tarif2108/gemma-3-270m-json-extractor
tarif2108/gemma-3-270m-json-extractor is a 0.3 billion parameter Gemma-3-270m-it model fine-tuned by tarif2108 for strict, raw JSON output and structured function-calling extraction. This model significantly improves JSON validity and inference speed, achieving a 6x reduction in latency and a 4x increase in valid JSON formatting accuracy compared to its base model. It is optimized for generating clean JSON responses without conversational filler, making it ideal for applications requiring reliable structured data extraction.
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Overview
tarif2108/gemma-3-270m-json-extractor is a specialized fine-tuned version of Google's Gemma-3-270m-it model, specifically optimized for generating strict, raw JSON output and extracting structured function calls. Despite its compact size of approximately 270 million parameters, this model demonstrates substantial improvements over its base model.
Key Capabilities & Performance
- Enhanced JSON Validity: Achieves a 90.0% JSON Validity Rate, a 291% improvement over the base model's 23.0%.
- Faster Inference: Delivers a 6x reduction in average inference latency, completing tasks in 1.92 seconds compared to 11.54 seconds for the base model.
- Structured Extraction: Shows significant gains in Exact Schema Match (21.0%) and Key Coverage Rate (59.0%), indicating better adherence to desired JSON structures.
- Clean Output: Eliminates conversational filler, directly generating JSON and cleanly emitting the
<eos>token upon completion.
Training Details
- Base Model:
google/gemma-3-270m-it - Dataset:
NousResearch/hermes-function-calling-v1(approx. 11,500 JSON rows) - Technique: QLoRA (4-bit NF4 quantization) on an NVIDIA RTX 3050.
Good for
- Applications requiring reliable and fast JSON data extraction.
- Function calling scenarios where strict JSON adherence is critical.
- Edge or resource-constrained environments due to its small parameter count and high efficiency.