Instructions to use keras/harrier_embedding_oss_270m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- KerasHub
How to use keras/harrier_embedding_oss_270m with KerasHub:
import keras_hub # Load CausalLM model (optional: use half precision for inference) causal_lm = keras_hub.models.CausalLM.from_preset("hf://keras/harrier_embedding_oss_270m", 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 TextEmbedder model task = keras_hub.models.TextEmbedder.from_preset("hf://keras/harrier_embedding_oss_270m")import keras_hub # Create a Backbone model unspecialized for any task backbone = keras_hub.models.Backbone.from_preset("hf://keras/harrier_embedding_oss_270m") - Keras
How to use keras/harrier_embedding_oss_270m 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/harrier_embedding_oss_270m") - Notebooks
- Google Colab
- Kaggle
File size: 3,783 Bytes
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"module": "keras_hub.src.models.gemma3.gemma3_text_embedder",
"class_name": "Gemma3TextEmbedder",
"config": {
"backbone": {
"module": "keras_hub.src.models.gemma3.gemma3_backbone",
"class_name": "Gemma3Backbone",
"config": {
"name": "gemma3_backbone",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"vocabulary_size": 262144,
"image_size": null,
"num_layers": 18,
"num_query_heads": 4,
"num_key_value_heads": 1,
"hidden_dim": 640,
"intermediate_dim": 2048,
"head_dim": 256,
"query_head_dim_normalize": true,
"use_query_key_norm": true,
"use_post_ffw_norm": true,
"use_post_attention_norm": true,
"attention_logit_soft_cap": null,
"final_logit_soft_cap": null,
"use_sliding_window_attention": false,
"sliding_window_size": 512,
"local_rope_scaling_factor": 1.0,
"global_rope_scaling_factor": 1.0,
"vision_encoder": null,
"use_bidirectional_attention": false,
"layer_norm_epsilon": 1e-06,
"dropout": 0,
"is_embedding_model": false,
"pooling_intermediate_dim": null,
"embedding_dim": null
},
"registered_name": "keras_hub>Gemma3Backbone"
},
"preprocessor": {
"module": "keras_hub.src.models.gemma3.gemma3_text_embedder_preprocessor",
"class_name": "Gemma3TextEmbedderPreprocessor",
"config": {
"name": "gemma3_text_embedder_preprocessor",
"trainable": true,
"dtype": {
"module": "keras",
"class_name": "DTypePolicy",
"config": {
"name": "float32"
},
"registered_name": null
},
"tokenizer": {
"module": "keras_hub.src.models.gemma3.gemma3_tokenizer",
"class_name": "Gemma3Tokenizer",
"config": {
"name": "gemma3_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,
"has_vision_tokens": true
},
"registered_name": "keras_hub>Gemma3Tokenizer"
},
"config_file": "preprocessor.json",
"sequence_length": 256,
"truncate": "round_robin"
},
"registered_name": "keras_hub>Gemma3TextEmbedderPreprocessor"
},
"name": "gemma3_text_embedder",
"pooling_mode": "last",
"normalize": true
},
"registered_name": "keras_hub>Gemma3TextEmbedder"
} |