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")# pip install -U transformers accelerate # 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
Download onnx/model_O2.onnx from BAAI/llm-embedder: direct link, hf CLI and curl.
- Browser
- Download file 436 MB
-
https://huggingface.co/BAAI/llm-embedder/resolve/refs%2Fpr%2F21/onnx/model_O2.onnx
- Command line
-
hf download hf://BAAI/llm-embedder@refs/pr/21/onnx/model_O2.onnx
-
curl -L -o model_O2.onnx https://huggingface.co/BAAI/llm-embedder/resolve/refs%2Fpr%2F21/onnx/model_O2.onnx
436 MB
- Xet hash:
- c07b0af2010dadde1eab22e551f5d377bd78d28742942137868bf4191ccb2b42
- Size of remote file:
- 436 MB
- SHA256:
- 5103da812b09ebdf689648cc7bec03d0a58a08a461420b49c67055d74efbea08
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