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)

Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v2 u-10bei/structured_data_with_cot_dataset_512_v4 u-10bei/structured_data_with_cot_dataset_512_v5 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.

nakamuratoshiya/sft-qwen-qlora-adapter-0204

qwen3-4b-structured-output-lora-0204-v2

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

nakamuratoshiya/
sft-qwen-qlora-adapter-0204