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Matsuo2025-Main-Qwen3-4B-SFT-T4-v1 – AI Model by RegFo | AlphaNeural AI

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RegFo/
Matsuo2025-Main-Qwen3-4B-SFT-T4-v1

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structured-output

text-generation

u-10bei/structured_data_with_cot_dataset_512_v2

Qwen/Qwen3-4B-Instruct-2507

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Matsuo2025-Main-Qwen3-4B-SFT-T4-v1

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth) on NVIDIA T4 GPU.

This is a baseline model to ensure reproducibility on Colab Free tier.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 3
Learning rate: 1e-06
LoRA: r=64, alpha=128

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
# ★変更点: T4版のIDを指定
adapter = "RegFo/Matsuo2025-Main-Qwen3-4B-SFT-T4-v1"

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