LLM2025_final_202603011837

qwen3-4b-structured-output-lora

This repository provides a LoRA adapter fine-tuned from 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: Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 1024
• Epochs: 2
• Learning rate: 5e-6
• LoRA: r=64, alpha=128
• LoRA dropout: 0.05
• Per-device train batch size: 16
• Gradient accumulation steps: 1
• Warmup ratio: 0.1
• Weight decay: 0.05
• Precision: bf16
• Hardware: NVIDIA DGX Spark (GB10 Grace Blackwell)

Changes from Baseline Notebook

Training Results

• Train loss: 0.8883
• Eval loss: 0.7539
• Total steps: 998
• Training time: ~170 min

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "kazfujita/LLM2025_final_202603011837"

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
• u-10bei/structured_data_with_cot_dataset_512_v4

Dataset License: MIT License. These datasets are 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.