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") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f1bba85c70779fd8e0a02e96fb1e59d2dd4ed01035b6613b8373e8de93845d25
- Size of remote file:
- 438 MB
- SHA256:
- 3f76bef3211017583a582eed6e45c3ef331cc23cbe986d66aaad95b134ed6bfb
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