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metadata
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

from transformers import AutoModelForCausalLM, AutoTokenizer

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

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