melon1891/qwen3-4b-structured-output-lora-v3

qwen3-4b-structured-output-lora-v3

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth).

This repository contains LoRA adapter weights only. The base model must be loaded separately.

Training Objective

This adapter is trained to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).

Loss is applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) is masked.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 768
• Epochs: 2
• Learning rate: 2e-05
• LoRA: r=64, alpha=128

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/your-repo"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Training data:

• u-10bei/structured_data_with_cot_dataset_512_v4 (MIT License) - 2,770 samples after deduplication
• daichira/structured-5k-mix-sft (CC-BY-4.0) - 5,000 samples

Preprocessing applied:

• Removed 1,838 exact duplicate samples from dataset 1 (4,608 → 2,770)
• Normalized CoT format ("Output:" marker standardization)
• Combined total: 7,770 samples

Dataset Licenses:

• u-10bei/structured_data_with_cot_dataset_512_v4: MIT License
• daichira/structured-5k-mix-sft: CC-BY-4.0 (attribution required)

Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.