Instructions to use trapoom555/Phi-2-Text-Embedding-cft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trapoom555/Phi-2-Text-Embedding-cft with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("trapoom555/Phi-2-Text-Embedding-cft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from trapoom555/Phi-2-Text-Embedding-cft: direct link, hf CLI and curl.
- Browser
- Download file 5.26 MB
-
https://huggingface.co/trapoom555/Phi-2-Text-Embedding-cft/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://trapoom555/Phi-2-Text-Embedding-cft/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/trapoom555/Phi-2-Text-Embedding-cft/resolve/main/adapter_model.safetensors
5.26 MB
- Xet hash:
- 8a03e0b1517848f86eb5b4090dca4b997db008e48c538a27a307538c3647f49f
- Size of remote file:
- 5.26 MB
- SHA256:
- b6e09be9034d10addf101599ede992d62244851972967567a7d63d8b70201885
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.