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Open-weight model · Robotics

train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0

by Mim Chess Vlas mim-chess-vlas/train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0

train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 is an open-weight model for robotics from Mim Chess Vlas, released under Apache License 2.0. It has 4.1B parameters. At 16-bit it needs about 9.9 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training.

Parameters4.1B
Context
Weights46.8 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads

Runs On

What it takes to serve train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 (4.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 8.3 GB 9.9 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 4.1 GB 5.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 2.1 GB 2.5 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Sep 20, 2026.

train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 on every accelerator the SAVRN Index prices, at every precision

Model Card

By Mim Chess Vlas, published under apache-2.0, revision b2d8a96f06ab.

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and…

Read Mim Chess Vlas's full model card

Model Card for pi05

π₀.₅ (Pi05) is a Vision-Language-Action model from Physical Intelligence designed for open-world generalization: it evolves π₀ to generalize to entirely new environments and situations that were never seen during training. The LeRobot implementation is adapted from their open-source OpenPI repository.

This policy has been trained and pushed to the Hub using LeRobot.

Learn how to train and run it in the LeRobot pi05 guide, or browse the full documentation.


Model Details

  • License: apache-2.0
  • Fine-tuned from: lerobot/pi05_base
  • Robot type: Panda
  • Cameras: agentview, robot0_eye_in_hand, robot0_eye_in_hand_2

Inputs & Outputs

The policy consumes these observation features and produces these action features.

Inputs

Feature Type Shape
observation.state STATE (9,)
observation.images.agentview VISUAL (3, 224, 224)
observation.images.robot0_eye_in_hand VISUAL (3, 224, 224)
observation.images.robot0_eye_in_hand_2 VISUAL (3, 224, 224)

Outputs

Feature Type Shape
action ACTION (7,)

Training Dataset

  • Repository: mim-chess-vlas/train_800_complex__mask__overlay_a75__sim__agentview_camera__live
  • Episodes: 790
  • Frames: 157904
  • Frame rate: 20 FPS
  • Task(s): "Pick the smooth ball clasped by two flat curved arms that arch over it from one side and almost meet, leaving an open gap between each arm and the ball and place it into the box", "Pick the upright egg flanked by a pair of tall flat ribs on each side, with a chain of small beads curving around its base and place it into the box", "Pick the small even-proportioned shape, with 4 lobes standing out around it, its surface gently textured, cut by deep recesses, widest right at the crown and place it into the box", "Pick the small even-proportioned shape, with 4 lobes standing out around it, its surface smooth and unbroken, more open frame than solid body, pierced by a few openings, widest right at the crown and place it into the box", "Pick the plain smooth cone, circular at the base and tapering to a rounded point, featureless apart from faint rings on the underside and place it into the box", "Pick the ball with one thick arm hooping right over its top and touching down on the far side, with a single stubby foot below and place it into the box", "Pick the smooth four-pointed star, its arms swept down like a starfish, with a small ring-shaped boss at the centre of its top and place it into the box", "Pick the small even-proportioned shape, round in plan, its surface deeply ribbed and knobbly, cut by deep recesses, pierced by a few openings and place it into the box", "Pick the plain smooth pillow-shaped block with one small ball stuck to a face and a shallow dent beside it and place it into the box", "Pick the medium-sized even-proportioned shape, with 4 lobes standing out around it, its surface ribbed, cut by deep recesses and place it into the box", "Pick the small even-proportioned shape, pinched into two lobes, its surface gently textured, more open frame than solid body, widest low down near the base and place it into the box", "Pick the rounded block with one long curved horn arching up and over from its top, and a shallow dimple sunk into one face and place it into the box", "Pick the smooth rounded body with one broad thin blade curving up off it like a sail, and a small dimple on the body below and place it into the box", "Pick the smooth crown of four tall tapering prongs rising from a squat cup, with narrow slots pierced through the cup wall between them and place it into the box", "Pick the smooth rounded cube with a single deep straight slot sawn down into it from the top, splitting one half in two and place it into the box", "Pick the medium-sized squat shape, pinched into two lobes, its surface smooth and unbroken, solid with shallow recesses and place it into the box", "Pick the medium-sized squat shape, with 4 lobes standing out around it, its surface gently textured, more open frame than solid body, pierced by many openings, widest right at the crown and place it into the box", "Pick the small squat shape, round in plan, its surface gently textured, solid with shallow recesses and place it into the box", "Pick the small squat shape, with 4 lobes standing out around it, its surface ribbed, cut by deep recesses, pierced by many openings and place it into the box", "Pick the medium-sized even-proportioned shape, with 4 lobes standing out around it, its surface gently textured, more open frame than solid body, pierced by many openings, widest low down near the base and place it into the box", "Pick the barrel ringed by about eight evenly spaced fat vertical ribs, like a cog, with a clean smooth ring around an open hole at the top and place it into the box", "Pick the clean square box with one round hole in its top face and a small handle bar bridging the hole and place it into the box", "Pick the small even-proportioned shape, with 4 lobes standing out around it, its surface ribbed, cut by deep recesses, pierced by a few openings, widest right at the crown and place it into the box", "Pick the smooth body with four horns curving up and out from its shoulders like a crown, standing on a low ribbed collar and place it into the box", "Pick the small bowl with three thick prongs curling up from its rim around a single bead sitting in the middle, on a ribbed foot and place it into the box", "Pick the plain smooth barrel, egg-like and featureless, with no arms, holes or ridges anywhere and place it into the box", "Pick the faceted pebble, smooth and plain on one face and hollowed by one big open socket on the opposite face, like a scooped-out stone and place it into the box", "Pick the deep round cup with a section of its wall cut clean away, standing on two long arms that reach out sideways and place it into the box", "Pick the thick-walled shallow bowl, plain outside, with one wide round well hollowed into it and a rounded underside and place it into the box", "Pick the open skeletal frame of thin curved struts arching over a shallow saddle, like a rocking cradle with a handle bar above it and place it into the box", "Pick the ball held by two broad straps that rise from the floor on opposite sides and meet in a small loop above it and place it into the box", "Pick the ball wearing a single broad flat flange across its top like a beret, with two short stubby feet under one side and place it into the box", "Pick the smooth broad scoop, like a cupped hand, with three tapering horns rising off its far rim and place it into the box", "Pick the medium-sized even-proportioned shape, with 4 lobes standing out around it, its surface smooth and unbroken, cut by deep recesses, widest low down near the base and place it into the box", "Pick the medium-sized squat shape, with 4 lobes standing out around it, its surface gently textured, more open frame than solid body, pierced by a few openings, widest right at the crown and place it into the box", "Pick the small even-proportioned shape, round in plan, its surface gently textured, solid all through, pierced by a few openings and place it into the box", "Pick the medium-sized even-proportioned shape, round in plan, its surface gently textured, solid with shallow recesses, pierced by a few openings and place it into the box", "Pick the small even-proportioned shape, round in plan, its surface ribbed, more open frame than solid body, pierced by a few openings and place it into the box", "Pick the medium-sized squat shape, with 4 lobes standing out around it, its surface ribbed, cut by deep recesses, pierced by many openings, widest low down near the base and place it into the box", "Pick the medium-sized even-proportioned shape, pinched into two lobes, its surface gently textured, more open frame than solid body, pierced by many openings, widest low down near the base and place it into the box"

