Argilla

Models by this creator

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notux-8x7b-v1

argilla

Total Score

162

The notux-8x7b-v1 is a preference-tuned version of the mistralai/Mixtral-8x7B-Instruct-v0.1 model, fine-tuned on the argilla/ultrafeedback-binarized-preferences-cleaned dataset using Direct Preference Optimization (DPO). As of Dec 26th 2023, it outperforms the original Mixtral-8x7B-Instruct-v0.1 model and is the top-ranked Mixture of Experts (MoE) model on the Hugging Face Open LLM Leaderboard. This model is part of the Notus family of models, where the Argilla team investigates data-first and preference tuning methods like distilled DPO. Model inputs and outputs The notux-8x7b-v1 model is a generative pretrained language model that can take natural language prompts as input and generate coherent text as output. The model supports multiple languages including English, Spanish, Italian, German, and French. Inputs Natural language prompts**: The model accepts free-form text prompts that provide context or instructions for the desired output. Outputs Generated text**: The model will generate text that continues or expands upon the provided prompt, aiming to be coherent, relevant, and in the style of the input. Capabilities The notux-8x7b-v1 model excels at a variety of language generation tasks, including story writing, question answering, summarization, and creative ideation. It can be used to generate high-quality, coherent text across a wide range of topics and styles. What can I use it for? The notux-8x7b-v1 model could be used for a variety of applications, such as: Content creation**: Generating draft text for articles, blog posts, scripts, stories, and other long-form content. Ideation and brainstorming**: Sparking creative ideas and exploring new concepts through open-ended prompts. Summarization**: Condensing lengthy text into concise summaries. Question answering**: Providing informative responses to queries on a broad range of subjects. Things to try One interesting aspect of the notux-8x7b-v1 model is its ability to generate text that adheres to specific stylistic preferences or guidelines. By crafting prompts that incorporate preferences, users can encourage the model to produce output that aligns with their desired tone, voice, and other characteristics.

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Updated 5/17/2024

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notus-7b-v1

argilla

Total Score

112

notus-7b-v1 is a 7B parameter language model fine-tuned by Argilla using Direct Preference Optimization (DPO) on a curated version of the UltraFeedback dataset. This model was developed as part of the Notus family of models, which explore data-first and preference tuning methods. Compared to the similar zephyr-7b-beta model, notus-7b-v1 uses a modified preference dataset that led to improved performance on benchmarks like AlpacaEval. Model inputs and outputs Inputs Text prompts for the model to continue or generate. Outputs Continuation of the input text, generating coherent and contextually relevant responses. Capabilities notus-7b-v1 demonstrates strong performance on chat-based tasks as evaluated on the MT-Bench and AlpacaEval benchmarks. It surpasses the Zephyr-7b-beta and Claude 2 models in these areas. However, the model has not been fully aligned for safety, so it may produce problematic outputs when prompted to do so. What can I use it for? Argilla intends for notus-7b-v1 to be used as a helpful assistant in chat-like applications. The model's capabilities make it well-suited for tasks like open-ended conversation, question answering, and task completion. However, users should be cautious when interacting with the model, as it lacks the safety alignment of more constrained models like ChatGPT. Things to try Explore the model's capabilities in open-ended conversations and task-oriented prompts. Pay attention to the model's reasoning abilities and its tendency to provide relevant and contextual responses. However, be mindful of potential biases or safety issues that may arise, and use the model with appropriate precautions.

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Updated 5/17/2024

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CapybaraHermes-2.5-Mistral-7B

argilla

Total Score

60

The CapybaraHermes-2.5-Mistral-7B is a 7B chat model developed by Argilla. It is a preference-tuned version of the OpenHermes-2.5-Mistral-7B model, fine-tuned using Argilla's distilabel-capybara-dpo-9k-binarized dataset. The model has shown improved performance on multi-turn conversation benchmarks compared to the base OpenHermes-2.5 model. Similar models include CapybaraHermes-2.5-Mistral-7B-GGUF from TheBloke, which provides quantized versions of the model for efficient inference, and NeuralHermes-2.5-Mistral-7B from mlabonne, which further fine-tunes the model using direct preference optimization. Model inputs and outputs The CapybaraHermes-2.5-Mistral-7B model takes natural language text as input and generates coherent, contextual responses. It can be used for a variety of text-to-text tasks, such as: Inputs Natural language prompts and questions Outputs Generated text responses Answers to questions Summaries of information Translations between languages Capabilities The CapybaraHermes-2.5-Mistral-7B model has demonstrated strong performance on multi-turn conversation benchmarks, indicating its ability to engage in coherent and contextual dialogue. The model can be used for tasks such as open-ended conversation, question answering, summarization, and more. What can I use it for? The CapybaraHermes-2.5-Mistral-7B model can be used in a variety of applications that require natural language processing and generation, such as: Chatbots and virtual assistants Content generation for blogs, articles, or social media Summarization of long-form text Question answering systems Prototyping and testing of conversational AI applications Argilla, the maintainer of the model, has also published quantized versions of the model for efficient inference, such as the CapybaraHermes-2.5-Mistral-7B-GGUF model from TheBloke. Things to try One interesting aspect of the CapybaraHermes-2.5-Mistral-7B model is its improved performance on multi-turn conversation benchmarks compared to the base OpenHermes-2.5 model. This suggests that the model may be particularly well-suited for tasks that require maintaining context and coherence across multiple exchanges, such as open-ended conversations or interactive question-answering. Developers and researchers may want to experiment with using the model in chatbot or virtual assistant applications, where the ability to engage in natural, contextual dialogue is crucial. Additionally, the model's strong performance on benchmarks like TruthfulQA and AGIEval indicates that it may be a good choice for applications that require factual, trustworthy responses.

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Updated 5/17/2024