.from_pretrained(model_id,load_in_4bit=True, device_map='cuda') model = PeftModel.from_pretrained(base_model, "zera09/llama_FT") ``` Template for inference ``` template = """### Instruction Given this context: {context} and price:{price}output onle one decision from the square bracket [buy,sell,hold] and provide reasoning om why. ### Response: Decision: Reasonong: ```""" ``` ``` from transformers import set_seed def gen(text): toks = tokenizer(text, return_tensors="pt").to("cuda") set_seed(32) model.eval() with torch.no_grad(): out = model.generate( toks, max_new_tokens=350, top_k=5, do_sample=True, ) return tokenizer.decode( out[0][len(toks["input_ids"][0]) :], skip_special_tokens=True ) ``` Runign Inference On single text ``` context = "The global recloser control market is expected to grow significantly, driven by increasing demand for power quality and reliability, especially in the electric segment and emerging economies like China. The positive score for this news is 1.1491235518690246e-08. The neutral score for this news is 0.9999998807907104. The negative score for this news is 6.358970239261907e-08" price = str(12.1) print(gen(template.format(context=news,price=price)).split("```")) ``` For multiple text ``` import pandas as pd data = panda.read_pickle('./DRIV_train.pkl') data = pd.DataFrame(data).T model.eval() answer_list = [] for idx,row in ans_sum.iterrows(): toks = tokenizer(template.format(context=row['news']['DRIV'][0],price=str(row['price']['DRIV'][0]))), return_tensors="pt").to("cuda") with torch.no_grad(): out = model.generate( toks, max_new_tokens=350, top_k=5, do_sample=True, ) ans_list.append(tokenizer.decode( out[0][len(toks["input_ids"][0]) :], skip_special_tokens=True ) ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 12 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.8.2 - Transformers 4.41.1 - Pytorch 1.13.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
Alexsandr0x/keras-dummy-sequential-demo	Alexsandr0x	2024-05-29T22:29:14Z	0	0	keras	[ "keras", "tf-keras", "region:us" ]	null	2024-05-24T00:02:35Z	--- library_name: keras --- ## 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: \| Hyperparameters \| Value \| \| :-- \| :-- \| \| name \| Adam \| \| weight_decay \| None \| \| clipnorm \| None \| \| global_clipnorm \| None \| \| clipvalue \| None \| \| use_ema \| False \| \| ema_momentum \| 0.99 \| \| ema_overwrite_frequency \| None \| \| jit_compile \| True \| \| is_legacy_optimizer \| False \| \| learning_rate \| 0.0010000000474974513 \| \| beta_1 \| 0.9 \| \| beta_2 \| 0.999 \| \| epsilon \| 1e-07 \| \| amsgrad \| False \| \| training_precision \| float32 \|
jeeyoung/dpo417610th_trial_10000_data	jeeyoung	2024-05-29T22:28:57Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:27:45Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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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. 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zhan1993/custom-resnet50d	zhan1993	2024-05-29T22:28:53Z	245	0	transformers	[ "transformers", "safetensors", "resnet", "image-classification", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "region:us" ]	image-classification	2024-05-29T22:24:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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. 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jeeyoung/dpo382810th_trial_10000_data	jeeyoung	2024-05-29T22:25:37Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:24:19Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
mayssakorbi/whisper-tiny-ar2	mayssakorbi	2024-05-29T22:23:07Z	93	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice_16_1", "base_model:mayssakorbi/whisper-tiny-ar2", "base_model:finetune:mayssakorbi/whisper-tiny-ar2", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T11:25:48Z	--- license: apache-2.0 base_model: mayssakorbi/whisper-tiny-ar2 tags: - generated_from_trainer datasets: - common_voice_16_1 metrics: - wer model-index: - name: whisper-tiny-ar2 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_1 type: common_voice_16_1 config: ar split: test[1000:1500] args: ar metrics: - name: Wer type: wer value: 87.11256117455139 --- <!-- 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. --> # whisper-tiny-ar2 This model is a fine-tuned version of [mayssakorbi/whisper-tiny-ar2](https://huggingface.co/mayssakorbi/whisper-tiny-ar2) on the common_voice_16_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.9240 - Wer Ortho: 77.8656 - Wer: 87.1126 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - 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_with_warmup - lr_scheduler_warmup_steps: 10 - training_steps: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.5216 \| 0.4 \| 50 \| 0.9240 \| 77.8656 \| 87.1126 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
shane062/whisper-small-finetuned	shane062	2024-05-29T22:23:00Z	96	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "dataset:audiofolder", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-29T14:22:42Z	--- license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - audiofolder metrics: - wer model-index: - name: whisper-small-finetuned results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: audiofolder type: audiofolder config: default split: test args: default metrics: - name: Wer type: wer value: 67.56756756756756 --- <!-- 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. --> # whisper-small-finetuned This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.8410 - Wer Ortho: 67.5676 - Wer: 67.5676 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant_with_warmup - lr_scheduler_warmup_steps: 10 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Ortho \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|:-------:\| \| 0.7732 \| 16.6667 \| 50 \| 1.3685 \| 70.2703 \| 70.2703 \| \| 0.0005 \| 33.3333 \| 100 \| 0.8410 \| 67.5676 \| 67.5676 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cpu - Datasets 2.19.1 - Tokenizers 0.19.1
jeeyoung/dpo348010th_trial_10000_data	jeeyoung	2024-05-29T22:22:10Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:21:01Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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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. 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jeeyoung/dpo313210th_trial_10000_data	jeeyoung	2024-05-29T22:18:55Z	2	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:17:43Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.0
salmanshahid/omega_a2a_test	salmanshahid	2024-05-29T22:16:20Z	0	1	null	[ "any-to-any", "omega", "omegalabs", "bittensor", "agi", "license:mit", "region:us" ]	any-to-any	2024-05-21T03:35:41Z	--- license: mit tags: - any-to-any - omega - omegalabs - bittensor - agi --- This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet. Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
Sonatafyai/Fake_news_Detection_SonatafyAI_RoBERTa	Sonatafyai	2024-05-29T22:12:44Z	117	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T21:48:45Z	--- license: apache-2.0 base_model: distilbert/distilroberta-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: Fake_news_Detection_SonatafyAI_RoBERTa results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Fake_news_Detection_SonatafyAI_RoBERTa This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 - Accuracy: 1.0 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0001 \| 1.0 \| 2245 \| 0.0007 \| 0.9999 \| \| 0.0 \| 2.0 \| 4490 \| 0.0000 \| 1.0 \| \| 0.0 \| 3.0 \| 6735 \| 0.0000 \| 1.0 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
BVRA/convnext_base.in1k_ft_fungitastic-m_384	BVRA	2024-05-29T22:11:20Z	10	0	DanishFungi	[ "DanishFungi", "pytorch", "image-classification", "ecology", "fungi", "FGVC", "license:cc-by-nc-4.0", "region:us" ]	image-classification	2024-05-29T22:10:43Z	--- tags: - image-classification - ecology - fungi - FGVC library_name: DanishFungi license: cc-by-nc-4.0 --- # Model card for BVRA/convnext_base.in1k_ft_df24m_384 ## Model Details - Model Type: Danish Fungi Classification - Model Stats: - Params (M): 87.8 - Image size: 384 x 384 - Papers: - Original: ?? - Train Dataset: DF24M --> https://sites.google.com/view/danish-fungi-dataset ## Model Usage ### Image Embeddings ```python import timm import torch import torchvision.transforms as T from PIL import Image from urllib.request import urlopen model = timm.create_model("hf-hub:BVRA/convnext_base.in1k_ft_df24m_384", pretrained=True) model = model.eval() train_transforms = T.Compose([T.Resize((384, 384)), T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]) img = Image.open(PATH_TO_YOUR_IMAGE) output = model(train_transforms(img).unsqueeze(0)) # output is a (1, num_features) shaped tensor ``` ## Citation ```bibtex @InProceedings{Picek_2022_WACV, author = {Picek, Luk'a{s} and {S}ulc, Milan and Matas, Ji{r}{'\i} and Jeppesen, Thomas S. and Heilmann-Clausen, Jacob and L{e}ss{\o}e, Thomas and Fr{\o}slev, Tobias}, title = {Danish Fungi 2020 - Not Just Another Image Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {1525-1535} } ``` ```bibtex @article{picek2022automatic, title={Automatic Fungi Recognition: Deep Learning Meets Mycology}, author={Picek, Luk{'a}{{s}} and {{S}}ulc, Milan and Matas, Ji{{r}}{'\i} and Heilmann-Clausen, Jacob and Jeppesen, Thomas S and Lind, Emil}, journal={Sensors}, volume={22}, number={2}, pages={633}, year={2022}, publisher={Multidisciplinary Digital Publishing Institute} } ```
andrian-kr/sherlock-mistral-asset-smpl	andrian-kr	2024-05-29T22:10:59Z	0	0	peft	[ "peft", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "base_model:SherlockAssistant/Mistral-7B-Instruct-Ukrainian", "base_model:adapter:SherlockAssistant/Mistral-7B-Instruct-Ukrainian", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-05-29T18:56:02Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: SherlockAssistant/Mistral-7B-Instruct-Ukrainian model-index: - name: sherlock-mistral-asset-smpl results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # sherlock-mistral-asset-smpl This model is a fine-tuned version of [SherlockAssistant/Mistral-7B-Instruct-Ukrainian](https://huggingface.co/SherlockAssistant/Mistral-7B-Instruct-Ukrainian) 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: 0.0003 - train_batch_size: 4 - eval_batch_size: 4 - 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: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.19.1
John6666/xl-caulkinum-animeline-gd-sdxl	John6666	2024-05-29T22:09:52Z	90	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-29T22:03:26Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/308286/sdxl-xlcaulkinum-animeline-for-2d-anime-characters?modelVersionId=446043).
dev137/Codestral-22B-v0.1-exl2-3.0bpw	dev137	2024-05-29T22:08:55Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "3-bit", "exl2", "region:us" ]	text-generation	2024-05-29T22:07:07Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall
scandukuri/llama3-8b-stargate-m1	scandukuri	2024-05-29T22:08:38Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:2403.19154", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-29T22:01:16Z	--- license: mit --- # STaR-GATE This repository contains the iteration 1 [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model from an additional experiment for [STaR-GATE: Teaching Language Models to Ask Clarifying Questions](https://arxiv.org/abs/2403.19154). Note that this experiment is an extension and is not yet included in the most recent revision of the linked preprint. The weights contained in this repository are represented by the <span style="color:#2EA5E7">blue</span> line in the left-side win-rate graph below. Note that this repository contains the weights for iteration t=1, i.e. only one iteration of self-improvement. When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models often struggle to ask good questions. We explore a language model's ability to self-improve (STaR; Zelikman et al., 2022) by rewarding the model for generating useful questions-a simple method we dub STaR-GATE. We generate a synthetic dataset of 25,500 unique persona-task prompts to simulate conversations between a pretrained language model-the Questioner-and a Roleplayer whose preferences are unknown to the Questioner. By asking questions, the Questioner elicits preferences from the Roleplayer. The Questioner is iteratively finetuned on questions that increase the probability of high-quality responses to the task, which are generated by an Oracle with access to the Roleplayer's latent preferences. After two iterations of self-improvement, the Questioner asks better questions, allowing it to generate responses that are preferred over responses from the initial model on 72% of tasks. Our results indicate that teaching a language model to ask better questions leads to better personalized responses. <p align="center"> <br> <img src="https://github.com/scandukuri/assistant-gate/assets/87667591/7c2fe82a-04e8-4779-ab8d-c2476724ac69" alt="fig_3"> <br><br> </p> # Usage Reference the [paper](https://arxiv.org/abs/2403.19154) appendix sections ```A.5.2``` (Figure 14: Questioner Elicitation Prompt) and ```A.6.2``` (Figure 17: Questioner Win-Rate Response Prompt.) to see how you can prompt the model for elicitation or for final responses. All code and data for the project can be found [here](https://github.com/scandukuri/assistant-gate).
RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf	RichardErkhov	2024-05-29T22:06:10Z	4	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-29T18:56:46Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mistral-7B-v0.1-hf - GGUF - Model creator: https://huggingface.co/kittn/ - Original model: https://huggingface.co/kittn/mistral-7B-v0.1-hf/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [mistral-7B-v0.1-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q2_K.gguf) \| Q2_K \| 2.53GB \| \| [mistral-7B-v0.1-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.IQ3_XS.gguf) \| IQ3_XS \| 2.81GB \| \| [mistral-7B-v0.1-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.IQ3_S.gguf) \| IQ3_S \| 2.96GB \| \| [mistral-7B-v0.1-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q3_K_S.gguf) \| Q3_K_S \| 2.95GB \| \| [mistral-7B-v0.1-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.IQ3_M.gguf) \| IQ3_M \| 3.06GB \| \| [mistral-7B-v0.1-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q3_K.gguf) \| Q3_K \| 3.28GB \| \| [mistral-7B-v0.1-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q3_K_M.gguf) \| Q3_K_M \| 3.28GB \| \| [mistral-7B-v0.1-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q3_K_L.gguf) \| Q3_K_L \| 3.56GB \| \| [mistral-7B-v0.1-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.IQ4_XS.gguf) \| IQ4_XS \| 3.67GB \| \| [mistral-7B-v0.1-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q4_0.gguf) \| Q4_0 \| 3.83GB \| \| [mistral-7B-v0.1-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.IQ4_NL.gguf) \| IQ4_NL \| 3.87GB \| \| [mistral-7B-v0.1-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q4_K_S.gguf) \| Q4_K_S \| 3.86GB \| \| [mistral-7B-v0.1-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q4_K.gguf) \| Q4_K \| 4.07GB \| \| [mistral-7B-v0.1-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q4_K_M.gguf) \| Q4_K_M \| 4.07GB \| \| [mistral-7B-v0.1-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q4_1.gguf) \| Q4_1 \| 4.24GB \| \| [mistral-7B-v0.1-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q5_0.gguf) \| Q5_0 \| 4.65GB \| \| [mistral-7B-v0.1-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q5_K_S.gguf) \| Q5_K_S \| 4.65GB \| \| [mistral-7B-v0.1-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q5_K.gguf) \| Q5_K \| 4.78GB \| \| [mistral-7B-v0.1-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q5_K_M.gguf) \| Q5_K_M \| 4.78GB \| \| [mistral-7B-v0.1-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q5_1.gguf) \| Q5_1 \| 5.07GB \| \| [mistral-7B-v0.1-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q6_K.gguf) \| Q6_K \| 5.53GB \| \| [mistral-7B-v0.1-hf.Q8_0.gguf](https://huggingface.co/RichardErkhov/kittn_-_mistral-7B-v0.1-hf-gguf/blob/main/mistral-7B-v0.1-hf.Q8_0.gguf) \| Q8_0 \| 7.17GB \| Original model description: ## UPDATE: Official version is out, use it instead: [https://huggingface.co/mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) --- --- --- --- # mistral-7B-v0.1-hf Huggingface compatible version of Mistral's 7B model: https://twitter.com/MistralAI/status/1706877320844509405 ## Usage ### Load in bfloat16 (16GB VRAM or higher) ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf") model = LlamaForCausalLM.from_pretrained( "kittn/mistral-7B-v0.1-hf", torch_dtype=torch.bfloat16, device_map={"": 0} ) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128) ``` ### Load in bitsandbytes nf4 (6GB VRAM or higher, maybe less with double_quant) ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf") model = LlamaForCausalLM.from_pretrained( "kittn/mistral-7B-v0.1-hf", device_map={"": 0}, quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=False, # set to True to save more VRAM at the cost of some speed/accuracy ), ) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128) ``` ### Load in bitsandbytes int8 (8GB VRAM or higher). Quite slow; not recommended. ```python import torch from transformers import LlamaForCausalLM, LlamaTokenizer, pipeline, TextStreamer, BitsAndBytesConfig tokenizer = LlamaTokenizer.from_pretrained("kittn/mistral-7B-v0.1-hf") model = LlamaForCausalLM.from_pretrained( "kittn/mistral-7B-v0.1-hf", device_map={"": 