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
Download sentence_bert_config.json from tiny-random/embeddinggemma-2: direct link, hf CLI and curl.
- Browser
- Download file 747 Bytes
-
https://huggingface.co/tiny-random/embeddinggemma-2/resolve/main/sentence_bert_config.json
- Command line
-
hf download hf://tiny-random/embeddinggemma-2/sentence_bert_config.json
-
curl -L -o sentence_bert_config.json https://huggingface.co/tiny-random/embeddinggemma-2/resolve/main/sentence_bert_config.json
747 Bytes
| { | |
| "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" | |
| } |