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-6500/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-6500/optimizer.pt
- Command line
-
hf download hf://trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/checkpoint-6500/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/trapoom555/MiniCPM-2B-Text-Embedding-cft-checkpoints/resolve/main/checkpoint-6500/optimizer.pt
11.9 MB
- Xet hash:
- 3e768dac4a07c2e4c99c1ca4a3eb87e52ed29b1df47662adbc547db5782852c9
- Size of remote file:
- 11.9 MB
- SHA256:
- 39206e358a29f8f5da57de4a126c3681baef216e34aafb1a97aacbcc0d2008b4
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