You can deploy this model and start earning money today!

PencilHu/
FastWAM-LingBotVA-PurpleBox

Name: FastWAM-LingBotVA-PurpleBox
Availability: InStock

jokeru/pick_right_purple_box_and_place_it_in_the_middle

Views

No views yet

FastWAM and LingBot-VA Purple Box Checkpoints

Public release of two checkpoints fine-tuned on jokeru/pick_right_purple_box_and_place_it_in_the_middle.

Path	Model	Training point	Notes
`fastwam/step_000500.pt`	memory FastWAM	step 500	Full model checkpoint used by the local FastWAM runtime
`lingbot-va/transformer/`	old `Robbyant/lingbot-va`	step 250	Fine-tuned transformer weights and transformer config

The LingBot-VA optimizer/FSDP state (training_state.pt, about 40.5 GB) is intentionally excluded. The uploaded transformer is sufficient for inference and as a fine-tuning initialization, but still requires the upstream LingBot-VA code and base Wan/VAE/text-encoder components.

Data and split

Source dataset: jokeru/pick_right_purple_box_and_place_it_in_the_middle
Training episodes: 18
Held-out episodes: 4 and 15
Task: pick the right purple box and place it in the middle

FastWAM

Architecture: project memory FastWAM variant
Input views: global_primary, left_wrist, right_wrist
Video size: 384 x 320
Action/proprio schema: canonical 80D with validity masks
Context length: 128
Memory history: long 8, mid 2, short 1
Optimization: 8 GPUs, batch 8/GPU, global batch 64, bf16, 500 steps
Initialization: official RoboTwin FastWAM MoT checkpoint plus the Helios memory patchers from memory checkpoint step 35000

The detailed transfer metadata is stored in fastwam/initialization_report.json. Paths in that report have been reduced to stable checkpoint identifiers.

LingBot-VA

Architecture: old Robbyant/lingbot-va
Optimization: 8 GPUs, batch 24/GPU, global batch 192, 250 steps
Fixed latent window: 16 frames
Action alignment: 16 source action steps per latent frame
Training episodes: 18; held-out episodes: 4 and 15
Input views: right_eye, left_wrist, right_wrist
Compact action: right-arm joints 7D plus right gripper 1D, mapped into the model's 30D action schema

Held-out open-loop results

These numbers are diagnostics from eight generated cases, not closed-loop robot success rates.

Model	Generated frames	Video PSNR	Video MAE	Action MAE
FastWAM	101	16.727	0.0776	0.4830
LingBot-VA	141	16.540	0.0913	0.1146

Loading

Download the FastWAM checkpoint:

python

from huggingface_hub import hf_hub_download

checkpoint = hf_hub_download(
    repo_id="PencilHu/FastWAM-LingBotVA-PurpleBox",
    filename="fastwam/step_000500.pt",
)

Download the LingBot-VA transformer snapshot: