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:
- 468f89786a084f976ebe64a0fb2ecf73029025701e08d5a125aa4239fff2908e
- Size of remote file:
- 17.5 MB
- SHA256:
- 3f289bc05132635a8bc7aca7aa21255efd5e18f3710f43e3cdb96bcd41be4922
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