Qwen3-4B-Instruct-2507-20260224_T222948

Qwen3-4B-Instruct-2507-20260224_T222948

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: 1024
• Epochs: 2
• Learning rate: 6e-04
• LoRA: r=64, alpha=64
• Lora target modules: down_proj
• Lora dropout: 0.05
• Per device batch size: 8
• Gradient accumulation step: 8

Training data

• Used dataset: u-10bei/structured_data_with_cot_dataset_512_v2
• Treatment of reasoning CoT: Reasoning CoT text was stored inside <think>...</think> tags and included in the training data as-is.

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "CLRafaelR/Qwen3-4B-Instruct-2507-20260224_T222948"

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_v2

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.