Instructions to use SparseCL/GTE-SparseCL-msmarco with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-msmarco with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-msmarco", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-2800/optimizer.pt from SparseCL/GTE-SparseCL-msmarco: direct link, hf CLI and curl.
- Browser
- Download file 3.47 GB
-
https://huggingface.co/SparseCL/GTE-SparseCL-msmarco/resolve/main/checkpoint-2800/optimizer.pt
- Command line
-
hf download hf://SparseCL/GTE-SparseCL-msmarco/checkpoint-2800/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SparseCL/GTE-SparseCL-msmarco/resolve/main/checkpoint-2800/optimizer.pt
3.47 GB
- Xet hash:
- 0da6535e1fb1ec0e7b38233f2a99a7927172b4cd243c0791d0bc5ce3ecbbba44
- Size of remote file:
- 3.47 GB
- SHA256:
- cc2cb31c5e472ba8950613142a034a182c62018bf0c3ce8230768ff03d85524f
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