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>
100 lines
3.3 KiB
Markdown
100 lines
3.3 KiB
Markdown
# Gemma
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[](https://github.com/google-deepmind/gemma/actions/workflows/pytest_and_autopublish.yml)
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[](https://badge.fury.io/py/gemma)
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[](https://gemma-llm.readthedocs.io/en/latest/?badge=latest)
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[Gemma](https://ai.google.dev/gemma) is a family of open-weights Large Language
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Model (LLM) by [Google DeepMind](https://deepmind.google/), based on Gemini
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research and technology.
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This repository contains the implementation of the
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[`gemma`](https://pypi.org/project/gemma/) PyPI package. A
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[JAX](https://github.com/jax-ml/jax) library to use and fine-tune Gemma.
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For examples and use cases, see our
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[documentation](https://gemma-llm.readthedocs.io/). Please
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report issues and feedback in
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[our GitHub](https://github.com/google-deepmind/gemma/issues).
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### Installation
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1. Install JAX for CPU, GPU or TPU. Follow the instructions on
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[the JAX website](https://jax.readthedocs.io/en/latest/installation.html).
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1. Run
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```sh
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pip install gemma
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```
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### Examples
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Here is a minimal example to have a multi-turn, multi-modal conversation with
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Gemma:
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```python
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from gemma import gm
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# Model and parameters (Gemma 4)
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model = gm.nn.Gemma4_E4B()
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params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA4_E4B_IT)
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# Example of multi-turn conversation
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sampler = gm.text.ChatSampler(
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model=model,
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params=params,
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multi_turn=True,
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)
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prompt = """Which of the 2 images do you prefer ?
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Image 1: <|image|>
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Image 2: <|image|>
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Write your answer as a poem."""
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out0 = sampler.chat(prompt, images=[image1, image2])
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out1 = sampler.chat('What about the other image ?')
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```
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The same `ChatSampler` API works with all Gemma versions (2, 3, 3n, 4).
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Our documentation contains various Colabs and tutorials, including:
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* [Sampling](https://gemma-llm.readthedocs.io/en/latest/colab_sampling.html)
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* [Multi-modal](https://gemma-llm.readthedocs.io/en/latest/colab_multimodal.html)
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* [Fine-tuning](https://gemma-llm.readthedocs.io/en/latest/colab_finetuning.html)
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* [LoRA](https://gemma-llm.readthedocs.io/en/latest/colab_lora_sampling.html)
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* ...
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Additionally, our
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[examples/](https://github.com/google-deepmind/gemma/tree/main/examples) folder
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contain additional scripts to fine-tune and sample with Gemma.
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### Learn more about Gemma
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* To use this library: [Gemma documentation](https://gemma-llm.readthedocs.io/)
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* Technical reports for metrics and model capabilities:
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* [Gemma 1](https://goo.gle/GemmaReport)
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* [Gemma 2](https://goo.gle/gemma2report)
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* [Gemma 3](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
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* Gemma 4 (Coming soon)
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* Other Gemma implementations and doc on the
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[Gemma ecosystem](https://ai.google.dev/gemma/docs)
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### Downloading the models
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To download the model weights. See
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[our documentation](https://gemma-llm.readthedocs.io/en/latest/checkpoints.html).
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### System Requirements
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Gemma can run on a CPU, GPU and TPU. For GPU, we recommend 8GB+ RAM on GPU for
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The 2B checkpoint and 24GB+ RAM on GPU are used for the 7B checkpoint.
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### Contributing
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We welcome contributions! Please read our [Contributing Guidelines](./CONTRIBUTING.md) before submitting a pull request.
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*This is not an official Google product.*
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