Jhjhugv/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
Jhjhugv/Qwen3-0.6B-JSON-SFT-GRPO is a 0.8 billion parameter Qwen3-based language model developed by Jhjhugv. Fine-tuned from NotoriousH2/Qwen3-0.6B-JSON-SFT, this model is optimized for JSON instruction following tasks. It was trained using Unsloth and Huggingface's TRL library, offering efficient performance for structured data generation.
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Model Overview
Jhjhugv/Qwen3-0.6B-JSON-SFT-GRPO is a 0.8 billion parameter language model based on the Qwen3 architecture, developed by Jhjhugv. It is a fine-tuned version of NotoriousH2/Qwen3-0.6B-JSON-SFT, specifically optimized for tasks requiring structured JSON output.
Key Capabilities
- JSON Instruction Following: The model excels at generating responses in a valid JSON format based on given instructions.
- Efficient Training: It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training times.
- Compact Size: With 0.8 billion parameters, it offers a balance between performance and computational efficiency.
Good For
- Structured Data Generation: Ideal for applications that require outputs in a consistent JSON structure.
- API Response Simulation: Can be used to generate mock API responses or data for testing.
- Data Extraction: Suitable for extracting information from text and formatting it into JSON.
- Resource-Constrained Environments: Its smaller size makes it viable for deployment in environments with limited computational resources.