Instructions to use castorini/mdpr-tied-pft-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use castorini/mdpr-tied-pft-nq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="castorini/mdpr-tied-pft-nq")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("castorini/mdpr-tied-pft-nq") model = AutoModel.from_pretrained("castorini/mdpr-tied-pft-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from castorini/mdpr-tied-pft-nq: direct link, hf CLI and curl.
- Browser
- Download file 711 MB
-
https://huggingface.co/castorini/mdpr-tied-pft-nq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://castorini/mdpr-tied-pft-nq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/castorini/mdpr-tied-pft-nq/resolve/main/pytorch_model.bin
711 MB
- Xet hash:
- 5800a3431dfc9723bdd0aeb7bb3a634c7706beb5871d8afd320e28d8bd8c2a1f
- Size of remote file:
- 711 MB
- SHA256:
- 948080d9cd7f54882fcbec20de7ae20764d8e83dd47ec585e8215e687c59030d
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