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

base = "unsloth/Qwen3-4B-Instruct-2507"
adapter = "takatuki56/2026-comp-model-v15"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.bfloat16, # Use bfloat16 for L4/A100
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

Sources & Terms (IMPORTANT)

Base model: unsloth/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 4096 (Optimized for L4)
Epochs: 2
Learning rate: 7e-07
LoRA: r=256, alpha=512
Precision: bf16 (Bfloat16)

Training Data (Combined):

u-10bei/structured_data_with_cot_dataset_512_v2

Dataset License: Please refer to the original repositories for specific license terms. Generally, these datasets are used and distributed under terms compliant with their original licenses (e.g., MIT, CC-BY).

Compliance: Users must comply with the licenses of the datasets and the base model's original terms of use.