Create README.md

README.md

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+---
+library_name: transformers
+pipeline_tag: image-to-text
+tags:
+- chemistry
+- image-to-text
+datasets:
+- UniParser/MolParser-7M
+- UniParser/MolGallery
+---
+
+# 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. 🤗 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)
+```
+
+### Option 2. 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
+)
+```
+
+
+## 📖 Citation
+If you use this model, please cite:
+