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_v5
daichira/structured-3k-mix-sft
daichira/structured-hard-sft-4k

Dataset License:

This dataset contains materials licensed under both the MIT License and the Creative Commons Attribution 4.0 International License (CC BY 4.0).

MIT-licensed portions are distributed under the terms of the MIT License. A copy of the license is included in the distribution.

CC BY 4.0-licensed portions are used in accordance with the Creative Commons Attribution 4.0 International License. Proper attribution to the original authors is provided, and modifications (if any) are indicated. See https://creativecommons.org/licenses/by/4.0/ for details.

Users are responsible for complying with all applicable license terms, including attribution requirements and preservation of license notices, as well as the base model’s original terms of use.

anyai/llm-comp-2025-subject

Training Objective

Improvements over Standard Code

Training Configuration

Usage

Sources & Terms (IMPORTANT)

anyai/
llm-comp-2025-subject