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license: mit |
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# SciPhi-SearchAgent-Alpha-7B Model Card |
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The SciPhi-SearchAgent-Alpha-7B is a Large Language Model (LLM) fine-tuned from Mistral-7B-v0.1. This model underwent a fine-tuning process using retrieval-augmented generation (RAG) over search with a fully synthetic dataset. The objective of this work is to generate accurate and well-cited summaries from a range of search results, providing more accurate answers to user queries. For best results, follow the prompting guidelines below. |
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SciPhi-AI is available via a free hosted API, though the exposed model can vary. Currently, SciPhi-SearchAgent-Alpha-7B is available. More details can be found in the docs [here](https://sciphi.readthedocs.io/en/latest/setup/quickstart.html). |
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## Model Architecture |
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Base Model: Mistral-7B-v0.1 |
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**Architecture Features:** |
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- Transformer-based model |
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- Grouped-Query Attention |
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- Sliding-Window Attention |
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- Byte-fallback BPE tokenizer |
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## Using the Model |
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It is recommended to use a single search query. The model will return an answer using search results as context. |
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In order to use the model, you can go to the website https://search.sciphi.ai/, or you can run it locally using the following simple command: |
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``` |
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export SCIPHI_API_KEY=MY_SCIPHI_API_KEY |
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# Use the SciPhi `SearchAgent` for LLM RAG w/ AgentSearch |
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python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?" |
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``` |
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See the documentation, linked above, for more information. |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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## References |
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1. Lian, W., Goodson, B., Wang, G., Pentland, E., Cook, A., Vong, C., & Teknium. (2023). MistralOrca: Mistral-7B Model Instruct-tuned on Filtered OpenOrcaV1 GPT-4 Dataset. *HuggingFace repository*. [Link](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) |
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2. Mukherjee, S., Mitra, A., Jawahar, G., Agarwal, S., Palangi, H., & Awadallah, A. (2023). Orca: Progressive Learning from Complex Explanation Traces of GPT-4. *arXiv preprint arXiv:2306.02707*. |
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3. Longpre, S., Hou, L., Vu, T., Webson, A., Chung, H. W., Tay, Y., Zhou, D., Le, Q. V., Zoph, B., Wei, J., & Roberts, A. (2023). The Flan Collection: Designing Data and Methods for Effective Instruction Tuning. *arXiv preprint arXiv:2301.13688*. |
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4. Mistral AI. (2023). Model Card for Mistral-7B-v0.1. The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks tested. For full details, please refer to the paper and release blog post. Model Architecture: Transformer with Grouped-Query Attention, Sliding-Window Attention, and Byte-fallback BPE tokenizer. [Link](https://huggingface.co/mistralai/Mistral-7B-v0.1) |
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## Acknowledgements |
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Thank you to the [AI Alignment Lab](https://huggingface.co/Alignment-Lab-AI), [vikp](https://huggingface.co/vikp), [jph00](https://huggingface.co/jph00) and others who contributed to this work. |
