Instructions to use thienle1942/my_gemma2_pt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use thienle1942/my_gemma2_pt with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://thienle1942/my_gemma2_pt", 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://thienle1942/my_gemma2_pt") - Keras
How to use thienle1942/my_gemma2_pt with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://thienle1942/my_gemma2_pt") - Notebooks
- Google Colab
- Kaggle
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2bc568c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"module": "keras_hub.src.models.gemma.gemma_causal_lm_preprocessor",
"class_name": "GemmaCausalLMPreprocessor",
"config": {
"name": "gemma_causal_lm_preprocessor",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"tokenizer": {
"module": "keras_hub.src.models.gemma.gemma_tokenizer",
"class_name": "GemmaTokenizer",
"config": {
"name": "gemma_tokenizer",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "int32"
},
"registered_name": null
},
"config_file": "tokenizer.json",
"proto": null,
"sequence_length": null,
"add_bos": false,
"add_eos": false
},
"registered_name": "keras_hub>GemmaTokenizer"
},
"config_file": "preprocessor.json",
"sequence_length": 128,
"add_start_token": true,
"add_end_token": true
},
"registered_name": "keras_hub>GemmaCausalLMPreprocessor"
} |