Improving Gemma 2B (with Example Colab Code)

• Google J has 4 variants: 2B, 7B, and fine-tuned versions.
• Use T4 GPU for 2B, and P100 for 7B training.
• Libraries required for fine-tuning: Transformers, TRL, PF, and Bytes.
• Install the required libraries, check GPU and token setup.
• Download and load 2B model, understand base vs. instruct model differences.
• Utilize prompt formats provided by Gemma for fine-tuning.
• Load and format data set according to Gemma instruct fine-tuning.
• Set the Lura configurations for fine-tuning parameters.
• Train the model using TRL library and fine-tune for a specified number of steps.
• Save the trained model and merge it back to the base model.
• Push the merged model to Hugging Face Hub for evaluation.

Hope this helps! Feel free to reach out on Twitter if you have any questions.🚀

🎯 This tutorial covers the process of fine-tuning Google’s J model using a free Google Colab notebook. The tutorial walks you through the steps to fine-tune the Gemma model and provides example code.

Setting Up Prerequisites and Environment

🔧 Step 1: Setting up the prerequisites and the proper environment
📦 Ensure the availability of the T4 GPU for the 2 billion parameter model or an 800 GPU for the 7 billion parameter variant in the pro version of Google Colab.

Installing Required Libraries

📚 According to the model to be fine-tuned, installation of necessary libraries for supervised fine-tuning, data loading and manipulation, parameter-efficient fine-tuning, and loading models.

The notebook can be accessed from the LM Alchemy chamber repository and is part of the LM Alchemy chamber repository. Here, users can find notebooks for fine-tuning other models as well.

Loading the Model

📤 Method for loading the model for fine-tuning, understanding the differences between the base model and the instruction fine-tune model, and loading the instruct fine-tune model using 4-bit quantization.

Determining Prompt Format

🛠️ Understanding and selecting the prompt format for the instruct fine-tune model to generate responses.

Loading and Formatting the Dataset

📊 Using a curated premier dataset by Token Blender, which follows the alpaca style, that includes input, output, and instruction columns.

Dataset Formatting

🔄 Step to format the dataset using the same structure as that used for fine-tuning the Gemma instruct model.

Lura Configurations

📈 Understanding and setting the Lura configurations using the P Library to target specific layers of the model, such as linear layers and projection gates.

Now, the fine-tuning process is initiated using the adjusted Lura configurations and the supervised fine-tuning (SFT) trainer provided by the TRL Library. The training data is fed to the SFT trainer, and the number of steps for training and logging are specified.

Merging the Model

🔄 After the fine-tuning, the new model with the merged Lura adapters is created and saved in a separate folder. This model is then pushed to the Hugging Face Hub.

In conclusion, the tutorial provides a comprehensive guide to fine-tuning the Gemma 2B model and offers an example Colab code to demonstrate the process. For additional information or queries, feel free to reach out on Twitter.

Key Takeaways:

• The tutorial provides a step-by-step guide for finetuning the Gemma 2B model on a free Google Colab notebook.
• The process includes setting up prerequisites, loading the model, formatting the dataset, determining prompt format, and configuring Lura settings.
• Code examples are provided at each stage for practical understanding.
• The LM Alchemy chamber repository offers various notebooks for fine-tuning different models.

FAQ:

1. Are the steps in the tutorial applicable to other models?
   Yes, the steps for model loading, dataset formatting, and Lura configurations can be adapted for other models as well.

2. Can the same process be used for models with different parameter sizes?
   The tutorial specifically demonstrates fine-tuning the 2 billion parameter model, but the process can be modified for other parameter sizes as well.