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abdelstark/
lewm-rs-so100

Name: lewm-rs-so100
Availability: InStock

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lewm-rs — SO-100 Trained Checkpoint

A Rust/Burn implementation of LeWorldModel (Le-WM) trained on the SO-100 pick-and-place dataset.

This checkpoint is trained on abdelstark/so100-pickplace-lewm-ready (1.9 GB HDF5, 6,559 timesteps, 50 episodes at 10 fps) for 6-DOF robotic manipulation.

Training Results

Metric	Value
Steps	5,000
Wall time	864 s (~14 min) on A10G-large
Initial loss	0.500
Final loss	9.56e-05
Gradient explosions	0
Job	`abdelstark/6a070e02e48bea4538b9e2a5` (v11a)

Loss curve:

Step	Total loss	SIGReg	Pred loss
1	5.00e-01	5.00e-01	2.40e-04
1,000	2.03e-01	2.03e-01	8.05e-06
2,500	3.70e-04	3.69e-04	1.44e-06
5,000	9.56e-05	9.50e-05	5.34e-07

Architecture

Same ViT-Tiny JEPA architecture as the PushT model, adapted for 6-DOF robotic manipulation:

ViT-Tiny visual encoder — 192-dim, 12 transformer layers, 3 attention heads, 14×14 patch tokens from 224×224 top-view images
Action encoder — 6-DOF joint actions (smoothed to 10-dim) → 192-dim embeddings
AdaLN-zero autoregressive predictor — 6 transformer blocks, 16 heads, 2048 MLP
Projector / Pred-proj MLPs — 192 → 2048 → 192 with BatchNorm1d

Total parameters: ~18M

Encoder:   ViT-Tiny (192-d, 12L, 3H, patch=14, img=224, top-view camera)
Predictor: 6 blocks, 16 heads, dim=192, mlp=2048
Action:    6-DOF → smooth10 → emb192 (mlp_scale=4)
Training:  SIGReg + prediction MSE loss (λ=1.0, knots=17, proj=1024)

Training Details

Field	Value
Dataset	`abdelstark/so100-pickplace-lewm-ready`
Hardware	A10G-large (HuggingFace Jobs)
Precision	bf16 mixed
Steps	5,000
Batch size	64
Optimizer	AdamW (lr=3e-4 → 1e-5, warmup=500, wd=0.05, β=[0.9, 0.95])
Grad clip	1.0
History size	3 frames
Prediction horizon	4 frames
Camera	Top view (224×224)
Seed	0

Dataset

Property	Value
Source	`abdelstark/so100-pickplace-lewm-ready`
Episodes	50
Timesteps	6,559
Sampling rate	10 fps
Image resolution	224×224
Action space	6-DOF (joint angles)
HDF5 size	1.9 GB

Artifacts

File	Description
`train/so100-full-20260515T122820Z/step_0005000.safetensors`	Model weights
`train/so100-full-20260515T122820Z/step_0005000.mpk`	Full checkpoint
`train/so100-full-20260515T122820Z/train_report.json`	Training summary
`train/so100-full-20260515T122820Z/train_losses.jsonl`	Per-step loss log

Warm-Start Evaluation

Pending. The .mpk checkpoint is available for warm-start evaluation (initialising from this SO-100 checkpoint vs. from the PushT checkpoint).

Repository

Training code: AbdelStark/lewm-rs
Dataset: abdelstark/so100-pickplace-lewm-ready
PushT model: abdelstark/lewm-rs-pusht
Demo Space: abdelstark/lewm-rs-demo

License

MIT. See LICENSE.

abdelstark/lewm-rs-so100