Find More Examples on the Optimum-TPU GitHub Repository

To find the latest examples, visit the examples folder in the optimum-tpu repo on github

Text Generation

Learn how to perform efficient inference for text generation tasks:

• Basic Generation Script (examples/text-generation/generation.py)
  • Demonstrates text generation using models like Gemma and Mistral
  • Features greedy sampling implementation
  • Shows how to use static caching for improved performance
  • Includes performance measurement and timing analysis
  • Supports custom model loading and configuration

Language Model Fine-tuning

Explore how to fine-tune language models on TPU infrastructure:

1. Interactive Gemma Tutorial (view in the docs)
   • Complete notebook showing Gemma fine-tuning process
   • Covers environment setup and TPU configuration
   • Demonstrates FSDPv2 integration for efficient model sharding
   • Includes dataset preparation and PEFT/LoRA implementation
   • Provides step-by-step training workflow

The full notebook is available at examples/language-modeling/gemma_tuning.ipynb

2. LLaMA Fine-tuning Guide (view in the docs)
   • Detailed guide for fine-tuning LLaMA-2 and LLaMA-3 models
   • Explains SPMD and FSDP concepts
   • Shows how to implement efficient data parallel training
   • Includes practical code examples and prerequisites

The full notebook is available at examples/language-modeling/llama_tuning.ipynb

Additional Resources

• Visit the Optimum-TPU GitHub repository for more details
• Explore the Google Cloud TPU documentation for deeper understanding of TPU architecture

To contribute to these examples, visit our GitHub repository.