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-500/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-500/optimizer.pt
- Command line
-
hf download hf://trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/checkpoint-500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/resolve/main/checkpoint-500/optimizer.pt
11.9 MB
- Xet hash:
- 949898454fcbc84bfc5dc9dcc3a54d1cf18253f3e4fd96cd0091ee4d2b604505
- Size of remote file:
- 11.9 MB
- SHA256:
- 3fda6254cd97ec05137a32ac6b0d9045e7bcb969ea06c1c98ddd1dd7eef83772
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