Instructions to use lamarr-llm-development/elbedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lamarr-llm-development/elbedding with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lamarr-llm-development/elbedding")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("lamarr-llm-development/elbedding") model = AutoModel.from_pretrained("lamarr-llm-development/elbedding") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "embedding_model.EmbeddingModel" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_TokenPooling", | |
| "type": "token_pooling.TokenPooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |