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:
- 2e9d1ad0867d27d0ab2512d46c9208774d473aeb2016bc8663f42ffb3544a644
- Size of remote file:
- 2.39 kB
- SHA256:
- 692e96c65ff5f54590de88ba171e84956f4f5c98d34deede93314d35eea7e866
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