Instructions to use Jingcz/RobustRDP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jingcz/RobustRDP with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Jingcz/RobustRDP")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Jingcz/RobustRDP")
model = AutoModelForMultimodalLM.from_pretrained("Jingcz/RobustRDP")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jingcz/RobustRDP with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jingcz/RobustRDP"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingcz/RobustRDP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Jingcz/RobustRDP

SGLang

How to use Jingcz/RobustRDP with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Jingcz/RobustRDP" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingcz/RobustRDP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Jingcz/RobustRDP" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jingcz/RobustRDP",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Jingcz/RobustRDP with Docker Model Runner:
```
docker model run hf.co/Jingcz/RobustRDP
```

RobustRDP / README.md

Jingcz

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	---
	license: other
	library_name: transformers
	tags:
	- chemistry
	- molecular-structure
	- reaction-diagram
	- robustrdp
	base_model: Qwen/Qwen2.5-VL-3B-Instruct
	pipeline_tag: image-text-to-text
	---

	# RobustRDP

	Fine-tuned model for the paper: RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training.

	## Description

	This model is a fine-tuned checkpoint based on Qwen2.5-VL-3B-Instruct, trained with a three-stage pipeline:

	1. Pretrain stage: Synthetic data pretraining on large-scale synthetic reaction diagrams.
	2. SFT stage: Supervised fine-tuning with three specialized tasks:
	- Vanilla Reaction Parsing (VRP): Standard reaction diagram parsing
	- Region-Guided Reaction Parsing (RGRP): Region-aware parsing with spatial guidance
	- Prefix-Perturbed Reaction Parsing (PPRP): Robustness-oriented parsing with prefix perturbations
	3. DPO stage: Direct Preference Optimization to further align model outputs with ground-truth annotations.

	## Training Details

	\| Config \| Pretrain \| SFT \| DPO \|
	\|--------\|----------\|-----\|-----\|
	\| Base Model \| Qwen2.5-VL-3B-Instruct \| Qwen2.5-VL-3B-Instruct \| Qwen2.5-VL-3B-Instruct \|
	\| Learning Rate \| 1.0×10⁻⁶ \| 1.0×10⁻⁵ \| 3.0×10⁻⁷ \|
	\| Batch Size \| 16 \| 4 \| 64 \|
	\| Epochs \| 1 \| 1 \| 1 \|
	\| Scheduler \| Cosine (warmup 0.03) \| Cosine (warmup 0.03) \| Cosine (warmup 0.03) \|
	\| Optimizer \| AdamW \| AdamW \| AdamW \|
	\| Trainable Params \| LLM only \| Full (vision + LLM) \| LLM only \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_name = "Jingcz/RobustRDP"
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(model_name)
	```

	## Related Resources

	- Dataset: [RxnLabelData](https://huggingface.co/datasets/Jingcz/RxnLabelData)
	- Annotation Platform: [RxnLabel](https://github.com/jaydetang/RxnLabel)