Instructions to use hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-GPTNeoForSequenceClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cbe728247b97d95db14e86543108cd89e2188fe4ba86ec932b66576204c18f13
- Size of remote file:
- 1.47 MB
- SHA256:
- beb676ba9c7f650f7dbb1a1b0b9e2d5ce3d6c5aae9f045921329aef6c6910bc5
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