Instructions to use jdchang/pi_ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdchang/pi_ppo with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jdchang/pi_ppo") model = AutoModelForSeq2SeqLM.from_pretrained("jdchang/pi_ppo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c356af1b76ef114240f80ecca03d0af229c38c49be90882ef0318fc9b9081e17
- Size of remote file:
- 793 MB
- SHA256:
- 4f47d1b0031593db80054c4f271f671dfd6eba7264d03cfe55cb7a021daceee5
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