Training Configuration

Setting Value
Training steps 60000
Batch size 16
Optimizer adamw
Learning rate 5e-05
Seed 0
LeRobot version 0.6.0

How to Get Started with the Model

New to LeRobot? These guides cover the full workflow:

The short version to run and train this policy:

Run the policy on your robot

lerobot-rollout \
  --strategy.type=base \
  --robot.type=Panda \
  --robot.port=<your_robot_port> \
  --robot.cameras="{ <camera_1>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}, <camera_2>: {type: opencv, index_or_path: <index_or_path>, width: 640, height: 480, fps: 30}}" \
  --policy.path=mim-chess-vlas/train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 \
  --task="Pick the smooth ball clasped by two flat curved arms that arch over it from one side and almost meet, leaving an open gap between each arm and the ball and place it into the box" \
  --duration=60

Replace the remaining <...> placeholders with your own values: --robot.port and the camera names/indices are specific to your machine, and the camera names must match the observation keys this policy was trained on.

When --strategy.type=base is used the script doesn't record the episodes. Skipping duration will make the policy run indefinitely. For more information look at rollout documentation.

Train your own policy

This policy type is usually fine-tuned from the pretrained base model lerobot/pi05_base:

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.path=lerobot/pi05_base \
  --output_dir=outputs/train/<policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<policy_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<policy_repo_id>/checkpoints/.


Evaluation

No evaluation results have been provided for this policy yet.


