---
title: smolvla-libero-fp16-ov
canonical_url: "https://www.modelscope.cn/models/OpenVINO/smolvla-libero-fp16-ov"
md_url: "https://www.modelscope.cn/models/OpenVINO/smolvla-libero-fp16-ov.md"
repository: OpenVINO/smolvla-libero-fp16-ov
last_updated: 2026-09-23
license: apache-2.0
base_model:
  - HuggingFaceVLA/smolvla_libero
base_model_relation: finetune
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 9
stars: 0
tags:
  - vision-language-action
  - robotics
  - physicalai
---

# smolvla-libero-fp16-ov

> smolvla-libero-fp16-ov - OpenVINO 在 ModelScope 开源的模型。SmolVLA That's a lightweight vision-language-action model, which targets single-GPU training and edge deployment use cases. The original implementations can be found here. The model takes images from one…

OpenVINO/smolvla-libero-fp16-ov 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可，基于 HuggingFaceVLA/smolvla_libero 构建。

- **Repository**: OpenVINO/smolvla-libero-fp16-ov
- **License**: apache-2.0
- **Base model**: HuggingFaceVLA/smolvla_libero
- **Tags**: vision-language-action, robotics, physicalai
- **Downloads**: 9
- **Stars**: 0
- **Last updated**: 2026-09-23

Source: https://www.modelscope.cn/models/OpenVINO/smolvla-libero-fp16-ov

---

# SmolVLA

That's a lightweight vision-language-action model, which targets single-GPU training and edge deployment use cases.
The original implementations can be found [here](https://huggingface.co/papers/2506.01844).
The model takes images from one or multiple cameras, robot state, and language instruction and produces
a chunk of actions, which robot can execute as a sequence of movements in real world.

The model weights are converted from the official [HF repo](https://huggingface.co/HuggingFaceVLA/smolvla_libero)
and can be directly applied in the [LIBERO](https://github.com/Lifelong-Robot-Learning/LIBERO) virtual benchmark environment.

To fine-tune your model with a custom dataset, you can use [Physical AI Studio](https://github.com/open-edge-platform/physical-ai-studio).

# How to Use

## Running Inference with [OpenVINO Physical AI framework](https://github.com/openvinotoolkit/physicalai).

### Installation

```bash
uv pip install physicalai numpy
```

The following API example showcases inference API for this model:

```python
import numpy as np
from physicalai.inference import InferenceModel

model = InferenceModel("smolvla-libero-fp16-ov", device="CPU")

# Build a dummy LIBERO-style observation.
# LIBERO provides two cameras (agentview + wrist) and an 8-dim robot state.
# Images use the LeRobot convention: float32 in [0, 1], shape (C, H, W).
observation = {
    "images.image": np.random.rand(1, 3, 256, 256).astype(np.float32),
    "images.image2": np.random.rand(1, 3, 256, 256).astype(np.float32),
    "state": np.zeros((1, 8), dtype=np.float32),
    "task": ["sample task description"],
}

chunk = model.predict_action_chunk(observation)
```

## Running Accuracy Evaluation on LIBERO

### Installation

```bash
uv pip install physicalai-train[libero,smolvla]
```

The following API example demonstrates how to run the model on LIBERO:

```python
from physicalai.benchmark import LiberoBenchmark
from physicalai.inference import InferenceModel

model = InferenceModel("smolvla-libero-fp16-ov", device="CPU")

benchmark = LiberoBenchmark(
    task_suite="libero_10",
    video_dir="fail_videos"
)

results = benchmark.evaluate(model)
```

Note that the model should be downloaded and saved to the `smolvla-libero-fp16-ov` folder prior to running this script.

## Legal information

The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license.

## Disclaimer

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.