Welcome to your ultimate guide on utilizing the Sensei-7B-V1 model! This advanced Large Language Model (LLM), fine-tuned from the mistral-ft-optimized-1218 base, specializes in retrieval-augmented generation (RAG). Whether you’re a novice or experienced in the world of AI, this article will help you grasp the essentials of using Sensei-7B-V1 for generating accurate responses from detailed web search results.

What is Sensei-7B-V1?

Sensei-7B-V1 has been meticulously designed for generating summaries based on a query and related search results using a fully synthetic dataset. By leveraging the search capabilities of tools like AgentSearch, this model delivers well-cited summaries and answers, enhancing your ability to extract meaningful information from various sources.

Model Architecture

Let’s break down the critical architecture that powers Sensei-7B-V1:

• Base Model: mistral-ft-optimized-1218
• Architecture Features:
  • Transformer-based model
  • Grouped-Query Attention
  • Sliding-Window Attention
  • Byte-fallback BPE tokenizer

Using the Model

To effectively use Sensei, follow these simple steps.

1. Set your API key to access the SCIPHI API.
2. Craft a single, clear search query. Highlight what you’re looking for.
3. Use the following command to generate an answer:

export SCIPHI_API_KEY=MY_SCIPHI_API_KEY
# Use Sensei for LLM RAG with AgentSearch
python -m agent_search.scripts.run_rag run --query="What is Fermat's last theorem?"

Understanding the Code: An Analogy

Imagine you are at a restaurant and you want to order a specific dish. The SCIPHI_API_KEY acts like your reservation; it gives you access to dine at this exclusive place. The python -m agent_search.scripts.run_rag command is like waving your hand to signal the waiter, letting them know you’re ready to place your order. The run –query= portion is your specific order, asking the waiter to bring you the dish that answers your question, in this case about Fermat’s last theorem.

Formatting the Context Manually

Alternatively, you can provide your own search context directly to the model. Ensure you follow this structured format:

### Instruction:
Your task is to perform retrieval augmented generation (RAG) over the given query and search results. Return your answer in a json format that includes a summary of the search results and a list of related queries.

Query: {Your prompt}
nnSearch Results:{Your context}
nnQuery: {Your prompt}

### Response:
summary:

Note: The inclusion of the leading text summary: following the Response footer is critical for returning properly formatted JSON. Skipping this could lead to unexpected formatting issues.

Troubleshooting Tips

While working with the Sensei-7B-V1 model, you might encounter some challenges. Here are some troubleshooting ideas:

• Issue with API Key: Make sure you have set your SCIPHI_API_KEY correctly. A common hiccup is a typographical error.
• Formatting Errors: Double-check that your input follows the required structure, especially the presence of the leading summary: prefix.
• Model Not Responding: Ensure that the SCIPHI API is not down by checking the service status.
• Data Retrieval Issues: If search results are inaccurate, provide clearer or more specific queries to improve the output.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By now, you should have a comprehensive understanding of how to utilize the Sensei-7B-V1 model effectively. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

References

If you wish to dive deeper into the specifics of Sensei-7B-V1, you can refer to the following resources:

• Mistral-FT-Optimized-1218 Model Card by OpenPipe AI