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)

This model was fine-tuned using a combined dataset consisting of:

Full dataset: u-10bei/structured_data_with_cot_dataset_512_v2 (all samples)
Partial dataset: u-10bei/structured_data_with_cot_dataset_512_v4 (100 samples)

Total training samples: [v2のサンプル数] + 100 = [合計数] samples

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.

Kaito-F/lora-repo-v10

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

Kaito-F/
lora-repo-v10