Instructions to use RLHFlow/Decision-Tree-Reward-Gemma-2-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RLHFlow/Decision-Tree-Reward-Gemma-2-27B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="RLHFlow/Decision-Tree-Reward-Gemma-2-27B", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("RLHFlow/Decision-Tree-Reward-Gemma-2-27B", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("RLHFlow/Decision-Tree-Reward-Gemma-2-27B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75c5a3d8b0d49a75428d6a4e8c8728cd8637d93190b37f3a63e0ec7efa2b2861
- Size of remote file:
- 4.98 GB
- SHA256:
- c0f013650428af46525cb1808cfc278084f1cca63d7eab063a9514a22f39414c
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