Instructions to use BAAI/llm-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BAAI/llm-embedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="BAAI/llm-embedder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("BAAI/llm-embedder") model = AutoModel.from_pretrained("BAAI/llm-embedder", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c07b0af2010dadde1eab22e551f5d377bd78d28742942137868bf4191ccb2b42
- Size of remote file:
- 436 MB
- SHA256:
- 5103da812b09ebdf689648cc7bec03d0a58a08a461420b49c67055d74efbea08
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