google-deepmind/gemma
Gemma open-weight LLM library, from Google DeepMind observed · 2026-08-28
Health v2 · maintenance only
87/100
- Activity 99
- Release rhythm 84
- Longevity 66
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 1
- age_days: 925
- days_rel: 105
- days_push: 7
- n_releases_24m: 18
Adoption not part of the score
5695 stars · 1014 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
The official JAX-based Python library from Google DeepMind for running, sampling from, and fine-tuning the Gemma family of open-weight large language models. It supports multi-turn and multi-modal chat, LoRA fine-tuning, and checkpoint loading across Gemma versions 2 through 4.
Use cases
- run gemma llm locally with jax
- fine-tune gemma model
- lora fine-tuning for gemma
- multi-turn chat with gemma
- multimodal image chat with gemma
- load gemma checkpoints in python
When to choose
- you want to use or fine-tune Gemma models in JAX on CPU, GPU, or TPU
- you need the official DeepMind implementation with multi-modal and multi-turn chat support
- you want LoRA-based parameter-efficient fine-tuning of Gemma
When to avoid
- you need PyTorch or transformers-based tooling instead of JAX
- you want to run other model families beyond Gemma
- you need a production serving stack rather than a research library
Facets
library · maturity active
llm-inference llm-training machine-learning chatbot large-language-models deep-learning artificial-intelligence python cloud jax open-weights fine-tuning lora multimodal google-deepmind gpu
2 sources
- readme: https://github.com/google-deepmind/gemma · fetched 2026-08-28 · 7f774bd3b896
- registry_pypi: https://pypi.org/pypi/gemma/json · fetched 2026-08-29 · 15804cf1cd45
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| google-deepmind/gemma | main | 87 |
For agents
markdown · JSON · MCP: product_card(name="google-deepmind/gemma")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem