Instructions to use xma/gptj-small-train-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xma/gptj-small-train-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xma/gptj-small-train-test")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xma/gptj-small-train-test") model = AutoModelForSequenceClassification.from_pretrained("xma/gptj-small-train-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3a98079ba00058196c6340506c2b41f9e7efb4a12c43d7e6f2f94271afcef1d2
- Size of remote file:
- 268 MB
- SHA256:
- b144459554708a7dfb84fb0c38f7ae4ab1206e8b0f1962bb39fa2531d19814e2
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