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
- Xet hash:
- bb5c3b24862767fc00c8c71c9ed00b55a9485c9013e76d53c97159726c8acbc5
- Size of remote file:
- 4.72 MB
- SHA256:
- 08d0c8316539a853f2fe6e14f51f0df583011dfb078fa08c8b6dc5c15a19a7e6
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