Qwen3 Local Fine-Tuning (Backend-First)

This project demonstrates how to fine-tune the open-source Qwen3 (14B) large language model using your own data, fully locally — with complete backend control.

Unlike most cloud-based platforms, this setup gives you:

• Full access to training configuration
• Efficient parameter fine-tuning with LoRA
• Reproducibility and transparency
• No third-party studios or hidden abstractions

🔧 Tech Stack

• Model: Qwen3 14B from Hugging Face
• Tokenization: Hugging Face Transformers
• Training: PyTorch + HF Trainer
• Adapters: LoRA via PEFT
• Optimization: 4-bit quantization (bitsandbytes)
• Execution: accelerate for hardware scaling

⚙️ Hardware Requirements

For Qwen3-14B:

• Minimum: 1x A100 (40GB) or 2x RTX 3090 (24GB each)
• For smaller setups: use Qwen1.5-7B

🧠 How to Fine-Tune

1. Install requirements

pip install -r requirements.txt

2. Prepare your dataset

Place your dataset in:

data/reasoning_dataset.json

Format:

[
  {"prompt": "Your question here", "response": "Your answer here"},
  ...
]

3. Configure Accelerate

accelerate config

4. Train the model

python src/train.py

🧪 Where to Tweak for Response Style

Inside src/prepare_data.py, we format how the prompt + response pairs are constructed. You can add instructions, system prompts, or wrap your inputs like so:

text = f"### Instruction:\n{example['prompt']}\n\n### Think step by step before answering.\n\n### Response:\n{example['response']}"

📦 Project Structure

qwen3-local-finetune/
├── data/
│   └── reasoning_dataset.json
├── src/
│   ├── load_model.py
│   ├── apply_lora.py
│   ├── prepare_data.py
│   ├── train.py
│   └── config.py
├── README.md
├── requirements.txt
├── accelerate_config.yaml

License

Open-source for educational and research use.