Qwen3-4B-Structured-Conversion-LoRA-v2 – AI Model by AshleyQu0311 | AlphaNeural AI

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AshleyQu0311/
Qwen3-4B-Structured-Conversion-LoRA-v2

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structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v5

daichira/structured-5k-mix-sft

daichira/structured-hard-sft-4k

Qwen/Qwen3-4B-Instruct-2507

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Qwen3-4B-Structured-Conversion-LoRA-v2

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. Chain-of-Thought reasoning is masked during training.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 1024
Epochs: 2
Learning rate: 1e-05
LoRA: r=64, alpha=128

Training Data

This model was trained on a balanced mixture of:

https://huggingface.co/datasets/u-10bei/structured_data_with_cot_dataset_512_v5
https://huggingface.co/datasets/daichira/structured-5k-mix-sft
https://huggingface.co/datasets/daichira/structured-hard-sft-4k

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "AshleyQu0311/Qwen3-4B-Structured-Conversion-LoRA-v2"

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

License and Terms

Users must comply with:

The base model license
The license of each dataset used (MIT)

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.