CapybaraHermes-2.5-Mistral-7B
Maintainer: argilla
60
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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