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google/gemma-3-270m for
zeromodels. One implementation runs unmodified on
TensorFlow / Torch / JAX. This is a base (pretrained) checkpoint, served here as text -> text via Gemma3TextGenerate; weights are
stored in bfloat16.1import os
2os.environ["KERAS_BACKEND"] = "torch" # or "jax" / "tensorflow"
3
4from zeromodels.models.gemma3 import Gemma3TextGenerate, Gemma3Tokenizer
5
6model = Gemma3TextGenerate.from_weights("zeromodels/gemma-3-270m")
7tokenizer = Gemma3Tokenizer.from_weights("zeromodels/gemma-3-270m")
8
9inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
10outputs = model.generate(**inputs, max_new_tokens=64)
11print(tokenizer.decode(outputs[0]))from_weights("zeromodels/<variant>"):| Variant | Hub |
|---|---|
gemma-3-12b-it | zeromodels/gemma-3-12b-it |
gemma-3-12b-pt | zeromodels/gemma-3-12b-pt |
gemma-3-1b-it | zeromodels/gemma-3-1b-it |
gemma-3-1b-pt | zeromodels/gemma-3-1b-pt |
gemma-3-270m | zeromodels/gemma-3-270m |
gemma-3-270m-it | zeromodels/gemma-3-270m-it |
gemma-3-27b-it | zeromodels/gemma-3-27b-it |
gemma-3-27b-pt | zeromodels/gemma-3-27b-pt |
gemma-3-4b-it | zeromodels/gemma-3-4b-it |
gemma-3-4b-pt | zeromodels/gemma-3-4b-pt |
KERAS_BACKEND before importing Keras / zeromodels.load_dtype="float32" for full precision,
or quantization="int8" to shrink further.hf: prefix:
Gemma3TextGenerate.from_weights("hf:google/gemma-3-270m").