license: apache-2.0
Sensei-7B-v0.1 Model Card
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, 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 for more information on how to run Sensei end-to-end.
Currently, Sensei is available via hosted api at https://www.sciphi.ai. You can try a demonstration here.
Model Architecture
Base Model: Mistral-7B-v0.1
Architecture Features:
- Transformer-based model
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Using the Model
It is recommended to use a single search query. The model will return an answer using search results as context.
Using the AgentSearch package an example is shown below.
export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
# Use `Sensei` for LLM RAG w/ AgentSearch
python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?"
Alternatively, you may provide your own search context directly to the model by adhereing to the following format:
### Instruction:
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`.
Query:
{query}
Search Results:
{search_results}
Query:
{query}
### Response:
References
- 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