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license: apache-2.0 |
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# Sensei-7B-v0.1 Model Card |
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Sensei-7B-v0.1 is a Large Language Model (LLM) fine-tuned from Mistral-7B-v0.1. This model was fine tuned with a fully synthetic dataset to specialize at performing retrieval-augmented generation (RAG) over detailed web search results. This model strives to specialize in using search, such as [AgentSearch](https://huggingface.co/datasets/SciPhi/AgentSearch-V1), to generate accurate and well-cited summaries from a range of search results, providing more accurate answers to user queries. Please refer to the [docs here](https://agent-search.readthedocs.io/en/latest/) for more information on how to run Sensei end-to-end. |
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Currently, Sensei is available via hosted api at https://www.sciphi.ai. You can try a demonstration [here](https://search.sciphi.ai/). |
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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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Using the AgentSearch package an example is shown below. |
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``` |
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export SCIPHI_API_KEY=MY_SCIPHI_API_KEY |
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# Use `Sensei` 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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Alternatively, you may provide your own search context directly to the model by adhereing to the following format: |
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``` |
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### Instruction: |
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Your task is to perform retrieval augmented generation (RAG) over the given query and search results. Return your answer with three sections `My Work`, `My Answer`, and `My Further Considerations`. |
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Query: |
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{query} |
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Search Results: |
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{search_results} |
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Query: |
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{query} |
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### Response: |
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``` |
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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. 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) |
