Instructions to use JayLee131/vqbet_pusht2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JayLee131/vqbet_pusht2 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JayLee131/vqbet_pusht2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from JayLee131/vqbet_pusht2: direct link, hf CLI and curl.
- Browser
- Download file 1.11 kB
-
https://huggingface.co/JayLee131/vqbet_pusht2/resolve/main/config.json
- Command line
-
hf download hf://JayLee131/vqbet_pusht2/config.json
-
curl -L -o config.json https://huggingface.co/JayLee131/vqbet_pusht2/resolve/main/config.json
1.11 kB
| { | |
| "action_chunk_size": 5, | |
| "bet_softmax_temperature": 0.01, | |
| "crop_is_random": true, | |
| "crop_shape": [ | |
| 84, | |
| 84 | |
| ], | |
| "dropout": 0.1, | |
| "gpt_block_size": 500, | |
| "gpt_hidden_dim": 512, | |
| "gpt_input_dim": 512, | |
| "gpt_n_head": 8, | |
| "gpt_n_layer": 8, | |
| "gpt_output_dim": 512, | |
| "input_normalization_modes": { | |
| "observation.image": "mean_std", | |
| "observation.state": "min_max" | |
| }, | |
| "input_shapes": { | |
| "observation.image": [ | |
| 3, | |
| 96, | |
| 96 | |
| ], | |
| "observation.state": [ | |
| 2 | |
| ] | |
| }, | |
| "mlp_hidden_dim": 1024, | |
| "n_action_pred_token": 7, | |
| "n_obs_steps": 5, | |
| "n_vqvae_training_steps": 20000, | |
| "offset_loss_weight": 10000.0, | |
| "output_normalization_modes": { | |
| "action": "min_max" | |
| }, | |
| "output_shapes": { | |
| "action": [ | |
| 2 | |
| ] | |
| }, | |
| "pretrained_backbone_weights": null, | |
| "primary_code_loss_weight": 5.0, | |
| "secondary_code_loss_weight": 0.5, | |
| "sequentially_select": false, | |
| "spatial_softmax_num_keypoints": 32, | |
| "use_group_norm": true, | |
| "vision_backbone": "resnet18", | |
| "vqvae_embedding_dim": 256, | |
| "vqvae_enc_hidden_dim": 128, | |
| "vqvae_n_embed": 16 | |
| } |