0}, quantization_config=BitsAndBytesConfig( load_in_8bit=True, ), ) pipe = pipeline("text-generation", model=model, tokenizer=tokenizer) pipe("Hi, my name", streamer=TextStreamer(tokenizer), max_new_tokens=128) ``` ## Notes * The original huggingface conversion script converts the model from bf16 to fp16 before saving it. This script doesn't * The tokenizer is created with `legacy=False`, [more about this here](https://github.com/huggingface/transformers/pull/24565) * Saved in safetensors format ## Conversion script [[link]](https://gist.github.com/sekstini/151d6946df1f6aa997b7cb15ee6f3be1) Unlike [meta-llama/Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b), this model uses GQA. This breaks some assumptions in the original conversion script, requiring a few changes. Conversion script: [link](https://gist.github.com/sekstini/151d6946df1f6aa997b7cb15ee6f3be1) Original conversion script: [link](https://github.com/huggingface/transformers/blob/946bac798caefada3f5f1c9fecdcfd587ed24ac7/src/transformers/models/llama/convert_llama_weights_to_hf.py)
jeeyoung/dpo174010th_trial_10000_data	jeeyoung	2024-05-29T22:05:36Z	1	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T22:04:26Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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amaiaruvi/beto-racist-comments-spanish	amaiaruvi	2024-05-29T22:05:19Z	115	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-29T22:01:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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numen-tech/Mistral-7B-Instruct-v0.3-w4a16g128asym	numen-tech	2024-05-29T22:02:11Z	0	0	null	[ "arxiv:2308.13137", "license:apache-2.0", "region:us" ]	null	2024-05-29T21:59:33Z	--- license: apache-2.0 --- 4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3).
jeeyoung/dpo69610th_trial_10000_data	jeeyoung	2024-05-29T21:55:33Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:beomi/KoAlpaca-Polyglot-5.8B", "base_model:adapter:beomi/KoAlpaca-Polyglot-5.8B", "region:us" ]	null	2024-05-29T21:54:24Z	--- library_name: peft base_model: beomi/KoAlpaca-Polyglot-5.8B --- # 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. 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John6666/xl-caulkinum-animeline-gep-sdxl	John6666	2024-05-29T21:54:57Z	96	1	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "anime", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-29T21:46:07Z	--- license: other tags: - text-to-image - stable-diffusion - stable-diffusion-xl - anime --- Original model is [here](https://civitai.com/models/308286/sdxl-xlcaulkinum-animeline-for-2d-anime-characters?modelVersionId=446043).
RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf	RichardErkhov	2024-05-29T21:52:36Z	9	0	null	[ "gguf", "endpoints_compatible", "region:us" ]	null	2024-05-29T18:38:57Z	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-2-7b-rockwell-final - GGUF - Model creator: https://huggingface.co/maximuslee07/ - Original model: https://huggingface.co/maximuslee07/llama-2-7b-rockwell-final/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [llama-2-7b-rockwell-final.Q2_K.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q2_K.gguf) \| Q2_K \| 2.36GB \| \| [llama-2-7b-rockwell-final.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.IQ3_XS.gguf) \| IQ3_XS \| 2.6GB \| \| [llama-2-7b-rockwell-final.IQ3_S.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.IQ3_S.gguf) \| IQ3_S \| 2.75GB \| \| [llama-2-7b-rockwell-final.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q3_K_S.gguf) \| Q3_K_S \| 2.75GB \| \| [llama-2-7b-rockwell-final.IQ3_M.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.IQ3_M.gguf) \| IQ3_M \| 2.9GB \| \| [llama-2-7b-rockwell-final.Q3_K.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q3_K.gguf) \| Q3_K \| 3.07GB \| \| [llama-2-7b-rockwell-final.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q3_K_M.gguf) \| Q3_K_M \| 3.07GB \| \| [llama-2-7b-rockwell-final.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q3_K_L.gguf) \| Q3_K_L \| 3.35GB \| \| [llama-2-7b-rockwell-final.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.IQ4_XS.gguf) \| IQ4_XS \| 3.4GB \| \| [llama-2-7b-rockwell-final.Q4_0.