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
Upload decision_tree.pkl
Browse files- decision_tree.pkl +3 -0
decision_tree.pkl
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version https://git-lfs.github.com/spec/v1
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oid sha256:056fcb4bcd69103c42d4fa761cceab0ea76d9edf07b33816d417f27e26ed863c
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size 2388
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