Mortdecai
eecebe7ef5
Five-lane parallel research pass. Each subdir under tooling/ has its own README indexing downloaded files with verified upstream sources. - google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts, gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev HTML snapshots, Gemma 3 tech report - huggingface/: 8 gemma-4-* model cards, chat-template .jinja files, tokenizer_config.json, transformers gemma4/ source, launch blog posts, official HF Spaces app.py - inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI comparison, run_commands.sh with 8 working launches, 9 code snippets - gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2, Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma) - fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE), TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md Findings that update earlier CORPUS_* docs are flagged in tooling/README.md (not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM, FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech report PDF yet, no Gemma-4-generation specialized siblings yet. Pre-commit secrets hook bypassed per user authorization — flagged "secrets" are base64 notebook cell outputs and example Ed25519 keys in the HDP agentic-security demo, not real credentials. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
81 lines
5.9 KiB
Markdown
81 lines
5.9 KiB
Markdown
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# Welcome to the Gemma Cookbook
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This is a collection of guides and examples for [Google Gemma](https://ai.google.dev/gemma/).
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> **Disclaimer:** Gemma is a family of developer-focused models built by Google DeepMind. This cookbook is a collection of guides and examples for Google Gemma. Please keep in mind that Gemma is an open model and can hallucinate as you build on examples in this cookbook.
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## Repository Structure
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* [**Tutorials**](tutorials/): The latest tested notebooks for Gemma models and variants.
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* [**Apps**](apps/): Full-stack demos and complex end-to-end use cases.
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* [**Experiments**](experiments/): Research-focused model notebooks, including [TxGemma](experiments/TxGemma) and [MedGemma](experiments/MedGemma).
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* [**Responsible**](responsible/): Notebooks for responsible AI development.
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* [**Docs**](docs/): Core documentation, capabilities, and technical guides.
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* [**Archive**](.archive/): All older notebooks and historical examples.
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## Get started with the Gemma models
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Gemma is a family of lightweight, generative artificial intelligence (AI) open models, built from the same research and technology used to create the Gemini models. The Gemma model family includes:
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* Gemma\
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The core models of the Gemma family.
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* [Gemma](https://ai.google.dev/gemma/docs/core/model_card)\
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For a variety of text generation tasks and can be further tuned for specific use cases
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* [Gemma 2](https://ai.google.dev/gemma/docs/core/model_card_2)\
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Higher-performing and more efficient, available in 2B, 9B, 27B parameter sizes
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* [Gemma 3](https://ai.google.dev/gemma/docs/core/model_card_3)\
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Longer context window and handling text and image input, available in 1B, 4B, 12B, and 27B parameter sizes
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* [Gemma 3n](https://ai.google.dev/gemma/docs/gemma-3n/model_card) \
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Designed for efficient execution on low-resource devices. Handling text, image, video, and audio input, available in E2B and E4B parameter sizes
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* [Gemma 4](https://ai.google.dev/gemma/docs/core/model_card_4)\
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Well-suited for reasoning, agentic workflows, coding, and multimodal understanding, available in E2B, E4B, 26B A4B, and 31B parameter sizes.
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* Gemma variants
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* [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)\
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Fine-tuned for a variety of coding tasks
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* [DataGemma](https://ai.google.dev/gemma/docs/datagemma)\
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Fine-tuned for using Data Commons to address AI hallucinations
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* [FunctionGemma](https://ai.google.dev/gemma/docs/functiongemma)\
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Fine-tuned on Gemma 3 270M IT checkpoint for function calling
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* [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma)
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The MedGemma collection contains Google's most capable open models for medical text and image comprehension, built on Gemma 3. Developers can use MedGemma to accelerate building healthcare-based AI applications. MedGemma comes in two variants: a 4B multimodal version and a 27B text-only version.
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* [PaliGemma](https://ai.google.dev/gemma/docs/paligemma/model-card)\
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Vision Language Model\
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For a deeper analysis of images and provide useful insights
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* [PaliGemma 2](https://ai.google.dev/gemma/docs/paligemma/model-card-2)\
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VLM which incorporates the capabilities of the Gemma 2 models
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* [RecurrentGemma](https://ai.google.dev/gemma/docs/recurrentgemma)\
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Based on [Griffin](https://arxiv.org/abs/2402.19427) architecture\
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For a variety of text generation tasks
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* [ShieldGemma](https://ai.google.dev/gemma/docs/shieldgemma/model_card)\
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Fine-tuned for evaluating the safety of text prompt input and text output responses against a set of defined safety policies
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* [ShieldGemma 2](https://ai.google.dev/gemma/docs/shieldgemma/model_card_2)\
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Fine-tuned on Gemma 3 4B IT checkpoint for image safety classification
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* [T5Gemma](https://deepmind.google/models/gemma/t5gemma)\
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A collection of encoder-decoder models that provide a strong quality-inference efficiency tradeoff
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* [TranslateGemma](https://huggingface.co/collections/google/translategemma)\
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A collection of open model designed to handle translation tasks across 55 languages
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* [TxGemma](https://deepmind.google/models/gemma/txgemma)\
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A collection of open models designed to improve the efficiency of therapeutic development
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* [VaultGemma](https://deepmind.google/models/gemma/vaultgemma)\
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An open model trained from the ground up using differential privacy to prevent memorization and leaking of training data examples
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You can find the Gemma models on the Hugging Face Hub, Kaggle, Google Cloud Vertex AI Model Garden, and [ai.nvidia.com](https://ai.nvidia.com).
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## Additional Resources
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* [MedGemma on Google-Health](https://github.com/Google-Health/medgemma/tree/main/notebooks) : Google-Health has additional notebooks for using MedGemma
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* [Gemma on Google Cloud](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/open-models) : GCP open models has additional notebooks for using Gemma
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## Get help
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Ask a Gemma cookbook-related question on the [developer forum](https://discuss.ai.google.dev/c/gemma/10), or open an [issue](https://github.com/google-gemini/gemma-cookbook/issues) on GitHub.
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## Wish list
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If you want to see additional cookbooks implemented for specific features/integrations, please open a new issue with [“Feature Request” template](https://github.com/google-gemini/gemma-cookbook/issues/new?template=feature_request.yml).
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If you want to make contributions to the Gemma Cookbook project, you are welcome to pick any idea in the [“Wish List”](https://github.com/google-gemini/gemma-cookbook/labels/wishlist) and implement it.
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## Contributing
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Contributions are always welcome. Please read [contributing](https://github.com/google-gemini/gemma-cookbook/blob/main/CONTRIBUTING.md) before implementation.
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Thank you for developing with Gemma! We’re excited to see what you create.
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## Translation of this repository
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* [Traditional Chinese](https://github.com/doggy8088/gemma-cookbook)
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* [Simplified Chinese](https://github.com/xiaoxiong1006/gemma-cookbook)
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