Huggingfaceh4
Models by this creator
🛠️
zephyr-7b-beta
1.5K
zephyr-7b-beta is a 7 billion parameter language model developed by HuggingFaceH4 as part of the Zephyr series of models trained to act as helpful assistants. It is a fine-tuned version of mistralai/Mistral-7B-v0.1, trained on publicly available, synthetic datasets using Direct Preference Optimization (DPO). The model has been optimized for performance on benchmarks like MT Bench and AlpacaEval, outperforming larger open models like Llama2-Chat-70B. Model inputs and outputs Inputs Text**: The model takes text-only data as input. Outputs Text generation**: The model generates natural language text as output. Capabilities zephyr-7b-beta has shown strong performance on a variety of benchmarks, particularly in the areas of open-ended text generation and question answering. It outperforms larger models like Llama2-Chat-70B on the MT Bench and AlpacaEval benchmarks, demonstrating its capabilities as a helpful language assistant. What can I use it for? zephyr-7b-beta can be used for a variety of natural language processing tasks, such as: Chatbots and virtual assistants**: The model can be used to power conversational interfaces that can engage in helpful and informative dialogues. Content generation**: The model can be used to generate high-quality text content, such as articles, stories, or product descriptions. Question answering**: The model can be used to answer a wide range of questions, drawing upon its broad knowledge base. Things to try Researchers and developers can experiment with zephyr-7b-beta to explore its capabilities in areas like open-ended conversation, creative writing, and task-oriented dialogue. The model's strong performance on benchmarks suggests it may be a useful tool for a variety of natural language processing applications.
Updated 5/27/2024
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zephyr-7b-alpha
1.1K
The zephyr-7b-alpha is a 7 billion parameter language model developed by HuggingFaceH4. It is part of the Zephyr series of models trained to act as helpful assistants. The model was fine-tuned from the mistralai/Mistral-7B-v0.1 model using a mix of publicly available, synthetic datasets and Direct Preference Optimization (DPO). Compared to the original Mistral model, the Zephyr-7B-alpha model has improved performance on benchmarks like MT Bench and AlpacaEval, though it may also generate more problematic text when prompted. Model inputs and outputs The zephyr-7b-alpha model is a text-to-text AI assistant, meaning it takes text prompts as input and generates relevant text responses. The model was trained on a diverse range of synthetic dialogue data, so it can engage in open-ended conversations and assist with a variety of language tasks. Inputs Text prompts or messages that the user wants the AI to respond to Outputs Relevant, coherent text responses generated by the model The model can generate responses of varying length depending on the prompt Capabilities The zephyr-7b-alpha model has strong performance on benchmarks like MT Bench and AlpacaEval, outperforming larger models like Llama2-Chat-70B on certain categories. It can engage in helpful, open-ended conversations across a wide range of topics. However, the model may also generate problematic text when prompted, as it was not trained with the same safeguards as models like ChatGPT. What can I use it for? The zephyr-7b-alpha model can be used for a variety of language-based tasks, such as: Open-ended chatbots and conversational assistants Question answering Summarization Creative writing You can test out the model's capabilities on the Zephyr chat demo provided by the maintainers. The model is available through the Hugging Face Transformers library, allowing you to easily integrate it into your own projects. Things to try One interesting aspect of the zephyr-7b-alpha model is its use of Direct Preference Optimization (DPO) during fine-tuning. This training approach boosted the model's performance on benchmarks, but also means it may generate more problematic content than models trained with additional alignment safeguards. It would be interesting to experiment with prompting the model to see how it responds in different contexts, and to compare its behavior to other large language models.
Updated 5/28/2024
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starchat-beta
261
The starchat-beta model is a 16B parameter GPT-like language model that has been fine-tuned on an "uncensored" variant of the openassistant-guanaco dataset by HuggingFaceH4. This fine-tuning process removed the in-built alignment of the OpenAssistant dataset, which the maintainers found boosted performance on the Open LLM Leaderboard and made the model more helpful at coding tasks. However, this also means the model is likely to generate problematic text when prompted, and should only be used for educational and research purposes. Similar models released by HuggingFaceH4 include the starchat2-15b-v0.1 and the starchat-alpha models, which also aim to serve as helpful coding assistants but with various differences in dataset, scale, and alignment approaches. Model inputs and outputs Inputs Text prompts**: The starchat-beta model accepts text prompts as input, which it can use to generate responses. Outputs Generated text**: The model outputs generated text, which can include code snippets, responses to queries, and other text. Capabilities The starchat-beta model is designed to act as a helpful coding assistant, with the ability to generate code in over 80 programming languages. It can be used to assist with a variety of coding-related tasks, such as explaining programming concepts, generating sample code, and providing suggestions for code improvements. What can I use it for? The starchat-beta model can be a useful tool for educational and research purposes, particularly in the context of exploring the capabilities of large language models in coding tasks. Developers and researchers may find the model helpful for prototyping ideas, testing hypotheses, or exploring the limits of current AI-powered coding assistance. However, due to the "uncensored" nature of the dataset used for fine-tuning, the model may also generate problematic or harmful content when prompted. As such, it should be used with caution and only for non-commercial, educational, and research purposes. Things to try One interesting aspect of the starchat-beta model is its ability to generate code in a wide range of programming languages. You could try providing prompts that ask the model to write code in various languages, and observe how the generated output varies. Additionally, you could experiment with different prompting strategies to see how the model responds, such as asking it to explain coding concepts or to provide suggestions for improving existing code snippets.
