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

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:

Pretrain stage: Synthetic data pretraining on large-scale synthetic reaction diagrams.
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
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

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
Annotation Platform: RxnLabel

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Safetensors

Model size

4B params

Tensor type

BF16

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