LLM-Competition-2026

This repository provides a LoRA adapter fine-tuned from Qwen3-4B-Instruct-2507 using QLoRA (4-bit).

This repository contains LoRA adapter weights only. The base model must be loaded separately.

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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.

Additionally, this adapter is further optimized using Direct Preference Optimization (DPO) to suppress hallucinated or incorrect structured outputs.

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Training Configuration

SFT Stage

• Base model: Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 512
• Learning rate: 1e-6
• LoRA: r=64, alpha=128

DPO Stage

• Starting adapter: Sakai0920/qwen3-4b-structured-output-lora-v3
• Method: DPO (TRL)
• Beta: 0.2
• Epochs: 1
• Learning rate: 1e-05
• Weight decay: 0.01

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Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "Sakai0920/LLM-Competition-2026"

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

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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.