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>
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Gemma
Gemma is a family of open-weights Large Language Model (LLM) by Google DeepMind, based on Gemini research and technology.
This repository contains the implementation of the
gemma PyPI package. A
JAX library to use and fine-tune Gemma.
For examples and use cases, see our documentation. Please report issues and feedback in our GitHub.
Installation
-
Install JAX for CPU, GPU or TPU. Follow the instructions on the JAX website.
-
Run
pip install gemma
Examples
Here is a minimal example to have a multi-turn, multi-modal conversation with Gemma:
from gemma import gm
# Model and parameters (Gemma 4)
model = gm.nn.Gemma4_E4B()
params = gm.ckpts.load_params(gm.ckpts.CheckpointPath.GEMMA4_E4B_IT)
# Example of multi-turn conversation
sampler = gm.text.ChatSampler(
model=model,
params=params,
multi_turn=True,
)
prompt = """Which of the 2 images do you prefer ?
Image 1: <|image|>
Image 2: <|image|>
Write your answer as a poem."""
out0 = sampler.chat(prompt, images=[image1, image2])
out1 = sampler.chat('What about the other image ?')
The same ChatSampler API works with all Gemma versions (2, 3, 3n, 4).
Our documentation contains various Colabs and tutorials, including:
Additionally, our examples/ folder contain additional scripts to fine-tune and sample with Gemma.
Learn more about Gemma
- To use this library: Gemma documentation
- Technical reports for metrics and model capabilities:
- Other Gemma implementations and doc on the Gemma ecosystem
Downloading the models
To download the model weights. See our documentation.
System Requirements
Gemma can run on a CPU, GPU and TPU. For GPU, we recommend 8GB+ RAM on GPU for The 2B checkpoint and 24GB+ RAM on GPU are used for the 7B checkpoint.
Contributing
We welcome contributions! Please read our Contributing Guidelines before submitting a pull request.
This is not an official Google product.