Instructions to use aractingi/wall_x_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use aractingi/wall_x_3 with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Download config.json from aractingi/wall_x_3: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/aractingi/wall_x_3/resolve/main/config.json
- Command line
-
hf download hf://aractingi/wall_x_3/config.json
-
curl -L -o config.json https://huggingface.co/aractingi/wall_x_3/resolve/main/config.json
1.76 kB
| { | |
| "type": "wall_x", | |
| "n_obs_steps": 1, | |
| "input_features": { | |
| "observation.state": { | |
| "type": "STATE", | |
| "shape": [ | |
| 6 | |
| ] | |
| }, | |
| "observation.images.front": { | |
| "type": "VISUAL", | |
| "shape": [ | |
| 3, | |
| 480, | |
| 640 | |
| ] | |
| }, | |
| "observation.images.eagle": { | |
| "type": "VISUAL", | |
| "shape": [ | |
| 3, | |
| 480, | |
| 640 | |
| ] | |
| }, | |
| "observation.images.glove": { | |
| "type": "VISUAL", | |
| "shape": [ | |
| 3, | |
| 480, | |
| 640 | |
| ] | |
| } | |
| }, | |
| "output_features": { | |
| "action": { | |
| "type": "ACTION", | |
| "shape": [ | |
| 6 | |
| ] | |
| } | |
| }, | |
| "device": "cuda", | |
| "use_amp": false, | |
| "push_to_hub": true, | |
| "repo_id": "aractingi/wall_x_3", | |
| "private": null, | |
| "tags": null, | |
| "license": null, | |
| "pretrained_path": null, | |
| "chunk_size": 32, | |
| "n_action_steps": 32, | |
| "max_action_dim": 20, | |
| "max_state_dim": 20, | |
| "normalization_mapping": { | |
| "VISUAL": "IDENTITY", | |
| "STATE": "MEAN_STD", | |
| "ACTION": "MEAN_STD" | |
| }, | |
| "pretrained_name_or_path": "x-square-robot/wall-oss-flow", | |
| "action_tokenizer_path": null, | |
| "prediction_mode": "diffusion", | |
| "attn_implementation": "eager", | |
| "optimizer_lr": 2e-05, | |
| "optimizer_betas": [ | |
| 0.9, | |
| 0.95 | |
| ], | |
| "optimizer_eps": 1e-08, | |
| "optimizer_weight_decay": 0.01, | |
| "optimizer_grad_clip_norm": 1.0, | |
| "scheduler_warmup_steps": 1000, | |
| "scheduler_decay_steps": 100000, | |
| "scheduler_decay_lr": 1e-06 | |
| } |