Instructions to use trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints 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-checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-10500/optimizer.pt from trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 11.9 MB
-
https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/resolve/main/checkpoint-10500/optimizer.pt
- Command line
-
hf download hf://trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/checkpoint-10500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/resolve/main/checkpoint-10500/optimizer.pt
11.9 MB
- Xet hash:
- 1bb6b6ee05954ed703f7f7ee35beb4004141ee3678c8ccb7159273acf5636d4e
- Size of remote file:
- 11.9 MB
- SHA256:
- 6ed6deea10a802ef09fffb64ada9e41e4187c048ae727c8b18be1ae549b0d005
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