Instructions to use jdchang/ppo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jdchang/ppo with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jdchang/ppo") model = AutoModelForSeq2SeqLM.from_pretrained("jdchang/ppo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca015035b3faf14484d1e4ead0127035f182f025eb0355f023e3f414aac48199
- Size of remote file:
- 1.88 GB
- SHA256:
- c91030ea0ed4f34efd94df238ff45e27264eb42037921417dfccdb714b375d87
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