Instructions to use keras/opt_6.7b_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/opt_6.7b_en with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://keras/opt_6.7b_en", 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://keras/opt_6.7b_en") - Keras
How to use keras/opt_6.7b_en with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://keras/opt_6.7b_en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 671fa2301aee955b8d8b6892180c9e6e064baa55a36874ee4cd375abc0ccd5b6
- Size of remote file:
- 26.6 GB
- SHA256:
- 3d0f5018c453aa0f5c50ddc256c3e2edea2bf2b5676a18e48700d1d83d6b6805
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