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:
- 00cc98da4c4e64a19506710d3725c88f178ce1d0f90552b2d0c3ed6f288fc640
- Size of remote file:
- 4.34 kB
- SHA256:
- 0b5e0387c7c4e630dec60fd41f49a1cbed83e406e4b0e2e288d36393fb2276f6
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