Upload policy weights, train config and readme
Browse files- README.md +63 -0
- config.json +88 -0
- model.safetensors +3 -0
- train_config.json +200 -0
README.md
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---
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base_model: lerobot/smolvla_base
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datasets: gribok201/record-test16
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library_name: lerobot
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license: apache-2.0
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model_name: smolvla
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pipeline_tag: robotics
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tags:
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- lerobot
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- smolvla
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- robotics
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---
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# Model Card for smolvla
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<!-- Provide a quick summary of what the model is/does. -->
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[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
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See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
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---
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## How to Get Started with the Model
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For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
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Below is the short version on how to train and run inference/eval:
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### Train from scratch
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```bash
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python -m lerobot.scripts.train \
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--dataset.repo_id=${HF_USER}/<dataset> \
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--policy.type=act \
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--output_dir=outputs/train/<desired_policy_repo_id> \
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--job_name=lerobot_training \
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--policy.device=cuda \
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--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
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--wandb.enable=true
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```
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*Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`.*
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### Evaluate the policy/run inference
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```bash
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python -m lerobot.record \
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--robot.type=so100_follower \
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--dataset.repo_id=<hf_user>/eval_<dataset> \
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--policy.path=<hf_user>/<desired_policy_repo_id> \
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--episodes=10
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```
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Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
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---
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## Model Details
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* **License:** apache-2.0
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config.json
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{
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"type": "smolvla",
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"n_obs_steps": 1,
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"normalization_mapping": {
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"VISUAL": "IDENTITY",
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"STATE": "MEAN_STD",
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"ACTION": "MEAN_STD"
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},
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"input_features": {
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"observation.state": {
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"type": "STATE",
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"shape": [
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6
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]
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},
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"observation.images.top": {
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"type": "VISUAL",
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"shape": [
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3,
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1080,
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1920
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]
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},
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"observation.images.front": {
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"type": "VISUAL",
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"shape": [
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3,
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960,
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1280
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]
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}
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},
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"output_features": {
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"action": {
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"type": "ACTION",
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"shape": [
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6
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]
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}
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},
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"device": "cuda",
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"use_amp": false,
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"push_to_hub": true,
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"repo_id": "smolvlav2",
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"private": null,
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"tags": null,
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"license": null,
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"chunk_size": 50,
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"n_action_steps": 50,
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"max_state_dim": 32,
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"max_action_dim": 32,
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"resize_imgs_with_padding": [
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512,
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512
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],
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"empty_cameras": 0,
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"adapt_to_pi_aloha": false,
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"use_delta_joint_actions_aloha": false,
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"tokenizer_max_length": 48,
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"num_steps": 10,
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"use_cache": true,
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"freeze_vision_encoder": true,
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"train_expert_only": true,
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"train_state_proj": true,
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"optimizer_lr": 0.0001,
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"optimizer_betas": [
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0.9,
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0.95
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],
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"optimizer_eps": 1e-08,
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"optimizer_weight_decay": 1e-10,
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"optimizer_grad_clip_norm": 10.0,
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"scheduler_warmup_steps": 1000,
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"scheduler_decay_steps": 30000,
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"scheduler_decay_lr": 2.5e-06,
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"vlm_model_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
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"load_vlm_weights": true,
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"add_image_special_tokens": false,
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"attention_mode": "cross_attn",
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"prefix_length": 0,
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"pad_language_to": "max_length",
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"num_expert_layers": 0,
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"num_vlm_layers": 16,
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"self_attn_every_n_layers": 2,
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"expert_width_multiplier": 0.75,
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"min_period": 0.004,
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"max_period": 4.0
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c1cc3b50edccf72ad579bf2662b1ec948f5bd8d75b9b1fce8b0db2b950709156
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size 906713296
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train_config.json
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{
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| 2 |
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"dataset": {
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| 3 |
+
"repo_id": "gribok201/record-test16",
|
| 4 |
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"root": null,
|
| 5 |
+
"episodes": null,
|
| 6 |
+
"image_transforms": {
|
| 7 |
+
"enable": false,
|
| 8 |
+
"max_num_transforms": 3,
|
| 9 |
+
"random_order": false,
|
| 10 |
+
"tfs": {
|
| 11 |
+
"brightness": {
|
| 12 |
+
"weight": 1.0,
|
| 13 |
+
"type": "ColorJitter",
|
| 14 |
+
"kwargs": {
|
| 15 |
+
"brightness": [
|
| 16 |
+
0.8,
|
| 17 |
+
1.2
|
| 18 |
+
]
|
| 19 |
+
}
|
| 20 |
+
},
|
| 21 |
+
"contrast": {
|
| 22 |
+
"weight": 1.0,
|
| 23 |
+
"type": "ColorJitter",
|
| 24 |
+
"kwargs": {
|
| 25 |
+
"contrast": [
|
| 26 |
+
0.8,
|
| 27 |
+
1.2
|
| 28 |
+
]
|
| 29 |
+
}
|
| 30 |
+
},
|
| 31 |
+
"saturation": {
|
| 32 |
+
"weight": 1.0,
|
| 33 |
+
"type": "ColorJitter",
|
| 34 |
+
"kwargs": {
|
| 35 |
+
"saturation": [
|
| 36 |
+
0.5,
|
| 37 |
+
1.5
|
| 38 |
+
]
|
| 39 |
+
}
|
| 40 |
+
},
|
| 41 |
+
"hue": {
|
| 42 |
+
"weight": 1.0,
|
| 43 |
+
"type": "ColorJitter",
|
| 44 |
+
"kwargs": {
|
| 45 |
+
"hue": [
|
| 46 |
+
-0.05,
|
| 47 |
+
0.05
|
| 48 |
+
]
|
| 49 |
+
}
|
| 50 |
+
},
|
| 51 |
+
"sharpness": {
|
| 52 |
+
"weight": 1.0,
|
| 53 |
+
"type": "SharpnessJitter",
|
| 54 |
+
"kwargs": {
|
| 55 |
+
"sharpness": [
|
| 56 |
+
0.5,
|
| 57 |
+
1.5
|
| 58 |
+
]
|
| 59 |
+
}
|
| 60 |
+
}
|
| 61 |
+
}
|
| 62 |
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},
|
| 63 |
+
"revision": null,
|
| 64 |
+
"use_imagenet_stats": true,
|
| 65 |
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"video_backend": "torchcodec"
|
| 66 |
+
},
|
| 67 |
+
"env": null,
|
| 68 |
+
"policy": {
|
| 69 |
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"type": "smolvla",
|
| 70 |
+
"n_obs_steps": 1,
|
| 71 |
+
"normalization_mapping": {
|
| 72 |
+
"VISUAL": "IDENTITY",
|
| 73 |
+
"STATE": "MEAN_STD",
|
| 74 |
+
"ACTION": "MEAN_STD"
|
| 75 |
+
},
|
| 76 |
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"input_features": {
|
| 77 |
+
"observation.state": {
|
| 78 |
+
"type": "STATE",
|
| 79 |
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"shape": [
|
| 80 |
+
6
|
| 81 |
+
]
|
| 82 |
+
},
|
| 83 |
+
"observation.images.top": {
|
| 84 |
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"type": "VISUAL",
|
| 85 |
+
"shape": [
|
| 86 |
+
3,
|
| 87 |
+
1080,
|
| 88 |
+
1920
|
| 89 |
+
]
|
| 90 |
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},
|
| 91 |
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"observation.images.front": {
|
| 92 |
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"type": "VISUAL",
|
| 93 |
+
"shape": [
|
| 94 |
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