SciPhi-Self-RAG-Mistral-7B-32k
Maintainer: SciPhi
83
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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
SciPhi-Self-RAG-Mistral-7B-32k is a Large Language Model (LLM) fine-tuned from the Mistral-7B-v0.1 model. It underwent further fine-tuning on the recently released self-rag dataset, as well as other RAG-related instruct datasets, in an effort to improve its conversational abilities. The model benchmarks well, but requires additional tuning to become an excellent conversationalist.
Similar models include the Sensei-7B-V1, which is also fine-tuned from the Mistral-7B base model but specializes in retrieval-augmented generation (RAG) over detailed web search results.
Model inputs and outputs
Inputs
- Conversation: The model accepts a list of messages in the format
{"role": "system|user|assistant", "content": "message text"}, where the "system" message provides additional instructions for the assistant.
Outputs
- Response: The model generates a response text, which can be a continuation of the conversation.
Capabilities
The SciPhi-Self-RAG-Mistral-7B-32k model is capable of engaging in open-ended conversations and leveraging search results to provide more accurate and well-cited responses to user queries. It has been fine-tuned to specialize in using search, such as AgentSearch, to generate summaries from a range of search results.
What can I use it for?
You can use the SciPhi-Self-RAG-Mistral-7B-32k model for a variety of natural language processing tasks, such as open-ended conversation, question-answering, and retrieval-augmented generation. The model could be particularly useful for applications that require accurate and well-cited responses, such as customer service chatbots, virtual assistants, or knowledge management systems.
Things to try
One interesting thing to try with the SciPhi-Self-RAG-Mistral-7B-32k model is to provide it with specific search queries and observe how it leverages the search results to generate responses. You can also experiment with different prompting techniques, such as providing the model with additional context or instructions, to see how it affects the quality and coherence of the generated 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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