This is a collection of guides and examples for Google Gemma. Gemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models.
Gemma is a family of lightweight, state-of-the art open models built from the same research and technology used to create the Gemini models. The Gemma model family includes:
- base Gemma
- Gemma variants
You can find the Gemma models on GitHub, Hugging Face models, Kaggle, Google Cloud Vertex AI Model Garden, and ai.nvidia.com.
| Company | Description |
|---|---|
| Hugging Face | Utilize Hugging Face Transformers and TRL for fine-tuning and inference tasks with Gemma models. |
| NVIDIA | Fine-tune Gemma models with NVIDIA NeMo Framework and export to TensorRT-LLM for production. |
| LangChain | This tutorial shows you how to get started with Gemma and LangChain, running in Google Cloud or in your Colab environment. |
| MongoDB | This article presents how to leverage Gemma as the foundation model in a retrieval-augmented generation pipeline or system. |
| Notebook | Description |
|---|---|
| Workshop_How_to_Fine_tuning_Gemma.ipynb | Recommended finetuning notebook for getting started |
| Self_extend_Gemma.ipynb | Self-extend context window for Gemma in the I/O 2024 Keras talk |
| Gemma_control_vectors.ipynb | Implement control vectors with Gemma in the I/O 2024 Keras talk |
| Folder |
|---|
| Business email assistant |
| Personal code assistant |
| Spoken language tasks |
| Gemma model overview | |
|---|---|
| Common_use_cases.ipynb | Illustrate some common use cases for Gemma, CodeGemma and PaliGemma. |
| Inference and serving | |
|---|---|
| Keras_Gemma_2_Quickstart.ipynb | Gemma 2 pre-trained 9B model quickstart tutorial with Keras. |
| Keras_Gemma_2_Quickstart_Chat.ipynb | Gemma 2 instruction-tuned 9B model quickstart tutorial with Keras. Referenced in this blog. |
| Chat_and_distributed_pirate_tuning.ipynb | Chat with Gemma 7B and finetune it so that it generates responses in pirates' tone. |
| gemma_inference_on_tpu.ipynb | Basic inference of Gemma with JAX/Flax on TPU. |
| gemma_data_parallel_inference_in_jax_tpu.ipynb | Parallel inference of Gemma with JAX/Flax on TPU. |
| Gemma_Basics_with_HF.ipynb | Load, run, finetune and deploy Gemma using Hugging Face. |
| Gemma_with_Langfun_and_LlamaCpp.ipynb | Leverage Langfun to seamlessly integrate natural language with programming using Gemma 2 and LlamaCpp. |
| Gemma_with_Langfun_and_LlamaCpp_Python_Bindings.ipynb | Leverage Langfun for smooth language-program interaction with Gemma 2 and llama-cpp-python. |
| Guess_the_word.ipynb | Play a word guessing game with Gemma using Keras. |
| Game_Design_Brainstorming.ipynb | Use Gemma to brainstorm ideas during game design using Keras. |
| Translator_of_Old_Korean_Literature.ipynb | Use Gemma to translate old Korean literature using Keras. |
| Gemma2_on_Groq.ipynb | Leverage the free Gemma 2 9B IT model hosted on Groq (super fast speed). |
| Run_with_Ollama.ipynb | Run Gemma models using Ollama. |
| Using_Gemma_with_Llamafile.ipynb | Run Gemma models using Llamafile. |
| Using_Gemma_with_LlamaCpp.ipynb | Run Gemma models using LlamaCpp. |
| Using_Gemma_with_LocalGemma.ipynb | Run Gemma models using Local Gemma. |
| Using_Gemini_and_Gemma_with_RouteLLM.ipynb | Route Gemma and Gemini models using RouteLLM. |
| Using_Gemma_with_SGLang.ipynb | Run Gemma models using SGLang. |
| Constrained_generation_with_Gemma.ipynb | Constrained generation with Gemma models using LlamaCpp and Guidance. |
| Integrate_with_Mesop.ipynb | Integrate Gemma with Google Mesop. |
| Integrate_with_OneTwo.ipynb | Integrate Gemma with Google OneTwo. |
| Deploy_with_vLLM.ipynb | Deploy a Gemma model using vLLM. |
| Deploy_Gemma_in_Vertex_AI.ipynb | Deploy a Gemma model using Vertex AI. |
| Prompting | |
