# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("ciCic/decisionTransformer")
model = AutoModel.from_pretrained("ciCic/decisionTransformer")Quick Links
Running training
- Num examples = 1000
- Num Epochs = 120
- Instantaneous batch size per device = 64
- Total train batch size = 64
- Gradient Accumulation steps = 1
- Total optimization steps = 1920
Train Output
- global_step = 1920
- train_runtime = 1849.2158
- train_samples_per_second = 64.892
- train_steps_per_second = 1.038
- train_loss = 0.04717305501302083
- epoch = 120.0
Dataset
- edbeeching/decision_transformer_gym_replay
- halfcheetah-expert-v2
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ciCic/decisionTransformer")