Instructions to use aequa-tech/flame-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aequa-tech/flame-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aequa-tech/flame-it")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aequa-tech/flame-it") model = AutoModelForSequenceClassification.from_pretrained("aequa-tech/flame-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c487806b556ebac8602d723b18e3cbe1bffab326b78aefefa4f715f41fb5e895
- Size of remote file:
- 1.47 GB
- SHA256:
- 0e5f4cdcf82807903861fbbcdc0bb4f0dd26579c71bc721decd2fcf7d5ac6298
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