| Prompt_chaining.ipynb | Illustrate prompt chaining and iterative generation with Gemma. |
| LangChain_chaining.ipynb | Illustrate LangChain chaining with Gemma. |
| Advanced_Prompting_Techniques.ipynb | Illustrate advanced prompting techniques with Gemma. |
| RAG | |
| RAG_with_ChromaDB.ipynb | Build a Retrieval Augmented Generation (RAG) system with Gemma using ChromaDB and Hugging Face. |
| Minimal_RAG.ipynb | Minimal example of building a RAG system with Gemma using Google UniSim and Hugging Face. |
| RAG_PDF_Search_in_multiple_documents_on_Colab.ipynb | RAG PDF Search in multiple documents using Gemma 2 2B on Google Colab. |
| Using_Gemma_with_LangChain.ipynb | Examples to demonstrate using Gemma with LangChain. |
| Using_Gemma_with_Elasticsearch_and_LangChain.ipynb | Example to demonstrate using Gemma with Elasticsearch, Ollama and LangChain. |
| Gemma_with_Firebase_Genkit_and_Ollama.ipynb | Example to demonstrate using Gemma with Firebase Genkit and Ollama |
| Gemma_RAG_LlamaIndex.ipynb | RAG example with LlamaIndex using Gemma. |
| Finetuning | |
| Finetune_with_Axolotl.ipynb | Finetune Gemma using Axolotl. |
| Finetune_with_XTuner.ipynb | Finetune Gemma using XTuner. |
| Finetune_with_LLaMA_Factory.ipynb | Finetune Gemma using LLaMA-Factory. |
| Finetune_with_Torch_XLA.ipynb | Finetune Gemma using PyTorch/XLA. |
| Custom_Vocabulary.ipynb | Demonstrate how to use a custom vocabulary "<unused[0-98]>" tokens in Gemma. |
| Alignment | |
| Aligning_DPO_Gemma_2b_it.ipynb | Demonstrate how to align a Gemma model using DPO (Direct Preference Optimization) with Hugging Face TRL. |
| Evaluation | |
| Gemma_evaluation.ipynb | Demonstrate how to use Eleuther AI's LM evaluation harness to perform model evaluation on Gemma. |
| Mobile | |
| Gemma on Android | Android app to deploy fine-tuned Gemma-2B-it model using MediaPipe LLM Inference API. |
| Inference | |
|---|---|
| Image_captioning_using_PaliGemma.ipynb | Use PaliGemma to generate image captions using Keras. |
| Image_captioning_using_finetuned_PaliGemma.ipynb | Compare the image captioning results using different PaliGemma versions with Hugging Face. |
| Finetune_PaliGemma_for_image_description.ipynb | Finetune PaliGemma for image description using JAX. |
| Integrate_PaliGemma_with_Mesop.ipynb | Integrate PaliGemma with Google Mesop. |
| Zero_shot_object_detection_in_images_using_PaliGemma.ipynb | Zero-shot Object Detection in images using PaliGemma. |
| Zero_shot_object_detection_in_videos_using_PaliGemma.ipynb | Zero-shot Object Detection in videos using PaliGemma. |
| Referring_expression_segmentation_in_images_using_PaliGemma.ipynb | Referring Expression Segmentation in images using PaliGemma. |
| Referring_expression_segmentation_in_videos_using_PaliGemma.ipynb | Referring Expression Segmentation in videos using PaliGemma. |
| Finetuning | |
| Finetune_PaliGemma_with_Keras.ipynb | Finetune PaliGemma with Keras. |
| Mobile | |
| PaliGemma on Android | Inference PaliGemma on Android using Hugging Face and Gradio Client API for tasks like zero-shot object detection, image captioning, and visual question-answering. |
| Finetuning | |
|---|---|
| CodeGemma_finetuned_on_SQL_with_HF.ipynb | Fine-Tuning CodeGemma on the SQL Spider Dataset. |
Ask a Gemma cookbook-related question on the developer forum, or open an issue on GitHub.
If you want to see additional cookbooks implemented for specific features/integrations, please send us a pull request by adding your feature request(s) in the wish list.
If you want to make contributions to the Gemma Cookbook project, you are welcome to pick any idea in the wish list and implement it.
Contributions are always welcome. Please read contributing before implementation.
Thank you for developing with Gemma! We’re excited to see what you create.
