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loom-ai-org/
gemma-3-270m-it-loom

Name: gemma-3-270m-it-loom
Availability: InStock

loom-py-rt

gguf

text-generation

google/gemma-3-270m-it

quantized

other

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Gemma 3 270M IT

Google's Gemma 3 270M instruction-tuned LM, exported for loom.cpp.

This is a loom.cpp export: a single self-describing GGUF that carries its own graph topologies, tokenizer (if any) and driver script, produced by loom-exporter.

Original model

Exported from google/gemma-3-270m-it. Weights are unmodified; this repo packages the same parameters into loom.cpp's GGUF format.

License

Gemma license -- inherited from the base model above.

Language(s)

(none tagged upstream)

trained on 140+ languages per the upstream model card; no ISO tag list published

Usage

Run it with loom-py -- loom-py-rt on PyPI:

pip install -U "loom-py-rt[hub]"

python

import loom

model = loom.Model.from_pretrained("loom-ai-org/gemma-3-270m-it-loom")
print(model.text2text.chat("Who discovered Brazil?", max_new_tokens=256))

# The same model, one turn at a time, and with the decode rule named rather than inherited:
print(model.chat([("user", "Name one river in Brazil.")], temperature=0.0))

The layer underneath

The call above is the high-level door: one per task, named for the modality pair it maps between, with the windowing, sampling and assembly this model needs already applied. Under it, model.infer(...) passes your arguments straight to the driver this GGUF embeds -- which is where you go for a knob the door does not name.

model.driver_source prints that driver, including a header comment documenting every argument it accepts for this model, and is the authority on it. See loom-py for the API and loom.cpp for what the engine does between the two.

Files

gemma-3-270m-it.gguf -- the model, exported with loom-exporter.