Gemma
hosomaki.gemma
A Docker Compose file for Gemma alone, generated from the catalog and checked by the safety inspector. No account needed.
Open-weight text generation models from Google DeepMind, built from the research behind Gemini and released in sizes that run on a single machine. Executed here by the Ollama runtime.
Gemma is a family of open-weight language models published in several parameter sizes, with base and instruction-tuned variants alongside task-specific derivatives such as CodeGemma and PaliGemma. The weights are downloaded and executed locally rather than called over a hosted API, so prompts and completions stay on the deploying machine. In this catalog the executing runtime is Ollama, which handles quantisation, local model storage and the HTTP interface. A first deployment pulls one Gemma variant and issues a completion against it; larger variants need proportionally more memory and are impractical without a GPU.
You know it worked when
- The runtime lists the Gemma model as present in its local library.
- A completion request returns generated text rather than a model-not-found error.
- Token generation proceeds at a usable rate for the chosen variant on the deployment's hardware.
- A second, smaller variant can be pulled and queried without disturbing the first.
Known sharp edges
- The first run downloads several gigabytes of weights and nothing responds until the pull completes.
- Memory demand scales with parameter count and quantisation level, so a variant that loads at 4-bit may fail to load at 8-bit on the same host.
- CPU-only generation works but is slow enough that interactive use is impractical for the larger variants.
- The Gemma Terms of Use restrict certain applications and those restrictions attach to derivatives, which matters before any fine-tune is redistributed.