Updated 5/28/2024
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zephyr-orpo-141b-A35b-v0.1
236
The zephyr-orpo-141b-A35b-v0.1 is the latest model in the Zephyr series of language models developed by HuggingFaceH4 to act as helpful assistants. It is a fine-tuned version of the mistral-community/Mixtral-8x22B-v0.1 model that uses a novel training algorithm called Odds Ratio Preference Optimization (ORPO) to achieve high performance without the need for a separate supervised fine-tuning (SFT) step. This makes the training process more computationally efficient. The model was trained on the argilla/distilabel-capybara-dpo-7k-binarized preference dataset, which contains high-quality, multi-turn preferences scored by large language models. Model inputs and outputs Inputs Text prompts for the model to continue or generate Outputs Continuation of the input text prompt Generated text in response to the input prompt Capabilities The zephyr-orpo-141b-A35b-v0.1 model achieves strong performance on chat benchmarks like MT Bench and IFEval, demonstrating its capabilities as a helpful AI assistant. What can I use it for? The zephyr-orpo-141b-A35b-v0.1 model can be used for a variety of natural language tasks, such as open-ended conversation, question answering, and text generation. It could be integrated into chatbots, virtual assistants, or other applications that require language understanding and generation. However, as with any large language model, care must be taken to ensure the outputs are aligned with the intended use case and do not contain harmful or biased information. Things to try One interesting aspect of the zephyr-orpo-141b-A35b-v0.1 model is its use of the ORPO training algorithm, which aims to improve efficiency compared to other preference-based training methods like DPO and PPO. Experimenting with different prompts and tasks could help uncover the specific strengths and limitations of this approach, and how it compares to other state-of-the-art language models.
Updated 5/28/2024
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starchat-alpha
229
starchat-alpha is a language model developed by HuggingFaceH4 that is fine-tuned from the bigcode/starcoderbase model to act as a helpful coding assistant. It is the first in a series of "StarChat" models, and as an alpha release, is intended only for educational or research purposes. The model has not been aligned to human preferences using techniques like Reinforcement Learning from Human Feedback (RLHF), so it may generate problematic content, especially if prompted to do so. In contrast, the starchat2-15b-v0.1 model is a later version in the series that has been fine-tuned using Supervised Fine-Tuning (SFT) and Debate-Preference Optimization (DPO) on a mix of synthetic datasets. It achieves stronger performance on chat and programming benchmarks compared to starchat-alpha. The Starling-LM-7B-alpha and Starling-LM-7B-beta models are also fine-tuned language models, but they use Reinforcement Learning from AI Feedback (RLAIF) and Preference Learning (PPO) techniques to improve helpfulness and safety. Model inputs and outputs Inputs Natural language prompts**: The model can accept natural language prompts, such as questions or instructions, that are related to programming tasks. Outputs Code snippets**: The model can generate code snippets in response to programming-related prompts. Natural language responses**: The model can also provide natural language responses to explain or clarify its code outputs. Capabilities starchat-alpha can generate code snippets in a variety of programming languages based on the provided prompts. It demonstrates strong capabilities in areas like syntax generation, algorithm implementation, and software engineering best practices. However, the model's outputs may contain bugs, security vulnerabilities, or other issues, as it has not been thoroughly aligned to ensure safety and reliability. What can I use it for? starchat-alpha can be used for educational and research purposes to explore the capabilities of open-source language models in the programming domain. Developers and researchers can experiment with the model to gain insights into its strengths and limitations, and potentially use it as a starting point for further fine-tuning or research into more robust and reliable coding assistants. Things to try One interesting aspect of starchat-alpha is its tendency to generate false URLs. Users should carefully inspect any URLs produced by the model before clicking on them, as they may lead to unintended or potentially harmful destinations. Experimenting with prompts that test the model's URL generation capabilities could yield valuable insights into its limitations and potential risks. Additionally, users could try prompting the model to generate code for specific programming tasks or challenges, and then evaluate the quality, correctness, and security of the resulting code snippets. This could help identify areas where the model performs well, as well as areas where further refinement or alignment is needed.
Updated 5/28/2024
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zephyr-7b-gemma-v0.1
118
The zephyr-7b-gemma-v0.1 is a 7 billion parameter language model from Hugging Face's HuggingFaceH4 that is fine-tuned on a mix of publicly available, synthetic datasets. It is a version of the google/gemma-7b model that has been further trained using Direct Preference Optimization (DPO). This model is part of the Zephyr series of language models aimed at serving as helpful AI assistants. Compared to the earlier zephyr-7b-beta model, the zephyr-7b-gemma-v0.1 achieves higher performance on benchmarks like MT Bench and IFEval. Model inputs and outputs Inputs Text prompts or messages in English Outputs Longer form text responses in English, generated to be helpful and informative Capabilities The zephyr-7b-gemma-v0.1 model is capable of generating human-like text on a wide variety of topics. It can be used for tasks like question answering, summarization, and open-ended conversation. The model's strong performance on benchmarks like MT Bench and IFEval suggests it is well-suited for natural language generation and understanding. What can I use it for? The zephyr-7b-gemma-v0.1 model could be useful for building conversational AI assistants, chatbots, and other applications that require natural language interaction. Its flexibility means it could be applied to tasks like content creation, summarization, and information retrieval. Developers could integrate the model into their projects to provide helpful and engaging language-based capabilities. Things to try One interesting aspect of the zephyr-7b-gemma-v0.1 model is its training approach using Direct Preference Optimization (DPO). This technique, described in the Alignment Handbook, aims to align the model's behavior with human preferences during the fine-tuning process. Developers could experiment with prompts that test the model's alignment, such as asking it to generate text on sensitive topics or to complete tasks that require ethical reasoning.
Updated 5/28/2024
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starchat2-15b-v0.1
88
The starchat2-15b-v0.1 model is a 15B parameter language model fine-tuned from the StarCoder2 model to act as a helpful coding assistant. It was trained by HuggingFaceH4 on a mix of synthetic datasets to balance chat and programming capabilities. The model achieves strong performance on chat benchmarks like MT Bench and IFEval, as well as the canonical HumanEval benchmark for Python code completion. Model inputs and outputs Inputs Text**: The model takes natural language text as input, which can include instructions, questions, or code snippets. Outputs Generated text**: The model outputs generated text, which can include responses to the input, completed code, or new text continuing the provided input. Capabilities The starchat2-15b-v0.1 model is capable of engaging in helpful conversations, answering questions, and generating code across a wide range of programming languages. It can assist with tasks like code completion, code explanation, and even high-level program design. What can I use it for? With its strong chat and programming capabilities, the starchat2-15b-v0.1 model can be used for a variety of applications. Developers could integrate it into their IDEs or workflow to boost productivity, while businesses could use it to provide technical support or automate certain coding tasks. The model could also be fine-tuned further for specialized domains or applications. Things to try One interesting thing to try with the starchat2-15b-v0.1 model is to provide it with partial code snippets and see how it completes them. You could also try giving the model high-level instructions or ideas and see how it translates those into working code. Additionally, you could explore the model's ability to explain code and programming concepts in natural language.
Updated 5/28/2024
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mistral-7b-grok
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The mistral-7b-grok model is a fine-tuned version of the mistralai/Mistral-7B-v0.1 model that has been aligned via Constitutional AI to mimic the style of xAI's Grok assistant. This model was developed by HuggingFaceH4. The model has been trained to achieve a loss of 0.9348 on the evaluation set, indicating strong performance. However, details about the model's intended uses and limitations, as well as the training and evaluation data, are not provided. Model Inputs and Outputs Inputs Text inputs for text-to-text tasks Outputs Transformed text outputs based on the input Capabilities The mistral-7b-grok model can be used for various text-to-text tasks, such as language generation, summarization, and translation. By mimicking the style of the Grok assistant, the model may be well-suited for conversational or interactive applications. What can I use it for? The mistral-7b-grok model could be used to develop interactive chatbots or virtual assistants that mimic the persona of the Grok assistant. This may be useful for customer service, educational applications, or entertainment purposes. The model could also be fine-tuned for specific text-to-text tasks, such as summarizing long-form content or translating between languages. Things to Try One interesting aspect of the mistral-7b-grok model is its ability to mimic the conversational style of the Grok assistant. Users could experiment with different prompts or conversation starters to see how the model responds and adapts its language to the desired persona. Additionally, the model could be evaluated on a wider range of tasks or benchmarks to better understand its capabilities and limitations.
Updated 9/6/2024