Instructions to use aabyaneh/fasterwam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aabyaneh/fasterwam with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aabyaneh/fasterwam", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
FasterWAM checkpoints
Checkpoint collection for four real-world tasks and four model families.
Layout: <task|joint>/<model>/<run_id>/checkpoints/.
- Tasks:
fruit_pick,dish_utensil,place_cube,push_t. - Models:
fastwam,fasterwam(formerly FlashWAM),lingbot-va,pi0.5. jointis a top-level folder alongside the four tasks, for models trained on all tasks.- Task-specific runs sit directly under
<task>/<model>/.
Each run includes its original checkpoint format and inference/training metadata. Reserved folders contain a README; inspect run folders for available weights.
See CHECKPOINT_UPLOADS.md for the upload contract and commands, checkpoint_layout.json for the path schema, and hf_checkpoint_imports.json for pinned source revisions and file hashes. Imported run READMEs retain their original training and evaluation limitations; these historical runs are not asserted to use the current benchmark split.
Current corrected-data joint checkpoints โ September 21, 2026
All four runs use the four tasks together and the verified corrected place_cube dataset revision 4ad42310b4408ff08370124e680e3a20ad267b1e, with 360 train / 40 held-out episodes and global batch 32 on 8 H200 GPUs.
| Model | Current run | Published epochs / steps | Training outcome |
|---|---|---|---|
| FasterWAM | 4414 | 10, 20, 30 | Time limit after epoch 30 was saved |
| FastWAM | 4415 | 10, 20 | Time limit during epoch 26; no epoch-30 checkpoint exists |
| pi0.5 | 4441 | 30, 40, 50 | Completed 50 epochs; final checkpoint published |
| LingBot-VA | 4469 | Steps 1,200 / 1,500 / 3,000 | Completed 3,000 updates; final checkpoint published |
Native WAM steps for epochs 10/20/30 are 29,310 / 58,620 / 87,930. The published pi0.5 epochs 30/40/50 use zero-based labels 87,929 / 117,239 / 146,549 (87,930 / 117,240 / 146,550 completed updates). These are inference bundles; optimizer/resume state is excluded.
Previous incorrect-data runs are preserved under each model's old/: FasterWAM archive, FastWAM archive. No prior joint pi0.5 run existed in this repository. Validation metrics are model-specific and are not closed-loop robot success measurements.
Imported archives
Completed local training runs
| Run | Native checkpoints | Final | Contents |
|---|---|---|---|
| fruit_pick/pi0.5/train90_job3977 | 5000, 10000, 15000, 16769 | 16769 | params and normalization (inference) |
| place_cube/fastwam/train90_scratch_job4135 | 2620, 7860, 13100 | 13100 | Three inference checkpoints and validation results |
| joint/fastwam/train90_joint_job4146 | 29310, 58620, 87930 | 87930 | Three inference checkpoints and validation results |
| place_cube/lingbot-va/train90_base_job4139 | 300, 1500, 3000 | 3000 | Three inference checkpoints and validation results |
| dish_utensil/pi0.5/train90_job4137 | 3557, 17789, 35579 | 35579 | Three inference checkpoints and validation results |
| place_cube/pi0.5/train90_job4138 | 785, 3929, 7859 | 7859 | Three inference checkpoints and validation results |
Latest update: three checkpoints per completed run from September 15โ16, 2026, spread across training and including the final checkpoint. See latest results for recorded metrics and evaluation limits.
The dataset repository is separate from this model repository. Original source licenses and terms continue to apply.
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