Omokemi/hop-1-gemma-270m
Omokemi/hop-1-gemma-270m is a 0.3 billion parameter Gemma-3-270M-it fine-tune by Omokemi, specifically designed for the first step of multi-hop question answering. It outputs the initial entity or fact to look up, not the final answer, in a YAML format with 'thought', 'action', and 'target_entity' fields. This model excels at decomposing complex questions into their first reasoning step, demonstrating significantly improved target correctness and format adherence compared to its untrained base model, even with a concise system prompt.
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Omokemi/hop-1-gemma-270m: Multi-Hop Question Answering Specialist
Omokemi/hop-1-gemma-270m is a specialized 0.3 billion parameter model, fine-tuned from Google's Gemma-3-270M-it. Its unique purpose is to act as a "hop-1" specialist for multi-hop question answering, meaning it identifies the first piece of information or entity required to answer a complex question, rather than providing the final answer. This model outputs its reasoning in a structured YAML format, including thought, action, and target_entity fields.
Key Capabilities
- First-Hop Decomposition: Accurately identifies the initial lookup step for multi-hop questions.
- Structured Output: Generates YAML output with specific fields for clear, machine-readable reasoning.
- Small Footprint: At 270 million parameters, it offers a highly efficient solution for this specific task.
- Offline Operation: Functions without a network connection, ensuring data privacy and reliability.
- Prompt-Efficient: Achieves high performance with a concise system prompt, unlike larger models that require extensive few-shot prompting for similar tasks.
Good For
- Building Multi-Hop QA Systems: Serves as a crucial initial component in a larger question-answering pipeline.
- Resource-Constrained Environments: Ideal for applications where computational resources or network access are limited.
- Privacy-Sensitive Applications: Processes data locally without external API calls.
- Specialized Reasoning Tasks: Demonstrates that small models can be highly effective when fine-tuned for a narrow, well-defined task, outperforming untrained larger models in specific metrics.