sasa5555/qwen3-4b-structured-output-lora_sft-creandata_merged
The sasa5555/qwen3-4b-structured-output-lora_sft-creandata_merged model is a 4 billion parameter Qwen3-based language model, fine-tuned and merged by sasa5555. It specializes in generating structured outputs, including data format conversions (CSV, JSON, YAML, TOML, XML) and structured data generation. This model is optimized for tasks requiring precise, formatted responses, leveraging its 40960 token context length.
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Model Overview
This model, sasa5555/qwen3-4b-structured-output-lora_sft-creandata_merged, is a 4 billion parameter language model built upon the Qwen/Qwen3-4B-Instruct-2507 base model. It has been enhanced through Supervised Fine-Tuning (SFT) using a LoRA adapter, which was subsequently merged into the base model. This results in a full 16-bit merged model that can be used directly without needing to load separate adapters.
Key Capabilities
- Structured Output Generation: Specifically fine-tuned to excel at producing structured data.
- Data Format Conversion: Proficient in converting data between various formats, including CSV, JSON, YAML, TOML, and XML.
- Structured Data Generation: Capable of generating new data in specified structured formats.
- Chain-of-Thought Reasoning: Incorporates Chain-of-Thought reasoning for improved structured output quality.
Use Cases
This model is particularly well-suited for applications requiring reliable and accurate structured text generation. Developers can leverage it for:
- Automating data transformation workflows.
- Generating configuration files in various formats.
- Creating structured responses from natural language prompts.
- Any task where precise, formatted output is critical.
License
The model operates under the Apache 2.0 license, inheriting terms from its Qwen3 base model.