Instructions to use keras/phi3_mini_128k_instruct_en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/phi3_mini_128k_instruct_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/phi3_mini_128k_instruct_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/phi3_mini_128k_instruct_en") - Keras
How to use keras/phi3_mini_128k_instruct_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/phi3_mini_128k_instruct_en") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97832f0dbf1fca94351abaed9598c35f42e9ca1458ef85818b915e8c606398dc
- Size of remote file:
- 7.64 GB
- SHA256:
- d7c2d6ff5183500e9167a354765ed755ab633417e989acf9e8c4aa07a2ea9e60
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.