Instructions to use keras/bloom_1.1b_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/bloom_1.1b_multi with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://keras/bloom_1.1b_multi", 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/bloom_1.1b_multi") - Keras
How to use keras/bloom_1.1b_multi 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/bloom_1.1b_multi") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edeb017e34c1707b4ac83f625ae089285f42f89f36ad16cac96ddcdd4700d00d
- Size of remote file:
- 2.13 GB
- SHA256:
- a6f6a7ed8fe365dcd04008352a4623eb04801934c499d620828c8df064a52f06
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.