Image-Text-to-Text
PEFT
Safetensors
lora
document-ai
form-understanding
table-structure-recognition
synthetic-data
conversational
Instructions to use D2I-CUHK-Shenzhen/FormStruct-Bench-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use D2I-CUHK-Shenzhen/FormStruct-Bench-SFT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.6-35B-A3B") model = PeftModel.from_pretrained(base_model, "D2I-CUHK-Shenzhen/FormStruct-Bench-SFT") - Notebooks
- Google Colab
- Kaggle
File size: 1,109 Bytes
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base_model: Qwen/Qwen3.6-35B-A3B
library_name: peft
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- base_model:adapter:Qwen/Qwen3.6-35B-A3B
- peft
- lora
- document-ai
- form-understanding
- table-structure-recognition
- synthetic-data
---
# FormStruct-Bench-SFT
FormStruct-Bench-SFT is a PEFT LoRA adapter for
[`Qwen/Qwen3.6-35B-A3B`](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).
It is fine-tuned on synthetic FormStruct training data for structured form
understanding from document images.
## Loading
```python
import torch
from peft import PeftModel
from transformers import AutoModelForImageTextToText, AutoProcessor
base_id = "Qwen/Qwen3.6-35B-A3B"
adapter_id = "D2I-CUHK-Shenzhen/FormStruct-Bench-SFT"
processor = AutoProcessor.from_pretrained(adapter_id, trust_remote_code=True)
base_model = AutoModelForImageTextToText.from_pretrained(
base_id,
dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()
```
## License
Apache-2.0, following the base model license. See `LICENSE`.
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