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")# 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
- Xet hash:
- 35d39b58dbcca81836266dc8bbe89f000a8221a7058022ac7aaa0f22a75bebcc
- Size of remote file:
- 321 kB
- SHA256:
- efc8fd27f670f3a33e7bae82e2ea94327945e57d6c368436279bf17f8c993a80
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.