---
title: MolParser-Mobile
canonical_url: "https://www.modelscope.ai/models/UniParser/MolParser-Mobile"
md_url: "https://www.modelscope.ai/models/UniParser/MolParser-Mobile.md"
repository: UniParser/MolParser-Mobile
last_updated: 2026-07-09
license: cc-by-nc-sa-4.0
model_type:
  - molparser_vision_encoder_decoder
architectures:
  - MolParserVisionEncoderDecoderModel
parameters: 10.0M
tensor_type:
  - F16
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
downloads: 63
stars: 0
tags:
  - chemistry
  - image-to-text
---

# MolParser-Mobile

> MolParser-Mobile - An open-source model by UniParser on ModelScope. 💻 Github | 📄 Report (Coming soon...) | 🚀 Demo

UniParser/MolParser-Mobile is a 10.0M-parameter machine learning model on ModelScope. licensed under cc-by-nc-sa-4.0.

- **Repository**: UniParser/MolParser-Mobile
- **License**: cc-by-nc-sa-4.0
- **Parameters**: 10.0M
- **Tags**: chemistry, image-to-text
- **Downloads**: 63
- **Stars**: 0
- **Last updated**: 2026-07-09

Source: https://www.modelscope.ai/models/UniParser/MolParser-Mobile

---

# MolParser Mobile

<p align="center">
💻 <a href="https://github.com/dptech-corp/MolParser">Github</a> |
📄 <a>Report (Coming soon...)</a> |
🚀 <a href="https://ocsr.dp.tech/">Demo</a>
</p>

**MolParser-Mobile** is the next-generation lightweight version of **MolParser**, designed for **ultrafast Optical Chemical Structure Recognition (OCSR)**. It converts molecular structure images into **E-SMILES** representations with significantly higher throughput while maintaining state-of-the-art recognition accuracy.

Compared with MolParser 1.0, MolParser-Mobile reduces the model size by **95.4%** (216M → **9.98M** parameters) and increases inference throughput by **38×**, reaching **1,520 molecules/second** on a single NVIDIA RTX 4090D GPU. Despite its compact architecture, it achieves better accuracy on multiple real-world OCSR benchmarks, making it suitable for large-scale chemical literature mining and industrial deployment.


## 🚀 Highlights

* **38× faster** than MolParser 1.0 (**1,520 Mol/s** on RTX 4090D)
* **9.98M parameters** (**95.4% smaller** than MolParser 1.0)
* **State-of-the-art** accuracy on challenging real-world OCSR benchmarks (BioVista)
* Designed for **web-scale** chemical literature mining
* Converts molecular images directly into **E-SMILES** strings


## 📊 Performance

| Model                |         Parameters |     Throughput (RTX 4090D) |   Uni-Parser Bench |           BioVista |    WildMol-10k |
| -------------------- | -----------------: | -------------------------: | -----------------: | -----------------: | -------------: |
| Gemma4-31B           |                31B |                        OOM |              0.073 |              0.118 |          0.232 |
| Qwen3.5-397B-A17B    |               397B |                        OOM |              0.272 |              0.347 |          0.303 |
| MolParser 1.0        |               216M |                 39.8 Mol/s |              0.800 |              0.703 |      **0.769** |
| **MolParser-Mobile** | **9.98M (-95.4%)** | **1,520 Mol/s (+3819.1%)** | **0.823 (+0.023)** | **0.801 (+0.098)** | 0.734 (-0.035) |


## ⚡ Usage

### Option 1. MolParser Library (Recommended)

The [MolParser library](https://github.com/dptech-corp/MolParser) provides a convenient interface for molecule detection and recognition.

Clone the repository and install the package:

```bash
git clone https://github.com/dptech-corp/MolParser.git
cd MolParser
pip install -e .
```

Then run:

```python
from molparser import MolParser

parser = MolParser()

result = parser.parse(
    "mol.png",
    rec_only=True,   # Recognition only
)
```

### Option 2. 🤗 Transformers
Load MolParser-Mobile directly with the Hugging Face transformers library.

```python
import torch
from PIL import Image
from transformers import AutoModelForImageTextToText, AutoProcessor

repo_id = "UniParser/MolParser-Mobile"
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32

processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    repo_id,
    dtype=dtype,
    trust_remote_code=True,
).to(device).eval()

image = Image.open("mol.png").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device, dtype=dtype) for k, v in inputs.items()}

output_ids = model.generate(**inputs, max_length=256, num_beams=1, do_sample=False)
caption = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
print(caption)
```


## 📜 License

### MolParser-Mobile Weight

The **MolParser-Mobile model weights** are provided for **non-commercial use only**.

For commercial licensing, please contact **fangxi@dp.tech** or open a discussion on Hugging Face.

### MolParser Github Repo

The **MolParser** library (including E-SMILES post-processing and rendering) is available at https://github.com/dptech-corp/MolParser and is licensed under the **Apache License 2.0**, which permits commercial use, modification, and distribution, provided that the license and copyright notices are retained.

**Note:** Model weights, datasets, and third-party dependencies are subject to their respective licenses.

## 📖 Citation

If you use this datasets in your work, please cite:

```
@inproceedings{fang2025molparser,
  title={Molparser: End-to-end visual recognition of molecule structures in the wild},
  author={Fang, Xi and Wang, Jiankun and Cai, Xiaochen and Chen, Shangqian and Yang, Shuwen and Tao, Haoyi and Wang, Nan and Yao, Lin and Zhang, Linfeng and Ke, Guolin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={24528--24538},
  year={2025}
}
```