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HamadaMayu/
qwen3-4b-structured-output-lora-v4-2epoch

Name: qwen3-4b-structured-output-lora-v4-2epoch
Availability: InStock

u-10bei/structured_data_with_cot_dataset_512_v4

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

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qwen3-4b-structured-output-lora-v4-2epoch

This repository provides a LoRA adapter fine-tuned from
Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit quantization) with Hugging Face Transformers and PEFT.

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 during training.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Training framework: Hugging Face Transformers + PEFT
Method: QLoRA (4-bit quantization, NF4)
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-6
LoRA configuration: r=64, alpha=128

Usage

python

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

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.

HamadaMayu/qwen3-4b-structured-output-lora-v4-2epoch

Training Objective

Training Configuration

Usage

HamadaMayu/
qwen3-4b-structured-output-lora-v4-2epoch