Instructions to use facebook/rag-sequence-nq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/rag-sequence-nq with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, RagSequenceForGeneration tokenizer = AutoTokenizer.from_pretrained("facebook/rag-sequence-nq") model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/rag-sequence-nq: direct link, hf CLI and curl.
- Browser
- Download file 2.06 GB
-
https://huggingface.co/facebook/rag-sequence-nq/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/rag-sequence-nq/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/rag-sequence-nq/resolve/main/pytorch_model.bin
2.06 GB
- Xet hash:
- 7f9e5ed0fe67840b8b095115d70a65c549f75f0644da6a84ddb0d76bf6223e5c
- Size of remote file:
- 2.06 GB
- SHA256:
- 5d4eed238842e949ce6645b6cd119336ad4bdcda746bf140979a6d0cbb04dfc6
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