Instructions to use weepcat/hh_sft_RM-Gemma-2B_partial_rm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use weepcat/hh_sft_RM-Gemma-2B_partial_rm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="weepcat/hh_sft_RM-Gemma-2B_partial_rm")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("weepcat/hh_sft_RM-Gemma-2B_partial_rm") model = AutoModelForSequenceClassification.from_pretrained("weepcat/hh_sft_RM-Gemma-2B_partial_rm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ffcac272acbc0a646d46d8a055ace84d3a36e0c732b64dcd89fbb50163eabde
- Size of remote file:
- 34.3 MB
- SHA256:
- 2b55226a7b560fdb3d01f9fa6388c58a725aecc628839aaba9bf1ca619c13fe3
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