Instructions to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification: direct link, hf CLI and curl.
- Browser
- Download file 321 kB
-
https://huggingface.co/hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification/resolve/dba163d34de10a8c6d7380c6e9e129c0fa89180d/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification@dba163d34de10a8c6d7380c6e9e129c0fa89180d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification/resolve/dba163d34de10a8c6d7380c6e9e129c0fa89180d/pytorch_model.bin
321 kB
- Xet hash:
- f62b0f4d0d6cb64bd9ec3d2ba7fde9ee4e3badb45aec6dc632db2c40153b9dff
- Size of remote file:
- 321 kB
- SHA256:
- b9fe2bdde7ea0dcd557762faab13ba98071b17dfdbafec1f9c1d8fdbe3030166
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