Instructions to use albus2024/dita_long with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use albus2024/dita_long 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
File size: 1,456 Bytes
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"type": "dita",
"n_obs_steps": 1,
"input_features": {
"observation.images.image": {
"type": "VISUAL",
"shape": [
3,
256,
256
]
}
},
"output_features": {
"action": {
"type": "ACTION",
"shape": [
7
]
}
},
"device": "cuda",
"use_peft": false,
"push_to_hub": false,
"repo_id": null,
"private": null,
"tags": null,
"license": null,
"pretrained_path": null,
"dataset_suite": "libero_10_no_noops",
"num_layers": 12,
"traj_length": 11,
"dropout_rate": 0.0,
"freeze_backbone": false,
"use_qformer": true,
"prediction_type": "epsilon",
"attn_implementation": "eager",
"num_inference_steps": 10,
"tokenizer_max_length": 77,
"image_resolution": [
224,
224
],
"chunk_size": 10,
"n_action_steps": 1,
"history_frames": 2,
"gripper_threshold": 0.5,
"normalization_mapping": {
"VISUAL": "IDENTITY",
"ACTION": "QUANTILES",
"LANGUAGE": "IDENTITY"
},
"optimizer_lr": 0.0005,
"optimizer_betas": [
0.9,
0.95
],
"optimizer_eps": 1e-08,
"optimizer_weight_decay": 0.05,
"optimizer_grad_clip_norm": 0.0,
"dtype": "float16",
"gripper_mode": "binary",
"gradient_checkpointing": true,
"optimizer_backbone_lr_scale": 0.1,
"scheduler_warmup_steps": 1000,
"scheduler_decay_steps": 100000,
"scheduler_min_lr_scale": 0.01,
"lora_dropout": 0.0,
"image_preprocessing_device": "cpu"
}
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