Instructions to use microsoft/unixcoder-base-nine with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/unixcoder-base-nine with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/unixcoder-base-nine")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unixcoder-base-nine") model = AutoModel.from_pretrained("microsoft/unixcoder-base-nine", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/unixcoder-base-nine: direct link, hf CLI and curl.
- Browser
- Download file 504 MB
-
https://huggingface.co/microsoft/unixcoder-base-nine/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/unixcoder-base-nine/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/unixcoder-base-nine/resolve/main/pytorch_model.bin
504 MB
- Xet hash:
- fde056ed0412eead4cfec5b11e2b88dd43f3d372afe3c29b0c28624916dc001f
- Size of remote file:
- 504 MB
- SHA256:
- e28385bb916434983692dfdd57f5c78c64f92d4a26614965d1e9d150b4a37145
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