qwen3-4b-structevalt-lora-nobuta-v4

qwen3-4b-structevalt-lora-nobuta-v3-run4

qwen3-4b-structevalt-lora-nobuta-v3-run3

qwen3-4b-structevalt-lora-nobuta-v4

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, while intermediate reasoning (Chain-of-Thought) is masked.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 512
• Epochs: 1
• Learning rate: 1e-06
• LoRA: r=64, alpha=128

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "NobutaMN/qwen3-4b-structevalt-lora-nobuta-v4"

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

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.

Reproducibility Notes

• Run: run3
• Tag: v3
• Environment: Google Colab (GPU varies: T4/L4/A100).
• Seed: 3407
• Batch size: per_device_train_batch_size=2, gradient_accumulation_steps=8 (effective batch size=16)
• Precision: fp16 or bf16 (auto; depends on GPU support)
• Sequence length: 512
• Masking: assistant-only loss; CoT masked by learning only after output markers (Output:, Final:, Answer:, Result:, Response:).