Instructions to use edbeeching/gpt2_reward_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use edbeeching/gpt2_reward_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="edbeeching/gpt2_reward_model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("edbeeching/gpt2_reward_model") model = AutoModelForSequenceClassification.from_pretrained("edbeeching/gpt2_reward_model") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
#1
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:8b793362e131b65a1e593685b2c26f24abae7b7a850bc16d9550e74f30b2bbfc
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size 510362744
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