neural-chat-7b-v3-3
Maintainer: Intel
71
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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 neural-chat-7b-v3-3 model is a fine-tuned 7B parameter large language model (LLM) from Intel. It was trained on the meta-math/MetaMathQA dataset and aligned using the Direct Performance Optimization (DPO) method with the Intel/orca_dpo_pairs dataset. The model was originally fine-tuned from the mistralai/Mistral-7B-v0.1 model. This model achieves state-of-the-art performance compared to similar 7B parameter models on various language tasks.
Model inputs and outputs
The neural-chat-7b-v3-3 model is a text-to-text transformer model that takes natural language text as input and generates natural language text as output. It can be used for a variety of language-related tasks such as question answering, dialogue, and summarization.
Inputs
- Natural language text prompts
Outputs
- Generated natural language text
Capabilities
The neural-chat-7b-v3-3 model demonstrates impressive performance on a wide range of language tasks, including question answering, dialogue, and summarization. It outperforms many similar-sized models on benchmarks such as the Open LLM Leaderboard, showcasing its strong capabilities in natural language understanding and generation.
What can I use it for?
The neural-chat-7b-v3-3 model can be used for a variety of language-related applications, such as building conversational AI assistants, generating helpful responses to user queries, summarizing long-form text, and more. Due to its strong performance on benchmarks, it could be a good starting point for developers looking to build high-quality language models for their projects.
Things to try
One interesting aspect of the neural-chat-7b-v3-3 model is its ability to handle long-form inputs and outputs, thanks to its 8192 token context length. This makes it well-suited for tasks that require reasoning over longer sequences, such as question answering or dialogue. You could try using the model to engage in extended conversations and see how it performs on tasks that require maintaining context over multiple turns.
Additionally, the model's strong performance on mathematical reasoning tasks, as demonstrated by its results on the MetaMathQA dataset, suggests that it could be a useful tool for building applications that involve solving complex math problems. You could experiment with prompting the model to solve math-related tasks and see how it performs.
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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