Instructions to use ciCic/decisionTransformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ciCic/decisionTransformer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ciCic/decisionTransformer")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ciCic/decisionTransformer") model = AutoModel.from_pretrained("ciCic/decisionTransformer", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 538aabba2f650b7d109b7d5ef85d894ed4904c1ca9ab664a7084c1919537cea2
- Size of remote file:
- 8.19 MB
- SHA256:
- 5330eea4cac3ceb108cead934cf15de4718ffd470869e8786088990aa4783967
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