---
base_model: Qwen/Qwen3-VL-2B-Instruct
library_name: transformers
model_name: Qwen3-VL-2B-catmus-medieval
tags:
- generated_from_trainer
- sft
- trl
- vision-language
- ocr
- transcription
- medieval
- latin
- manuscript
licence: license
datasets:
- CATMuS/medieval
---

# Model Card for Qwen3-VL-2B-catmus-medieval

This model is a fine-tuned version of [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct) for transcribing line-level medieval manuscripts from images.
It has been trained using [TRL](https://github.com/huggingface/trl) on the [CATMuS/medieval](https://huggingface.co/datasets/CATMuS/medieval) dataset.

## Model Description

This vision-language model specializes in transcribing text from images of line-level medieval manuscripts. Given an image of manuscript text, the model generates the corresponding transcription.

## Performance

The model was evaluated on 100 examples from the [CATMuS/medieval](https://huggingface.co/datasets/CATMuS/medieval) dataset (test split).

### Metrics

| Metric | Base Model | Fine-tuned Model | Improvement |
|--------|-----------|------------------|-------------|
| **Character Error Rate (CER)** | 1.0815 (108.15%) | 0.2779 (27.79%) | **+74.30%** |
| **Word Error Rate (WER)** | 1.7386 (173.86%) | 0.7043 (70.43%) | **+59.49%** |

### Sample Predictions

Here are some example transcriptions comparing the base model and fine-tuned model:


**Example 1:**
- **Reference:** paulꝯ ad thessalonicenses .iii.
- **Base Model:** Paulus ad the Malomancis · iii.
- **Fine-tuned Model:** Paulꝰ ad thessalonensis .iii.

**Example 2:**
- **Reference:** acceptad mi humilde seruicio. e dissipad. e plantad en el
- **Base Model:**  acceptad mi humilde servicio, e dissipad, e plantad en el
- **Fine-tuned Model:** acceptad mi humilde seruicio, e dissipad, e plantad en el

**Example 3:**
- **Reference:** ꝙ mattheus illam dictionem ponat
- **Base Model:**  p mattheus illam dictoneum proa
- **Fine-tuned Model:** ꝑ mattheus illam dictione in ponat

**Example 4:**
- **Reference:** Elige ꝗd uoueas. eadẽ ħ ꝗꝗ sama ferebat.
- **Base Model:** f. ligeq d uonear. eade h q q fama ferebat.
- **Fine-tuned Model:** f liges ꝗd uonear. eadẽ li ꝗq tanta ferebat᷑.

**Example 5:**
- **Reference:** a prima coniugatione ue
- **Base Model:** Grigimacopissagazione-ve
- **Fine-tuned Model:** a ꝑrũt̾tacõnueꝰatione. ne


## Quick start

```python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
from peft import PeftModel
from PIL import Image

# Load model and processor
base_model = "Qwen/Qwen3-VL-2B-Instruct"
adapter_model = "small-models-for-glam/Qwen3-VL-2B-catmus"

model = Qwen3VLForConditionalGeneration.from_pretrained(
    base_model,
    torch_dtype="auto",
    device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter_model)
processor = AutoProcessor.from_pretrained(base_model)

# Load your image
image = Image.open("path/to/your/manuscript_image.jpg")

# Prepare the message
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "Transcribe the text shown in this image."},
        ],
    },
]

# Generate transcription
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=256)
generated_ids_trimmed = [
    out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
transcription = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)[0]

print(transcription)
```

## Use Cases

This model is designed for:
- Transcribing line-level medieval manuscripts
- Digitizing historical manuscripts
- Supporting historical research and archival work
- Optical Character Recognition (OCR) for specialized historical texts

## Training procedure

This model was fine-tuned using Supervised Fine-Tuning (SFT) with LoRA adapters on the Qwen3-VL-2B-Instruct base model.

### Training Data

The model was trained on [CATMuS/medieval](https://huggingface.co/datasets/CATMuS/medieval), 
a dataset containing images of line-level medieval manuscripts with corresponding text transcriptions.

### Training Configuration

- **Base Model**: Qwen/Qwen3-VL-2B-Instruct
- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
- **LoRA Configuration**:
  - Rank (r): 16
  - Alpha: 32
  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
  - Dropout: 0.1
- **Training Arguments**:
  - Epochs: 3
  - Batch size per device: 2
  - Gradient accumulation steps: 4
  - Learning rate: 5e-05
  - Optimizer: AdamW
  - Mixed precision: FP16

### Framework versions

- TRL: 0.23.0
- Transformers: 4.57.1
- Pytorch: 2.8.0
- Datasets: 4.1.1
- Tokenizers: 0.22.1

## Limitations

- The model is specialized for line-level medieval manuscripts and may not perform well on other types of text or images
- Performance may vary depending on image quality, resolution, and handwriting style
- The model has been trained on a specific dataset and may require fine-tuning for other manuscript collections

## Citations

If you use this model, please cite the base model and training framework:

### Qwen3-VL

```bibtex
@article{Qwen3-VL,
  title={Qwen3-VL: Large Vision Language Models Pretrained on Massive Data},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024}
}
```

### TRL (Transformer Reinforcement Learning)
    
```bibtex
@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'{'e}}dec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}
```

---

*README generated automatically on 2025-10-24 10:49:05*