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:
- 4a7d3bda499c4dc1670b86f38f2866475d68be40ebb29d6509d9c80f3a05e1b3
- Size of remote file:
- 4.74 MB
- SHA256:
- 1094d23f68bffc5d257d8a449b9354b1be2476eca95964bbfab629b7e5c6dcd0
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