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kusasyu45/
winning-structeval-lora

u-10bei/structured_data_with_cot_dataset_512_v4

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

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winning-structeval-lora

LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth). Contains LoRA adapter weights only. Base model must be loaded separately.

Training Objective

Improve structured output accuracy (JSON / YAML / XML / TOML / CSV). Loss applied only to final assistant output (CoT masked).

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Datasets: u-10bei/structured_data_with_cot_dataset_512_v4, daichira/structured-hard-sft-4k
Max sequence length: 512
Epochs: 3
Learning rate: 2e-05
LoRA: r=64, alpha=128

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "kusasyu45/winning-structeval-lora"

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

Training data: u-10bei/structured_data_with_cot_dataset_512_v4, daichira/structured-hard-sft-4k Dataset License: MIT License. Compliance: Users must comply with MIT license and base model's terms.