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: daichira/structured-hard-sft-4k

Dataset License: Creative Commons Attribution (CC-BY-4.0). This dataset is used and distributed under the terms of the CC-BY-4.0 License. Compliance: Users must comply with the CC-BY-4.0 license (including providing proper attribution to the dataset creator) and the base model's original terms of use.

gimlet09/qwen3-4b-structured-output-lora-hard4k-new1

qwen3-4b-structured-output-lora-hard4k-new1

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

gimlet09/
qwen3-4b-structured-output-lora-hard4k-new1