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

base = "unsloth/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

Modifications: This dataset was created by cleaning the original v5 dataset.

Removed all Chain-of-Thought (CoT) reasoning.
Removed conversational fillers (e.g., "Here is the JSON...").
Extracted only the pure JSON output for assistant responses.

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.

EriUmezawa/qwen3-4b-structeval-custom

Structured Output Tuned Qwen3-4B (LoRA)_c1

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

EriUmezawa/
qwen3-4b-structeval-custom