Instructions to use vectorzhou/gemma-2-2b-it-preference_700K-Preference with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vectorzhou/gemma-2-2b-it-preference_700K-Preference with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vectorzhou/gemma-2-2b-it-preference_700K-Preference")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vectorzhou/gemma-2-2b-it-preference_700K-Preference") model = AutoModelForSequenceClassification.from_pretrained("vectorzhou/gemma-2-2b-it-preference_700K-Preference") - Notebooks
- Google Colab
- Kaggle