Instructions to use jmmr-8282/llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jmmr-8282/llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jmmr-8282/llm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jmmr-8282/llm") model = AutoModelForSequenceClassification.from_pretrained("jmmr-8282/llm") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e47fbf95c44c82daf45e9f9c26adb45430e3ae7f2d11547f3b0a5c6ec288c38
- Size of remote file:
- 5.2 kB
- SHA256:
- 1107345434efc608151ecaa36a5f772158c66da373d39ab11cc42f09d2bcbda3
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