Instructions to use microsoft/longcoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/longcoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/longcoder-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/longcoder-base") model = AutoModel.from_pretrained("microsoft/longcoder-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/longcoder-base: direct link, hf CLI and curl.
- Browser
- Download file 598 MB
-
https://huggingface.co/microsoft/longcoder-base/resolve/73d5c4da86a3afa0694067dee0bb1fe27da40f4c/pytorch_model.bin
- Command line
-
hf download hf://microsoft/longcoder-base@73d5c4da86a3afa0694067dee0bb1fe27da40f4c/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/longcoder-base/resolve/73d5c4da86a3afa0694067dee0bb1fe27da40f4c/pytorch_model.bin
598 MB
- Xet hash:
- 668e58fa683ced299449dfde8a3bf52b91add81c0e1edd27d5c8caa8e0f1cbb7
- Size of remote file:
- 598 MB
- SHA256:
- fde67a1823e80f44c0749b245d7365835be2f1e78221665eabc714bff25116e0
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