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Jingcz/RobustRDP-Pretrain-SFT
RobustRDP-Pretrain-SFT is a image-text-to-text model from Jingcz. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Fine-tuned model for the paper: RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training.
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From the Hugging Face model README
Fine-tuned model for the paper: RobustRDP: Advancing Reaction Diagram Parsing via Synthetic-to-Real Data Scaling and Robustness-Oriented Training.
This model is a fine-tuned checkpoint based on Qwen2.5-VL-3B-Instruct, trained with a two-stage pipeline:
| Config | Pretrain | SFT |
|---|---|---|
| Base Model | Qwen2.5-VL-3B-Instruct | Qwen2.5-VL-3B-Instruct |
| Learning Rate | 1.0×10⁻⁶ | 1.0×10⁻⁵ |
| Batch Size | 16 | 4 |
| Epochs | 1 | 1 |
| Scheduler | Cosine (warmup 0.03) | Cosine (warmup 0.03) |
| Optimizer | AdamW | AdamW |
| Trainable Params | LLM only | Full (vision + LLM) |
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Jingcz/RobustRDP-Pretrain-SFT"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)