Instructions to use SparseCL/GTE-SparseCL-hotpotqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SparseCL/GTE-SparseCL-hotpotqa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import NewModelForCL model = NewModelForCL.from_pretrained("SparseCL/GTE-SparseCL-hotpotqa", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-4158/optimizer.pt from SparseCL/GTE-SparseCL-hotpotqa: direct link, hf CLI and curl.
- Browser
- Download file 3.47 GB
-
https://huggingface.co/SparseCL/GTE-SparseCL-hotpotqa/resolve/main/checkpoint-4158/optimizer.pt
- Command line
-
hf download hf://SparseCL/GTE-SparseCL-hotpotqa/checkpoint-4158/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/SparseCL/GTE-SparseCL-hotpotqa/resolve/main/checkpoint-4158/optimizer.pt
3.47 GB
- Xet hash:
- c670f67b23816d3bcd6af10417bdf6eb8a9639e9d06531ba35ada1ba394042af
- Size of remote file:
- 3.47 GB
- SHA256:
- e0d162ab177522dab04b64d81f731454bd618e4f2cb83d14f9d4c07bd3642d55
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