Overview
Arc53/docsgpt-7b-mistral is a 7 billion parameter language model, fine-tuned from the Zephyr-7B-beta architecture using the LoRA process. Its primary optimization is for Documentation (RAG optimized), focusing on generating answers based on provided context.
Key Capabilities
- Context-Driven Answering: Specifically designed to provide accurate responses by leveraging contextual information, reducing hallucination.
- Strong Performance on BACON Test: Achieves a score of 8.64 on the internal BACON test, which evaluates context understanding, hallucination, and attention span, placing it competitively with larger models like GPT-3.5-turbo.
- Competitive MTbench Scores: Demonstrates solid performance on the MTbench with LLM judge, scoring 7.166875 on average, outperforming its base model Zephyr-7b-beta and other 13B parameter models like Vicuna-13b-v1.3.
- Commercial Use: Licensed under Apache-2.0, allowing for commercial applications.
Good for
- Technical Support: Ideal for systems requiring precise answers from technical documentation.
- Developer Tools: Useful for integrating into developer workflows where accurate information retrieval from documentation is critical.
- RAG Applications: Optimized for Retrieval Augmented Generation (RAG) scenarios, ensuring responses are grounded in provided documents.
Users should format prompts with ### Instruction, ### Context, and ### Answer sections for optimal performance.