Instructions to use ehsanhon/my-finetuned-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use ehsanhon/my-finetuned-model with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://ehsanhon/my-finetuned-model", 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://ehsanhon/my-finetuned-model") - Keras
How to use ehsanhon/my-finetuned-model 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://ehsanhon/my-finetuned-model") - Notebooks
- Google Colab
- Kaggle
Download model.weights.h5 from ehsanhon/my-finetuned-model: direct link, hf CLI and curl.
- Browser
- Download file 10 GB
-
https://huggingface.co/ehsanhon/my-finetuned-model/resolve/main/model.weights.h5
- Command line
-
hf download hf://ehsanhon/my-finetuned-model/model.weights.h5
-
curl -L -o model.weights.h5 https://huggingface.co/ehsanhon/my-finetuned-model/resolve/main/model.weights.h5
10 GB
- Xet hash:
- f59bd1fb6865d681a322eca9b54a7b14dfab52270a9f7e5860e8dd03ce4a337d
- Size of remote file:
- 10 GB
- SHA256:
- 39ed320a138b8f028d5bd507102bfcc0fadbbdb43ce58d6563d77c0c2c0a0d1e
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