JeongMinMin/Qwen3-0.6B-JSON-SFT-GRPO
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.8BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 4, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
JeongMinMin/Qwen3-0.6B-JSON-SFT-GRPO is a 0.8 billion parameter Qwen3 model developed by JeongMinMin. This model is specifically fine-tuned for JSON instruction following, building upon NotoriousH2/Qwen3-0.6B-JSON-SFT. It was trained using Unsloth and Huggingface's TRL library, enabling faster training. This model is optimized for tasks requiring structured JSON output.
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Model Overview
JeongMinMin/Qwen3-0.6B-JSON-SFT-GRPO is a compact 0.8 billion parameter language model based on the Qwen3 architecture. Developed by JeongMinMin, this model is a fine-tuned variant of NotoriousH2/Qwen3-0.6B-JSON-SFT, specifically optimized for generating structured JSON outputs.
Key Characteristics
- Architecture: Qwen3 base model.
- Parameter Count: 0.8 billion parameters.
- Fine-tuning Focus: Specialized for JSON instruction following, making it suitable for applications requiring structured data generation.
- Training Efficiency: The model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Use Cases
This model is particularly well-suited for:
- Structured Data Extraction: Generating JSON from unstructured text inputs.
- API Interaction: Creating JSON payloads or parsing JSON responses for API calls.
- Configuration Generation: Producing configuration files in JSON format.
- Any application requiring reliable JSON output based on instructions.