TSUTAYA/qwen3-4b-struct-lora-4

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.

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Overview

This adapter is designed to improve structured output reliability across formats such as:

• JSON
• YAML
• XML
• TOML
• CSV

The focus is on increasing formatting stability, bracket correctness, key-value alignment, and structural consistency.

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Training Objective

The model is trained via Supervised Fine-Tuning (SFT) with the following design:

• Loss is applied only to the final assistant output
• Intermediate reasoning (Chain-of-Thought) is masked
• Training emphasizes output formatting precision

This setup prioritizes structured output correctness over verbose reasoning.

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Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 1024
• Epochs: 1
• Learning rate: 2e-05
• LoRA configuration:
  • r = 128
  • alpha = 256

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Usage

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)
model.eval()

Sources & Terms (IMPORTANT)

Training data: daichira/structured-hard-sft-4k

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.