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:
- d5ef5747b10615fdcea192db0fecea98babab060c28d3e130cc6818e740f9277
- Size of remote file:
- 4.87 GB
- SHA256:
- 1ddf3eeef4f46fa7fec3147bc1e5bf90fa7151b4c876ed34ee5edcc844bdab6e
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