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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from vectorzhou/gemma-2-2b-it-preference_700K-Preference: direct link, hf CLI and curl.
- Browser
- Download file 4.24 MB
-
https://huggingface.co/vectorzhou/gemma-2-2b-it-preference_700K-Preference/resolve/main/tokenizer.model
- Command line
-
hf download hf://vectorzhou/gemma-2-2b-it-preference_700K-Preference/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/vectorzhou/gemma-2-2b-it-preference_700K-Preference/resolve/main/tokenizer.model
4.24 MB
- Xet hash:
- fde8653f2f656fb4ab30c2a5db64ba86a916a86134355b76c3bf26a5b022b323
- Size of remote file:
- 4.24 MB
- SHA256:
- 61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
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