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kerasformers/
gemma-1.1-2b-it

Name: gemma-1.1-2b-it
Availability: InStock

google/gemma-1.1-2b-it

finetune

gemma

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Run Gemma with Keras 3: JAX, PyTorch, or TensorFlow

GitHub

HuggingFace

zeromodels/gemma-1.1-2b-it

Pure-Keras 3 conversion of google/gemma-1.1-2b-it for zeromodels. One implementation runs unmodified on TensorFlow / Torch / JAX. This is the instruction-tuned checkpoint, served here as text -> text via GemmaTextGenerate; weights are stored in bfloat16.

For model details, license, and usage terms, see Google's model card.

✨ Quick start

python

import os
os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"

from zeromodels.models.gemma import GemmaTextGenerate, GemmaTokenizer

model = GemmaTextGenerate.from_weights("zeromodels/gemma-1.1-2b-it")
tokenizer = GemmaTokenizer.from_weights("zeromodels/gemma-1.1-2b-it")

inputs = tokenizer([{"role": "user", "content": "Hello, who are you?"}])
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0]))

Load any Gemma variant the same way with from_weights("zeromodels/<variant>"):

Variant	Hub
`gemma-1.1-2b-it`	zeromodels/gemma-1.1-2b-it
`gemma-1.1-7b-it`	zeromodels/gemma-1.1-7b-it
`gemma-2b`	zeromodels/gemma-2b
`gemma-2b-it`	zeromodels/gemma-2b-it
`gemma-7b`	zeromodels/gemma-7b
`gemma-7b-it`	zeromodels/gemma-7b-it

Tips

Set KERAS_BACKEND before importing Keras / zeromodels.
Loads in bfloat16 by default. Pass load_dtype="float32" for full precision, or quantization="int8" to shrink further.
See the Gemma docs.
Community / upstream weights still work via the hf: prefix: GemmaTextGenerate.from_weights("hf:google/gemma-1.1-2b-it").

Special Thanks

A huge thank you to the Google Gemma authors for creating and releasing these models.

License: Gemma (gated). Accept the license on the upstream Hub card before downloading.

kerasformers/gemma-1.1-2b-it