Praxis-1 YAM
Praxis-1 policy fine-tuned for the bimanual YAM rig: two YAM arms, a top camera and two wrist cameras.
| Model | 27.2B-parameter MoE video-diffusion policy: chunk-causal trunk + 0.3B action expert |
| Frozen dependencies (not in this repo) | Wan2.2 VAE, Qwen/Qwen3.5-2B text encoder |
| Embodiment | bimanual, cameras top_left (top) + wrist_left + wrist_right |
| Canvas | three 256x416 tiles side by side (256x1248), 16:10 views |
| Control | 15 Hz, 24-step action chunks |
| Action space | 20-D [L: xyz(3) rot6d(6) grip(1) | R: same], each arm relative to its own chunk-start pose; grippers absolute |
| Pose frame | rig (world frame of the rig, no conversion) |
| Weights | bf16, 54.4 GB, 17 safetensors shards (model.safetensors.index.json) |
Normalization statistics and the action codec are in config.json; no dataset
statistics are needed at inference time.
Prompts go to the text encoder verbatim. The checkpoint was trained on captions such as "Pick up the pen from the center of the table, then place the pen in the bag" (no closing period), so send prompts in that form.
Files
config.json: praxis model config (model_type: praxis,format_version: 2)model-000NN-of-00017.safetensors+model.safetensors.index.json: sharded weightsSHA256SUMS: checksums for every file above
Usage
from huggingface_hub import snapshot_download
path = snapshot_download("runwayml/praxis-1-yam")
Load path with runwayml/praxis-1-oss,
serving it with configs/serve_yam.yaml (see "Bimanual rig" in its README).
Inference needs one 80 GB GPU.
Related
runwayml/praxis-1-base: base model, no action heads; the start of a fine-tune for a new rigrunwayml/praxis-1-droid: single-arm DROID policyrunwayml/praxis-1-droid-lerobot:praxis-1-droidin the LeRobot policy layoutrunwayml/praxis-1-yam: bimanual YAM policyrunwayml/praxis-1-eval: evaluation setsrunwayml/praxis-1-oss: reference code (inference, serving, fine-tuning)
License
Apache License 2.0. The frozen dependencies (Wan2.2 VAE, Qwen/Qwen3.5-2B)
are distributed separately under their own licenses.
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