This repository provides a LoRA adapter fine-tuned from unsloth/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: unsloth/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 1
Learning rate: 2e-04
LoRA: r=64, alpha=128
Training data: daichira/structured-5k-mix-sft +

Usage

python

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

base = "unsloth/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 & License (IMPORTANT)

Training Data 1: daichira/structured-5k-mix-sft
Training Data 2:
Dataset License: Please refer to each dataset's license terms.
Base Model License: Apache 2.0 (unsloth/Qwen3-4B-Instruct-2507)
Compliance: Users must comply with both the dataset's attribution requirements and the base model's original terms of use.

takana0229/your-lora-repo5

qwen3-4b-structured-output-lora

Training Objective

Training Configuration

Usage

Sources & License (IMPORTANT)

takana0229/
your-lora-repo5