SmolVLA Federated Learning Checkpoint
This model is a fine-tuned SmolVLA checkpoint trained using federated learning on SO-100 robotics datasets.
Training Details
Training Type: Federated Learning (Flower Framework) Base Model: lerobot/smolvla_base Timestamp: 2025-12-08T15:39:46.531084 Version: 0.7.0
Hyperparameters
- Server Rounds: 2
- Local Epochs: 2
- Proximal μ: 0.01
- Initial Learning Rate: 0.0001
- Batch Size: 64
- Fraction Fit: 0.2
- Fraction Evaluate: 0.2
- Eval Frequency: 1
- Eval Batches: 2
- Checkpoint Interval: 1
- Dynamic Training Decay: True
- Scheduler Type: cosine_warm_restarts
- Adaptive LR Enabled: True
- Adaptive μ Enabled: True
Training Datasets
- shaunkirby/record-test: Put the red LEGO in the bin
- ethanCSL/direction_test: turn to the right side - VALIDATED CLEAN
- gimarchetti/so101-winnie-us5: rub the plush toy with the bottle - VALIDATED CLEAN
- olingoudey/so101_stationary_mug445: Put the stuffed animal in the mug. - VALIDATED CLEAN
- sebastiandavidlee/chopsticksTable2Organizer: Grasp each utensil, classify it, and place it in its designated organizer slot. - VALIDATED CLEAN
- giovipeg/record-test: Pick and place pink knob. - VALIDATED CLEAN
- VoicAndrei/so100_cubes_three_cameras: Pick up the green cubes from the white plate and place them in the black recipient. - VALIDATED CLEAN
- qm30631122/so101_grab_pen_20sec_50ep_SCB_102725_2: Grab pen and put in to cylinder. - VALIDATED CLEAN
- wsi-dev/tictactoe: Place the X in the top right box. - VALIDATED CLEAN
- KeisukeSato1024/so101_pp_green_box: Pick and place the green box. - VALIDATED CLEAN
- stanl1y/record_60_simple: Grab the red pen and place it into the bin. - VALIDATED CLEAN
- RickRain/Imperio: Grab the block and drop it on the black square. - VALIDATED CLEAN
Evaluation Datasets
- Hupy440/Two_Cubes_and_Two_Buckets_v2: Pick up a cube. Is the cube red, put it in the white bucket. Is the cube white, put it in the red bucket. - VALIDATED CLEAN
- dll-hackathon-102025/oct_19_440pm: Put orange slice in glass. - VALIDATED CLEAN
- shuohsuan/grasp1: grasp the red cube and put it into the bin. - VALIDATED CLEAN
- yinxinyuchen/p_p_2: Grab the green cube and put the cube in the green box. - VALIDATED CLEAN
- dll-hackathon-102025/oct_25_umbrella_435pm: put umbrella in glass. - VALIDATED CLEAN
Final Evaluation Metrics
- Composite Eval Loss: 1.240616226196289
- Aggregated Client Metrics: {}
- Individual Client Metrics: 0 clients
Per-Dataset Results
Training Insights
- Convergence Trend: Policy loss: 0.3460 → 1.2406
- Avg Client Loss Trend: Started at 0.0000, ended at 0.6928
- Param Update Norm Trend: Average 0.000000
- Client Participation Rate: Average 1.0 clients per round
- Anomalies: Client dropouts in rounds: [0]
Usage
This model can be used for robotics manipulation tasks. Load with:
from lerobot.policies.smolvla import SmolVLAPolicy
policy = SmolVLAPolicy.from_pretrained("ivelin/zk0-smolvla-fl")
### Model Format
- Weights saved in secure safetensors format (model.safetensors).
- Load with: `from safetensors.torch import load_file; state_dict = load_file("model.safetensors")`
- Avoid legacy pytorch_model.bin for security reasons.
## Limitations
- Trained on SO-100 datasets only
## Citation
If you use this model, please cite:
@misc{zk0-smolvla-fl-2025}, title={SmolVLA Federated Learning on SO-100}, author={Kilo Code, Grok AI, ivelin.eth, and contributors}, year={2025}, url={https://github.com/ivelin/zk0} } ```
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