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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 394 Bytes
cd40de0 57f7776 cd40de0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | [
{
"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"
}
] |