Arc53/docsgpt-7b-mistral
Arc53/docsgpt-7b-mistral is a 7 billion parameter language model, fine-tuned from Zephyr-7B-beta using LoRA, specifically optimized for documentation-based question answering. It excels at providing context-driven responses, making it highly suitable for developers and technical support teams. The model demonstrates strong performance in hallucination reduction and attention span on the internal BACON test, outperforming several larger models in this domain.
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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.