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
| { | |
| "_name_or_path": "./output/checkpoint-1500", | |
| "act_dim": 6, | |
| "action_tanh": true, | |
| "activation_function": "relu", | |
| "architectures": [ | |
| "DecisionTransformerModel" | |
| ], | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 50256, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 50256, | |
| "hidden_size": 128, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_ep_len": 4096, | |
| "model_type": "decision_transformer", | |
| "n_embd": 768, | |
| "n_head": 1, | |
| "n_inner": null, | |
| "n_layer": 3, | |
| "n_positions": 1024, | |
| "reorder_and_upcast_attn": false, | |
| "resid_pdrop": 0.1, | |
| "scale_attn_by_inverse_layer_idx": false, | |
| "scale_attn_weights": true, | |
| "state_dim": 17, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.21.1", | |
| "use_cache": true, | |
| "vocab_size": 1 | |
| } | |