khaimaitien/qa-expert-7B-V1.0
The khaimaitien/qa-expert-7B-V1.0 model is a 7 billion parameter language model developed by khaimaitien, fine-tuned from mistralai/Mistral-7B-v0.1. It specializes in multi-hop question answering by decomposing complex questions into single-hop queries and synthesizing information. This model is optimized for accurate information retrieval and summarization in complex Q&A scenarios, offering a focused solution for intricate question answering tasks.
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Overview
khaimaitien/qa-expert-7B-V1.0 is a 7 billion parameter model, fine-tuned from the mistralai/Mistral-7B-v0.1 architecture. Its primary purpose is to address Multi-hop Question Answering tasks effectively. The model achieves this by breaking down multi-hop questions into a sequence of simpler, single-hop questions, processing each, and then synthesizing the gathered information to formulate a comprehensive final answer.
Key Capabilities
- Multi-hop Question Answering: Designed to handle complex questions requiring information from multiple sources or inference steps.
- Question Decomposition: Splits intricate questions into manageable single-hop queries.
- Information Synthesis: Summarizes and integrates answers from individual queries to provide a final, coherent response.
- Customizable Retrieval: Integrates with a user-defined retrieval function, allowing flexibility in how context is provided.
Training Details
The model was fine-tuned on the khaimaitien/qa-expert-multi-hop-qa-V1.0 dataset, specifically curated for multi-hop question answering tasks.
Usage
Developers can integrate this model by cloning its associated GitHub repository and following the provided Python example for inference. It requires a custom retrieve function to fetch context relevant to the decomposed questions, similar to function calling in other LLM frameworks.