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:
- 68749d21a5a47e238661b6abebcf12c0b79386386f1733a4f7eb1f994d40b791
- Size of remote file:
- 737 MB
- SHA256:
- bd751a2da597410e04b24d41f7a61e34f5bf2690079a64800d24d481eff4a91c
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