mogemma 0.1.2

Python/Mojo interface for Google Gemma 3

🔥 MoGemma

Python/Mojo interface for Google Gemma 3.

Features

• Embeddings — Dense vector embeddings via a pure Mojo backend.
• Text generation — Synchronous and async streaming with configurable sampling.
• Google Cloud Storage — Automatic model download from Google's gemma-data bucket.
• OpenTelemetry — Optional tracing instrumentation.

Installation

pip install mogemma

For text generation (requires tokenizer):

pip install 'mogemma[llm]'

Quick Start

Text Generation

from mogemma import SyncGemmaModel

model = SyncGemmaModel()
print(model.generate("Explain quantum computing in one sentence:"))

Async Streaming

import asyncio
from mogemma import AsyncGemmaModel

async def main():
    model = AsyncGemmaModel()
    async for token in model.generate_stream("Once upon a time"):
        print(token, end="", flush=True)

asyncio.run(main())

Embeddings

from mogemma import EmbeddingModel

model = EmbeddingModel()
embeddings = model.embed(["Hello, world!", "Mojo runs Gemma inference."])
print(embeddings.shape)  # (2, 768)

Selecting a Model Variant

All model classes default to gemma3-270m-it. Pass a model ID to use a different variant:

model = SyncGemmaModel("gemma3-1b-it")

For full control over sampling parameters, pass a GenerationConfig:

from mogemma import GenerationConfig, SyncGemmaModel

config = GenerationConfig(model_path="gemma3-1b-it", temperature=0.7)
model = SyncGemmaModel(config)

License

MIT