NeuralHermes-2.5-Mistral-7B
Maintainer: mlabonne
148
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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 NeuralHermes-2.5-Mistral-7B model is a fine-tuned version of the OpenHermes-2.5-Mistral-7B model. It was developed by mlabonne and further trained using Direct Preference Optimization (DPO) on the mlabonne/chatml_dpo_pairs dataset. The model surpasses the original OpenHermes-2.5-Mistral-7B on most benchmarks, ranking as one of the best 7B models on the Open LLM leaderboard.
Model inputs and outputs
The NeuralHermes-2.5-Mistral-7B model is a text-to-text model that can be used for a variety of natural language processing tasks. It accepts text input and generates relevant text output.
Inputs
- Text: The model takes in text-based input, such as prompts, questions, or instructions.
Outputs
- Text: The model generates text-based output, such as responses, answers, or completions.
Capabilities
The NeuralHermes-2.5-Mistral-7B model has demonstrated strong performance on a range of tasks, including instruction following, reasoning, and question answering. It can engage in open-ended conversations, provide creative responses, and assist with tasks like writing, analysis, and code generation.
What can I use it for?
The NeuralHermes-2.5-Mistral-7B model can be useful for a wide range of applications, such as:
- Conversational AI: Develop chatbots and virtual assistants that can engage in natural language interactions.
- Content Generation: Create text-based content, such as articles, stories, or product descriptions.
- Task Assistance: Provide support for tasks like research, analysis, code generation, and problem-solving.
- Educational Applications: Develop interactive learning tools and tutoring systems.
Things to try
One interesting thing to try with the NeuralHermes-2.5-Mistral-7B model is to use the provided quantized models to explore the model's capabilities on different hardware setups. The quantized versions can be deployed on a wider range of devices, making the model more accessible for a variety of use cases.
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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