kmd2525/v8_stage1_json_csv-merged
The kmd2525/v8_stage1_json_csv-merged model is a 4 billion parameter instruction-tuned causal language model, based on Qwen/Qwen3-4B-Instruct-2507, specifically fine-tuned for structured data output. This is the first stage in a Sequential Format Learning pipeline, focusing on achieving 100% parse success rates for JSON and CSV formats. It is optimized for developers requiring reliable generation of structured data in these specific formats.
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Overview
The kmd2525/v8_stage1_json_csv-merged model represents Stage 1 of a Sequential Format Learning (v8 strategy) pipeline designed for robust structured data output. This 4 billion parameter model is built upon the Qwen/Qwen3-4B-Instruct-2507 base and has been fine-tuned with a focus on generating accurate JSON and CSV data.
Key Capabilities
- Specialized Structured Output: This model is specifically trained to produce JSON and CSV formats with a high degree of parsing success.
- Sequential Learning Approach: It is the initial step in a multi-stage training strategy, where each stage focuses on a specific data format, and the resulting LoRA is merged into the base model for subsequent stages.
- Targeted Format Training: Stage 1 involved training on 400 JSON samples and 400 CSV samples, aiming for a 100% parse success rate for these two formats.
- Foundation for Further Specialization: This model serves as the base for subsequent stages in the v8 pipeline, which will extend its capabilities to YAML, XML, and mixed formats.
Training Details
- Base Model:
Qwen/Qwen3-4B-Instruct-2507 - Context Length: Trained with a maximum sequence length of 1024 tokens.
- Epochs: 2
- LoRA Configuration: R=64, Alpha=128
Good For
- Applications requiring reliable and parseable JSON output.
- Systems needing accurate CSV data generation.
- Developers looking for a foundational model to build upon for broader structured data generation tasks within the v8 Sequential Format Learning pipeline.