Instructions to use ShihTing/KaggleAI4Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShihTing/KaggleAI4Code with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ShihTing/KaggleAI4Code")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ShihTing/KaggleAI4Code") model = AutoModelForSequenceClassification.from_pretrained("ShihTing/KaggleAI4Code", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ShihTing/KaggleAI4Code: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/ShihTing/KaggleAI4Code/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ShihTing/KaggleAI4Code/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ShihTing/KaggleAI4Code/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- e48590715a75a07e5fd0268d83d3221db699eecc96d90e4b0b2639e09a1367a6
- Size of remote file:
- 438 MB
- SHA256:
- e30f2e06515ffbcac8b947ce8e4ff415342e07eabc3ed46084a4aba48c4e6f7f
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