Model Overview

This model, curio184/qwen3-4b-struct-exp77, is a 4 billion parameter language model derived from Qwen/Qwen3-4B-Instruct-2507. It has been fine-tuned using QLoRA (4-bit, Unsloth) to significantly improve its ability to generate accurate structured data.

Key Capabilities

• Enhanced Structured Output: Specifically trained to produce precise JSON, YAML, XML, TOML, and CSV formats.
• Full Merged Weights: Distributed with full 16-bit merged weights, simplifying deployment as no adapter loading is required.
• Efficient Fine-tuning: Utilizes QLoRA for efficient training on a specialized dataset.

Training Details

The model was fine-tuned on the u-10bei/structured_data_with_cot_dataset_512_v2 dataset over 3 epochs. Key training parameters included a maximum sequence length of 512, a learning rate of 1e-06, and LoRA configuration with r=128 and alpha=256.

Ideal Use Cases

This model is particularly well-suited for applications where reliable and accurate structured data generation is critical, such as:

• API response generation
• Configuration file creation
• Data serialization tasks
• Automated report generation in structured formats