n4/Qwen3-4B-Instruct-2507-sft_166
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Feb 23, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The n4/Qwen3-4B-Instruct-2507-sft_166 is a 4 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507. This model specializes in generating accurate structured outputs, including JSON, YAML, XML, TOML, and CSV formats. It was developed by n4 through a multi-stage SFT process, focusing on improving reliability for data serialization tasks.
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Model Overview
This model, n4/Qwen3-4B-Instruct-2507-sft_166, is a 4 billion parameter instruction-tuned language model derived from the Qwen/Qwen3-4B-Instruct-2507 base. It has been specifically fine-tuned using a multi-stage Supervised Fine-Tuning (SFT) process to significantly enhance its structured output accuracy across various formats.
Key Capabilities
- Specialized Structured Output: Excels at generating data in JSON, YAML, XML, TOML, and CSV formats.
- Multi-stage SFT: Training involved distinct stages, with dedicated focus on YAML and XML generation, using a maximum sequence length of 512 tokens.
- Merged LoRA Weights: The model incorporates merged LoRA adapters, providing a full, ready-to-use fine-tuned model.
Good For
- API Development: Generating structured responses for API endpoints.
- Configuration Management: Creating configuration files in YAML or TOML.
- Data Serialization: Tasks requiring reliable output in common structured data formats.
- Automated Data Processing: Use cases where precise data formatting is critical for downstream systems.
Limitations
- May encounter difficulties with extremely long, deeply nested, or ambiguous schemas.
- Users should implement validation (e.g., JSON parsing, XML validation) for generated outputs in production environments.