Citation

If you use this policy, please cite the method linked in the description above, along with LeRobot:

@misc{cadene2024lerobot,
    author = {Cadene, Remi and Alibert, Simon and Soare, Alexander and Gallouedec, Quentin and Zouitine, Adil and Palma, Steven and Kooijmans, Pepijn and Aractingi, Michel and Shukor, Mustafa and Aubakirova, Dana and Russi, Martino and Capuano, Francesco and Pascal, Caroline and Choghari, Jade and Moss, Jess and Wolf, Thomas},
    title = {LeRobot: State-of-the-art Machine Learning for Real-World Robotics in Pytorch},
    howpublished = "\url{https://github.com/huggingface/lerobot}",
    year = {2024}
}

Identity and Version

Repository
mim-chess-vlas/train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0
Publisher
Mim Chess Vlas
Task
Robotics
Modality
Control
Library
lerobot
Parameters
4.1B parameters
Languages
Not stated by the source
Revision
b2d8a96f06abc4485de493018b988aaaff46f771
First published
2026-09-18
Last updated
2026-09-19

Files and Weights

37 files, 46.8 GB in total. The weights are 15 files totalling 46.8 GB in safetensors.

Weights15 files · 46.8 GB
Configuration20 files · 66.3 KB
Documentation1 file · 13.2 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
checkpoints/010000/pretrained_model/model.safetensorsWeights9.4 GB 901e6ac3f402
checkpoints/010000/pretrained_model/policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/010000/pretrained_model/policy_preprocessor_step_3_normalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/015000/pretrained_model/model.safetensorsWeights9.4 GB ccea8f32500f
checkpoints/015000/pretrained_model/policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/015000/pretrained_model/policy_preprocessor_step_3_normalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/030000/pretrained_model/model.safetensorsWeights9.4 GB 6d8fca58832d
checkpoints/030000/pretrained_model/policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/030000/pretrained_model/policy_preprocessor_step_3_normalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/060000/pretrained_model/model.safetensorsWeights9.4 GB c5c78ad9dbea
checkpoints/060000/pretrained_model/policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/060000/pretrained_model/policy_preprocessor_step_3_normalizer_processor.safetensorsWeights2.7 KB 1920796874b2
model.safetensorsWeights9.4 GB c5c78ad9dbea
policy_postprocessor_step_0_unnormalizer_processor.safetensorsWeights2.7 KB 1920796874b2
policy_preprocessor_step_3_normalizer_processor.safetensorsWeights2.7 KB 1920796874b2
checkpoints/010000/pretrained_model/config.jsonConfiguration2.6 KB
checkpoints/010000/pretrained_model/policy_postprocessor.jsonConfiguration779 B
checkpoints/010000/pretrained_model/policy_preprocessor.jsonConfiguration2.1 KB
checkpoints/010000/pretrained_model/train_config.jsonConfiguration7.7 KB
checkpoints/015000/pretrained_model/config.jsonConfiguration2.6 KB
checkpoints/015000/pretrained_model/policy_postprocessor.jsonConfiguration779 B
checkpoints/015000/pretrained_model/policy_preprocessor.jsonConfiguration2.1 KB
checkpoints/015000/pretrained_model/train_config.jsonConfiguration7.7 KB
checkpoints/030000/pretrained_model/config.jsonConfiguration2.6 KB
checkpoints/030000/pretrained_model/policy_postprocessor.jsonConfiguration779 B
checkpoints/030000/pretrained_model/policy_preprocessor.jsonConfiguration2.1 KB
checkpoints/030000/pretrained_model/train_config.jsonConfiguration7.7 KB
checkpoints/060000/pretrained_model/config.jsonConfiguration2.6 KB
checkpoints/060000/pretrained_model/policy_postprocessor.jsonConfiguration779 B
checkpoints/060000/pretrained_model/policy_preprocessor.jsonConfiguration2.1 KB
checkpoints/060000/pretrained_model/train_config.jsonConfiguration7.7 KB
config.jsonConfiguration2.6 KB
policy_postprocessor.jsonConfiguration779 B
policy_preprocessor.jsonConfiguration2.1 KB
train_config.jsonConfiguration7.7 KB
README.mdDocumentation13.2 KB
.gitattributesRepository1.5 KB

License and Download

License
apache-2.0
Access
Open weights, no gate
Download size
46.8 GB
Download from Mim Chess Vlas

Released by Mim Chess Vlas through its official repository on Hugging Face. Read the license.

Built From

  • Derived from lerobot/pi05_base
  • Trained on (disclosed) mim-chess-vlas/train_800_complex__mask__overlay_a75__sim__agentview_camera__live

Memory Requirements

PrecisionWeights in memory
As published46.8 GB
16-bit8.3 GB
8-bit4.1 GB
4-bit2.1 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0

How much GPU memory does train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 need?

About 9.9 GB at 16-bit and 2.5 GB at 4-bit: the weights (4.1B parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Can I use train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 commercially?

Yes. train_800_complex__mask__overlay_a75__sim__agentview_camera__live__pi05__seed_0 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.

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