Instructions to use microsoft/unixcoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/unixcoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/unixcoder-base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base") model = AutoModel.from_pretrained("microsoft/unixcoder-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/unixcoder-base: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/microsoft/unixcoder-base/resolve/342b13390b7697bc56d496beb40e9cba97d5fd2d/pytorch_model.bin
- Command line
-
hf download hf://microsoft/unixcoder-base@342b13390b7697bc56d496beb40e9cba97d5fd2d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/unixcoder-base/resolve/342b13390b7697bc56d496beb40e9cba97d5fd2d/pytorch_model.bin
504 MB
- Xet hash:
- 7d440bb876a033d9478838a8214bd37bcddf52cdb75ec4dc8bd46cd7b6c83c58
- Size of remote file:
- 504 MB
- SHA256:
- 6732b60037b086557e4c731c3748c99b4fec735d9ffe3c4c70061fd142e6b9d9
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