This project demonstrates how to fine-tune the Google Gemma 2 2B model to improve its performance on Japanese instruction-following tasks. It utilizes the Hugging Face ecosystem, including transformers, datasets, and trl libraries, to efficiently fine-tune the model using QLoRA (Quantized Low-Rank Adaptation) technique.

• Fine-tuning Google Gemma 2 2B model for Japanese language tasks
• Utilization of QLoRA for efficient fine-tuning
• Dataset preparation and formatting for instruction tuning
• Integration with Hugging Face's transformers and trl libraries
• Model evaluation and inference examples

• PyTorch
• Transformers
• Datasets
• TRL (Transformer Reinforcement Learning)
• Accelerate
• PEFT (Parameter-Efficient Fine-Tuning)
• BitsAndBytes

1. Prepare your dataset:

   • The notebook uses the "Mustain/JapaneseQADataset" from Hugging Face, but you can replace it with your own dataset.
   • Ensure your dataset is in the correct format (conversation or instruction format).

2. Set up your environment:

   • Make sure you have access to a GPU for faster training.
   • Set your Hugging Face token for accessing the Gemma model.

3. Run the notebook:

   • Follow the steps in the notebook to load the model, prepare the dataset, and start the fine-tuning process.

4. Evaluate the model:

   • Use the provided evaluation code to test your fine-tuned model on new Japanese instructions.

• Model: Google Gemma 2 2B
• Fine-tuning Method: QLoRA (Quantized Low-Rank Adaptation)
• Training Framework: TRL's SFTTrainer
• Dataset: Japanese Q&A dataset (customizable)

The notebook demonstrates how the fine-tuned model improves in following Japanese instructions compared to the base model. Specific results may vary based on your dataset and training parameters.

You can easily adapt this notebook for other languages or specific domains by:

• Changing the base model (e.g., to Gemma 2 9B or other models)
• Using a different dataset relevant to your task
• Adjusting hyperparameters in the TrainingArguments and LoraConfig

This project is licensed under the MIT License - see the LICENSE file for details.