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/64a7398d5763161037b818314c60dd83d93d03e9/pytorch_model.bin
- Command line
-
hf download hf://hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification@64a7398d5763161037b818314c60dd83d93d03e9/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-internal-testing/tiny-random-GPTBigCodeForSequenceClassification/resolve/64a7398d5763161037b818314c60dd83d93d03e9/pytorch_model.bin
321 kB
- Xet hash:
- e6b77d5fdb49fc2af779f73c589833cb182fe9dcecf36d79414315e70bdb219c
- Size of remote file:
- 321 kB
- SHA256:
- 7b96ce9d1088e421e045123068b81e23dfbe48b4a4400ebbc66560d44f939cf2
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