granite-vision-3.2-2b-table2html is a fine-tuned multimodal model based on granite-vision-3.2-2b. It specializes in extracting HTML <table> structures from images of tables.

Intended Use

🧾 Input: An image containing a table (e.g., screenshot, scan, or photo).
🧪 Output: HTML snippet limited to the <table>...</table> content that structurally and semantically represents the table in the image.

Use Cases

OCR post-processing for tables
Automatic document parsing
AI agents generating structured markup from visual input

Training Details

This model was fine-tuned using PEFT with LoRA (Low-Rank Adaptation) to reduce memory footprint and improve training efficiency.

Training Dataset: apoidea/pubtabnet-html
System Message: "Convert table to HTML (<table> ... </table>)"
Number of Training Images: 10,000
Number of Test Images: 250
Max Sequence Length: 1024
Gradient Accumulation Steps: 8
Epochs: 1
Batch Size: 1 (per device)
Learning Rate: 3e-4
Warmup Steps: 10
Weight Decay: 0.01
Optimizer: adamw_torch_fused
Precision: bf16

LoRA Configuration (PEFT)

python

target_modules = []
for layer_type in layers_to_tune:
    target_modules.extend(
        name for name, _ in model.named_modules()
        if (layer_type in name) 
        and '_proj' in name
    )
LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.1,
    target_modules=target_modules,
    use_dora=True,
    init_lora_weights="gaussian"
)

Evaluation

🧪 Eval Loss: 0.0118
🧮 HTML Similarity: 0.770 These metrics indicate that the model not only converged well during training but also performs accurately on semantic table reconstruction tasks.

Limitations

❌ Not designed for full HTML document generation
❌ May struggle with highly complex or nested tables
⚠️ Requires reasonably clean and well-captured table images

How to Use

python

from transformers import AutoProcessor, AutoModelForVision2Seq
from huggingface_hub import hf_hub_download
import torch

model_path = "ibm-granite/granite-vision-3.2-2b"
processor = AutoProcessor.from_pretrained(model_path, use_fast=True)
model = AutoModelForVision2Seq.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    _attn_implementation="flash_attention_2"
)

def predict(img):
    # Prepare prompt
    conversation = [
        {
          "role": "system",
          "content": [
              {"type": "text", "text": "Convert table to HTML (<table> ... </table>)"}
            ]
        },
        {
            "role": "user",
            "content": [
                {"type": "image"}
            ],
        },
    ]
    text = processor.apply_chat_template(conversation,
        add_generation_prompt=True,
    )
    inputs = processor(images=[img], text=text, return_tensors="pt").to(device)
    output = model.generate(**inputs, max_new_tokens=1500)
    output = processor.decode(output[0], skip_special_tokens=True)
    return output.split('<|assistant|>')[-1].strip()

# Load image
ds = load_dataset('apoidea/pubtabnet-html', streaming=True)['validation']
sample = next(iter(ds))

# autoregressively complete prompt
table = predict(sample['image'])
display(HTML(table)

GitHub Repo

TableVision2html

Blog Post

👉 Read the full story behind this project: "Fine-Tuning Granite-Vision 2B to Outperform 90B Giants (Table Extraction Task)"

Citation

If you use this model, please cite the work:

bibtex

@misc{granite2025table2html,
  title={granite-vision-3.2-2b-table2html: Table HTML extraction from images},
  author={Julio Sánchez},
  year={2025},
  howpublished={\url{https://huggingface.co/JulioSnchezD/granite-vision-3.2-2b-table2html}},
}