---
license: apache-2.0
language:
- fr
pipeline_tag: image-text-to-text
tags:
- multimodal
library_name: transformers
metrics:
- cer
- wer
- f1
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
---

# Qwen2.5-VL-7B-Instruct Index Cards Nested

## Introduction

This version of QWEN2.5-VL-7B is specialized for document parsing on French index cards.
It was fine-tuned as part of the [DAI-CReTDHI](https://dai-cretdhi.univ-lr.fr/) project.

## Training

The model is a [QWEN2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) fine-tuned on French index cards using LoRA.

Parameters:
- Image width: 800 pixels
- LoRa rank: 8
- LoRa alpha: 32
- Epochs: 10 (about 4k steps) 

Wandb: https://wandb.ai/starride-teklia/DAI-CReTDHI/runs/hk78u308

## Evaluation

|    Set               | CER (%) | WER (%) | F1 @ 0.0 (%) | F1 @ 0.3 (%) | N samples | N entities |
|:--------------------:|:-------:|:-------:|:------------:|:------------:|-----------|------------|
| QWEN2.5-VL-7B Flat   |  10.23  | 18.07   | 83.6   | 91.96    | 55       | 808        |
| QWEN2.5-VL-7B Nested |   **5.48**   |  **15.94**  | **84.86** | **92.27** |  58    | 909        |

### Usage

Here we show a code snippet to show you how to use the model with `transformers` and `qwen_vl_utils`:

* Prediction script

```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Teklia/DAI-cards-nested",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
    device_map="auto",
)

processor = AutoProcessor.from_pretrained("Teklia/DAI-cards-nested")

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": "12e74aa3-4d7d-47b4-b46b-7013b9a1f251.jpg",
            },
            {"type": "text", "text": "Extrait les informations en XML."},
        ],
    }
]

# Preparation for inference
text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=1024)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text[0])
```

* Output

```xml
<root>
	<Décès>
		<Défunt>
			<Nom>Choisnard</Nom>
			<Prénom>Marie Madelaine</Prénom>
			<Sexe>F</Sexe>
			<DateDeNaissance>23 juillet 1753</DateDeNaissance>
			<LieuDeNaissance>Ambroise (Indre-et-Loire)</LieuDeNaissance>
			<Profession>journalière</Profession>
			<Statut>veuf(ve)</Statut>
		</Défunt>
		<Conjoint>
			<Nom>Rocheriou</Nom>
			<Prénom>Pierre</Prénom>
			<Statut>décédé(e)</Statut>
		</Conjoint>
		<Père>
			<Nom>Choisnard</Nom>
			<Prénom>Michel</Prénom>
		</Père>
		<Mère>
			<Nom>Dubeuf</Nom>
			<Prénom>Louise</Prénom>
		</Mère>
	</Décès>
	<Date>
		<Année>1826</Année>
		<Mois>septembre</Mois>
		<Jour>5</Jour>
	</Date>
</root>
```

## Citation

To cite the original QWEN2.5-VL model:

```
@misc{qwen2.5-VL,
    title = {Qwen2.5-VL},
    url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
    author = {Qwen Team},
    month = {January},
    year = {2025}
}
```