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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "chillro/qwen3-4b-structured-output-lora-ver9-L4"

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,
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,
daichira/structured-5k-mix-sft

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.

chillro/qwen3-4b-struct-lora-ver9-L4-checkpoint-152

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

chillro/
qwen3-4b-struct-lora-ver9-L4-checkpoint-152