SciPhi-Mistral-7B-32k
Maintainer: SciPhi
68
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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 SciPhi-Mistral-7B-32k is a Large Language Model (LLM) fine-tuned from the Mistral-7B-v0.1 model. This model underwent a fine-tuning process over four epochs using more than 1 billion tokens, which include regular instruction tuning data and synthetic textbooks. The objective of this work was to increase the model's scientific reasoning and educational abilities.
Similar models include the SciPhi-Self-RAG-Mistral-7B-32k, which was further fine-tuned on the self-rag dataset, and the Sensei-7B-V1 which specializes in retrieval-augmented generation (RAG) over detailed web search results.
Model inputs and outputs
The SciPhi-Mistral-7B-32k is a text-to-text model that can take in a variety of prompts and generate relevant responses. For best results, it is recommended to follow the Alpaca prompting guidelines.
Inputs
- Prompts: Natural language instructions or questions that the model should respond to.
Outputs
- Text responses: The model will generate relevant text responses based on the input prompt.
Capabilities
The SciPhi-Mistral-7B-32k model has been trained to excel at scientific reasoning and educational tasks. It can provide informative and well-cited responses to questions on a wide range of scientific topics. The model also demonstrates strong language understanding and generation capabilities, allowing it to engage in natural conversations.
What can I use it for?
The SciPhi-Mistral-7B-32k model can be utilized in a variety of applications that require scientific knowledge or educational capabilities. This could include:
- Developing interactive educational tools or virtual assistants
- Generating summaries or explanations of complex scientific concepts
- Answering questions and providing information on scientific topics
- Assisting with research and literature review tasks
Things to try
One interesting aspect of the SciPhi-Mistral-7B-32k model is its ability to provide well-cited responses. By following the Alpaca prompting guidelines, you can prompt the model to generate responses that incorporate relevant information from the provided context. This can be useful for tasks that require factual accuracy and transparency, such as research assistance or explainable AI applications.
Another interesting feature is the model's potential for conversational abilities. By framing prompts as natural language dialogues, you can explore the model's ability to engage in coherent and contextual exchanges, potentially uncovering new use cases or areas for further development.
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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