yuk1chan/qwen3-4b-structeval-stage0-1-merged
The yuk1chan/qwen3-4b-structeval-stage0-1-merged model is a 4 billion parameter Qwen3 architecture, fine-tuned by yuk1chan, specifically optimized for structured output generation. It combines Stage 0 (YAML/XML boost) and Stage 1 (YAML/XML specialized SFT) LoRA adapters to achieve high accuracy in producing structured data formats. With a context length of 32768 tokens, this model excels at generating JSON, YAML, XML, and CSV outputs.
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Model Overview
This model, yuk1chan/qwen3-4b-structeval-stage0-1-merged, is a 4 billion parameter Qwen3-based instruction-tuned language model. It has been specifically fine-tuned by yuk1chan to enhance its capabilities in generating structured data formats, merging two specialized LoRA adapters: Stage 0 for general YAML/XML boosting and Stage 1 for specialized YAML/XML Supervised Fine-Tuning (SFT).
Key Capabilities
This model demonstrates strong performance in generating various structured outputs:
- JSON: Achieves 100% accuracy on evaluation tasks.
- YAML: Performs with over 99% accuracy.
- XML: Shows over 96% accuracy.
- CSV: Reaches 95% accuracy.
- TOML: Achieves 76% accuracy, with ongoing potential for improvement.
Good For
This model is ideal for applications requiring reliable and accurate generation of structured data. Developers can leverage it for tasks such as:
- Automated API response generation in JSON or XML.
- Configuration file creation in YAML.
- Data export or reporting in CSV format.
Its specialized training makes it a strong candidate for scenarios where precise structured output is critical, offering a significant advantage over general-purpose LLMs for these specific tasks.