Instructions to use xiaotinghe/buffer-embedding-002 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaotinghe/buffer-embedding-002 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="xiaotinghe/buffer-embedding-002", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True) model = AutoModel.from_pretrained("xiaotinghe/buffer-embedding-002", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xiaotinghe/buffer-embedding-002: direct link, hf CLI and curl.
- Browser
- Download file 1.65 GB
-
https://huggingface.co/xiaotinghe/buffer-embedding-002/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xiaotinghe/buffer-embedding-002/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xiaotinghe/buffer-embedding-002/resolve/main/pytorch_model.bin
1.65 GB
- Xet hash:
- 776256b1ffc8f75f0d6d023b04726b4126368d745fd30327cb8a79e0baede214
- Size of remote file:
- 1.65 GB
- SHA256:
- 2d52d56062dce41743e6a21f04e7e725a82ef7eff0a3edc01e610cc2ddd9619f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.