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 1_Pooling/config.json from BAAI/llm-embedder: direct link, hf CLI and curl.
- Browser
- Download file 190 Bytes
-
https://huggingface.co/BAAI/llm-embedder/resolve/refs%2Fpr%2F13/1_Pooling/config.json
- Command line
-
hf download hf://BAAI/llm-embedder@refs/pr/13/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/BAAI/llm-embedder/resolve/refs%2Fpr%2F13/1_Pooling/config.json
190 Bytes
| { | |
| "word_embedding_dimension": 768, | |
| "pooling_mode_cls_token": true, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false | |
| } |