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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "hara-CU/Qwenqwen3-4b-lora-Data11cleaned-R32A32D005e5"

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: hara-CU/structured_data_with_cot_dataset_512_v2_cleaned2

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.

hara-CU/Qwenqwen3-4b-lora-Data11cleaned-R32A32D005e5-Main-Final0208

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

hara-CU/
Qwenqwen3-4b-lora-Data11cleaned-R32A32D005e5-Main-Final0208