Instructions to use tiny-random/embeddinggemma-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/embeddinggemma-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tiny-random/embeddinggemma-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("tiny-random/embeddinggemma-2") model = AutoModel.from_pretrained("tiny-random/embeddinggemma-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 747 Bytes
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"transformer_task": "feature-extraction",
"modality_config": {
"text": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"image": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"audio": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"video": {
"method": "forward",
"method_output_name": "last_hidden_state"
},
"message": {
"method": "forward",
"method_output_name": "last_hidden_state",
"format": "structured"
}
},
"module_output_name": "token_embeddings"
} |