Arc53/docsgpt-7b-mistral

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.