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q4_0.gguf) \| Q4_0 \| 3.56GB \| \| [llama-2-7b-rockwell-final.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.IQ4_NL.gguf) \| IQ4_NL \| 3.58GB \| \| [llama-2-7b-rockwell-final.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q4_K_S.gguf) \| Q4_K_S \| 3.59GB \| \| [llama-2-7b-rockwell-final.Q4_K.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q4_K.gguf) \| Q4_K \| 3.8GB \| \| [llama-2-7b-rockwell-final.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q4_K_M.gguf) \| Q4_K_M \| 3.8GB \| \| [llama-2-7b-rockwell-final.Q4_1.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q4_1.gguf) \| Q4_1 \| 3.95GB \| \| [llama-2-7b-rockwell-final.Q5_0.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q5_0.gguf) \| Q5_0 \| 4.33GB \| \| [llama-2-7b-rockwell-final.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q5_K_S.gguf) \| Q5_K_S \| 4.33GB \| \| [llama-2-7b-rockwell-final.Q5_K.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q5_K.gguf) \| Q5_K \| 4.45GB \| \| [llama-2-7b-rockwell-final.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q5_K_M.gguf) \| Q5_K_M \| 4.45GB \| \| [llama-2-7b-rockwell-final.Q5_1.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q5_1.gguf) \| Q5_1 \| 4.72GB \| \| [llama-2-7b-rockwell-final.Q6_K.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q6_K.gguf) \| Q6_K \| 5.15GB \| \| [llama-2-7b-rockwell-final.Q8_0.gguf](https://huggingface.co/RichardErkhov/maximuslee07_-_llama-2-7b-rockwell-final-gguf/blob/main/llama-2-7b-rockwell-final.Q8_0.gguf) \| Q8_0 \| 6.67GB \| Original model description: --- license: llama2 ---
Alissa111/a	Alissa111	2024-05-29T21:49:57Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T20:55:11Z	--- license: apache-2.0 ---
dev137/Codestral-22B-v0.1-exl2-3.5bpw	dev137	2024-05-29T21:49:01Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "code", "license:other", "autotrain_compatible", "text-generation-inference", "exl2", "region:us" ]	text-generation	2024-05-29T21:46:49Z	--- inference: false license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script # Model Card for Codestral-22B-v0.1 Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried: - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code) ## Installation It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference). ``` pip install mistral_inference ``` ## Download ```py from huggingface_hub import snapshot_download from pathlib import Path mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1') mistral_models_path.mkdir(parents=True, exist_ok=True) snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path) ``` ### Chat After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. ``` mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256 ``` Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines: ``` Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number. fn fibonacci(n: u32) -> u32 { match n { 0 => 0, 1 => 1, _ => fibonacci(n - 1) + fibonacci(n - 2), } } fn main() { let n = 10; println!("The {}th Fibonacci number is: {}", n, fibonacci(n)); } This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers. ``` ### Fill-in-the-middle (FIM) After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed: ```py from mistral_inference.model import Transformer from mistral_inference.generate import generate from mistral_common.tokens.tokenizers.mistral import MistralTokenizer from mistral_common.tokens.instruct.request import FIMRequest tokenizer = MistralTokenizer.v3() model = Transformer.from_folder("~/codestral-22B-240529") prefix = """def add(""" suffix = """ return sum""" request = FIMRequest(prompt=prefix, suffix=suffix) tokens = tokenizer.encode_fim(request).tokens out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id) result = tokenizer.decode(out_tokens[0]) middle = result.split(suffix)[0].strip() print(middle) ``` Should give something along the following lines: ``` num1, num2): # Add two numbers sum = num1 + num2 # return the sum ``` ## Limitations The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## License Codestral-22B-v0.1 is released under the `MNLP-0.1` license. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

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