Instructions to use gimarchetti/gemma-2-9b-openmath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use gimarchetti/gemma-2-9b-openmath with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://gimarchetti/gemma-2-9b-openmath", dtype="bfloat16") causal_lm.compile(sampler="greedy") # (optional) specify a sampler # Generate text causal_lm.generate("Keras: deep learning for", max_length=64)import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://gimarchetti/gemma-2-9b-openmath") - Keras
How to use gimarchetti/gemma-2-9b-openmath with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://gimarchetti/gemma-2-9b-openmath") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- metadata.json +1 -1
- model.weights.h5 +1 -1
metadata.json
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"keras_version": "3.10.0",
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"keras_hub_version": "0.21.1",
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"parameter_count": 9241705984,
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"date_saved": "2025-07-
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"tasks": [
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"CausalLM"
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"keras_version": "3.10.0",
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"date_saved": "2025-07-16@05:02:16",
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model.weights.h5
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