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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "NobutaMN/qwen3-4b-structevalt-lora-nobuta-v4"

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

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.

Reproducibility Notes

Run: run3
Tag: v3
Environment: Google Colab (GPU varies: T4/L4/A100).
Seed: 3407
Batch size: per_device_train_batch_size=2, gradient_accumulation_steps=8 (effective batch size=16)
Precision: fp16 or bf16 (auto; depends on GPU support)
Sequence length: 512
Masking: assistant-only loss; CoT masked by learning only after output markers (Output:, Final:, Answer:, Result:, Response:).