Upload policy weights, train config and readme
Browse files- README.md +63 -0
- config.json +4 -4
- model.safetensors +1 -1
- train_config.json +10 -10
README.md
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---
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base_model: lerobot/smolvla_base
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datasets: hubnemo/so101_sort
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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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- robotics
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- smolvla
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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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lerobot-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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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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"device": "cuda",
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"use_amp": false,
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"use_peft": false,
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"push_to_hub":
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"repo_id":
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"private": null,
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"tags": null,
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"license": null,
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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,
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"scheduler_warmup_steps":
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"scheduler_decay_steps":
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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": false,
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"device": "cuda",
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"use_amp": false,
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"use_peft": false,
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"push_to_hub": true,
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"repo_id": "orellius/so101_sort_smolvla",
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"private": null,
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"tags": null,
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"license": null,
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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,
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"scheduler_warmup_steps": 250,
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"scheduler_decay_steps": 5000,
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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": false,
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 1197789224
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version https://git-lfs.github.com/spec/v1
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oid sha256:55bdd30528904dfb97a97a40f84f26e322a35fa7d8d6533594e0c90d1da0b592
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size 1197789224
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train_config.json
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{
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"dataset": {
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"repo_id": "hubnemo/so101_sort",
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"root": "datasets/
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"episodes": null,
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"image_transforms": {
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"enable": false,
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"device": "cuda",
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"use_amp": false,
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"use_peft": false,
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"push_to_hub":
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"repo_id":
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"private": null,
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"tags": null,
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"license": null,
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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,
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"scheduler_warmup_steps":
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"scheduler_decay_steps":
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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": false,
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"min_period": 0.004,
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"max_period": 4.0
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},
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"output_dir": "outputs/train/
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"job_name": "
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"resume": false,
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"seed": 1000,
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"num_workers": 4,
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"batch_size": 4,
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"steps":
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"eval_freq": 200,
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"log_freq": 100,
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"save_checkpoint": true,
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},
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"scheduler": {
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"type": "cosine_decay_with_warmup",
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"num_warmup_steps":
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"num_decay_steps":
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"peak_lr": 0.0003,
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"decay_lr": 2.5e-06
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},
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{
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"dataset": {
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"repo_id": "hubnemo/so101_sort",
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"root": "datasets/so101_sort",
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"episodes": null,
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"image_transforms": {
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"enable": false,
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"device": "cuda",
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"use_amp": false,
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"use_peft": false,
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"push_to_hub": true,
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"repo_id": "orellius/so101_sort_smolvla",
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"private": null,
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"tags": null,
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"license": null,
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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,
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"scheduler_warmup_steps": 250,
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"scheduler_decay_steps": 5000,
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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": false,
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"min_period": 0.004,
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"max_period": 4.0
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},
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"output_dir": "outputs/train/so101_sort_so101_sort_smolvla_20251107_2019",
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"job_name": "so101_sort_so101_sort_smolvla_20251107_2019",
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"resume": false,
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"seed": 1000,
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"num_workers": 4,
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"batch_size": 4,
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"steps": 5000,
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"eval_freq": 200,
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"log_freq": 100,
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"save_checkpoint": true,
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},
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"scheduler": {
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"type": "cosine_decay_with_warmup",
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"num_warmup_steps": 250,
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"num_decay_steps": 5000,
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"peak_lr": 0.0003,
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"decay_lr": 2.5e-06
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},
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