Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…
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—
Weights9.4 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads2.8k
Runs On
What it takes to serve unlv_vla_policy (4.1B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also 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 18, 2026.
Model Card
By Jinseok Kim, published under apache-2.0, revision ca743c6a3869.
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 Jinseok Kim'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:
rby1 - Cameras:
front,right,left
Inputs & Outputs
The policy consumes these observation features and produces these action features.
Inputs
| Feature | Type | Shape |
|---|---|---|
observation.state |
STATE | (16,) |
observation.images.front |
VISUAL | (3, 480, 640) |
observation.images.right |
VISUAL | (3, 640, 480) |
observation.images.left |
VISUAL | (3, 640, 480) |
Outputs
| Feature | Type | Shape |
|---|---|---|
action |
ACTION | (16,) |
Training Dataset
- Repository: kemjensak/unlv_vla
- Episodes: 62
- Frames: 162900
- Frame rate: 30 FPS
- Task(s): "pick up the snack and place it in the box on the moving conveyor", "pickup the snack and place it in the box on the moving conveyor"
Training Configuration
| Setting | Value |
|---|---|
| Training steps | 12000 |
| Batch size | 64 |
| Optimizer | adamw |
| Learning rate | 2.5e-05 |
| Seed | 1000 |
| LeRobot version | 0.6.2 |
How to Get Started with the Model
New to LeRobot? These guides cover the full workflow:
- Install LeRobot — set up the
lerobotpackage. - Hardware setup — assemble, wire, and calibrate your robot and cameras.
- Record data & train a policy — the end-to-end imitation-learning walkthrough.
- CLI cheat-sheet — quick reference for the
lerobot-*commands.
The short version to run and train this policy:
Run the policy on your robot
lerobot-rollout \
--strategy.type=base \
--robot.type=rby1 \
--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=kemjensak/unlv_vla_policy \
--task="pick up the snack and place it in the box on the moving conveyor" \
--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
- kemjensak/unlv_vla_policy
- Publisher
- Jinseok Kim
- Task
- Robotics
- Modality
- Control
- Library
- lerobot
- Parameters
- 4.1B parameters
- Languages
- Not stated by the source
- Revision
- ca743c6a386964ccdf45868891d41abda56d5e94
- First published
- 2026-08-21
- Last updated
- 2026-08-21
Files and Weights
11 files, 9.4 GB in total. The weights are 3 files totalling 9.4 GB in safetensors.
Weights3 files · 9.4 GB
Configuration4 files · 15.0 KB
Tokenizer2 files · 34.4 MB
Documentation1 file · 6.2 KB
Repository1 file · 1.6 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 9.4 GB | 2ae5b740b5d8 |
| policy_postprocessor_step_0_unnormalizer_processor.safetensors | Weights | 9.3 KB | bdaadeec7cb0 |
| policy_preprocessor_step_3_normalizer_processor.safetensors | Weights | 9.4 KB | 8d8afd02d9d9 |
| config.json | Configuration | 3.0 KB | — |
| policy_postprocessor.json | Configuration | 778 B | — |
| policy_preprocessor.json | Configuration | 2.2 KB | — |
| train_config.json | Configuration | 9.0 KB | — |
| README.md | Documentation | 6.2 KB | — |
| .gitattributes | Repository | 1.6 KB | — |
| tokenizer/tokenizer.json | Tokenizer | 34.4 MB | 7336bb4ce016 |
| tokenizer/tokenizer_config.json | Tokenizer | 503 B | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 9.4 GB
Download from Jinseok Kim
Released by Jinseok Kim through its official repository on Hugging Face. Read the license.
Built From
- Derived from lerobot/pi05_base
- Trained on (disclosed) kemjensak/unlv_vla
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 9.4 GB |
| 16-bit | 8.3 GB |
| 8-bit | 4.1 GB |
| 4-bit | 2.1 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About unlv_vla_policy
How much GPU memory does unlv_vla_policy 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 unlv_vla_policy 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 unlv_vla_policy commercially?
Yes. unlv_vla_policy 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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Final checkpoint after 60,000 optimization steps. This is a trained policy, not an evaluation result. Policy weights, policy configuration, preprocessing/postprocessing and normalization states are at the repository root. Training resume files are in trainingstate/. Host-specific paths were removed from JSON metadata; supply local dataset/output paths when resuming. The tokenizer reference points to google/paligemma-3b-pt-224 (training revision 35e4f46485b4d07967e7e9935bc3786aad50687c). For subtask models, supply the corresponding per-frame subtask as the policy task text. For 3-view models also supply the dataset-defined keyframe image. No real-robot evaluation metrics are claimed here.…
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…
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