Instructions to use trapoom555/MiniCPM-2B-Text-Embedding-cft-pos with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trapoom555/MiniCPM-2B-Text-Embedding-cft-pos with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("trapoom555/MiniCPM-2B-Text-Embedding-cft-pos", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from trapoom555/MiniCPM-2B-Text-Embedding-cft-pos: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-pos/resolve/main/training_args.bin
- Command line
-
hf download hf://trapoom555/MiniCPM-2B-Text-Embedding-cft-pos/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-pos/resolve/main/training_args.bin
4.98 kB
- Xet hash:
- 1e7ac11d1aa5cc1a109a8b285e6a87bf4171f8c090f952485197a7ba9638c55b
- Size of remote file:
- 4.98 kB
- SHA256:
- f4122a97d1616648f1c4c98ecf5e213bfcd54b55d4af110f51aea582f4c439d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.