README.md

metadata

license: cc-by-4.0
extra_gated_fields:
  Full Name: text
  Affiliation (Organization/University): text
  Country: country
  DISCLAIMER The model is released for research purposes only and authors do not take any responsibility for any damage or loss arising due to usage of model or any system/model developed using the model: checkbox
datasets:
  - Exploration-Lab/TechING
base_model:
  - meta-llama/Llama-3.2-11B-Vision-Instruct
tags:
  - Techincal Image Understanding
pretty_name: LLama-VL-TUG

LLama-VL-TUG (A finetuned model for Technical Image Understanding)

This is the official model repo of the paper:

TechING: Towards Real World Technical Image Understanding via VLMs

Authors: Tafazzul Nadeem*, Bhavik Shangari*, Manish Rai, Gagan Raj Gupta, Ashutosh Modi

Abstract: Professionals working in technical domain typically hand-draw (on whiteboard, paper, etc.) technical diagrams (e.g., flowcharts, block diagrams, etc.) during discussions; however, if they want to edit these later, it needs to be drawn from scratch. Modern day VLMs have made tremendous progress in image understanding but they struggle when it comes to understanding technical diagrams. One way to overcome this problem is to fine-tune on real world hand-drawn images, but it is not practically possible to generate large number of such images. In this paper, we introduce a large synthetically generated corpus (reflective of real world images) for training VLMs and subsequently evaluate VLMs on a smaller corpus of hand-drawn images (with the help of humans). We introduce several new self-supervision tasks for training and perform extensive experiments with various baseline models and fine-tune Llama 3.2 11B-instruct model on synthetic images on these tasks to obtain LLama-VL-TUG, which significantly improves the ROUGE-L performance of Llama 3.2 11B-instruct by 2.14x and achieves the best all-round performance across all baseline models. On real-world images, human evaluation reveals that we achieve minimum compilation errors across all baselines in 7 out of 8 diagram types and improve the average F1 score of Llama 3.2 11B-instruct by 6.97x.

Base Model

Base model: meta-llama/Llama-3.2-11B-Vision-Instruct
Architecture: Vision-Language Transformer
Fine-tuning method: LoRA

Training Methodology

We fine-tuned Llama3.2-11B-Vision-Instruct using LoRA (image encoder as well as text decoder) on the combination of Primary and Self Supervision tasks (described below) using D1 and D2 corpus of TechING dataset.

Primary Tasks

1. Image2Code: Generating corresponding Mermaid code for a given image.
2. Description2Code: Converting natural language descriptions into Mermaid code.
3. Image2Description: Generating Descriptions from technical diagram images.
4. Image Enhancement via Prompt: Generating Mermaid code of the updated image, given an image and a natural language enhancement prompt.

Self Supervision Tasks

1. Image Enhancement via Description: Given an image along with a textual description of the target image, produce code that reflects the enhanced description.
2. Code Enhancement via Prompt: Given a Mermaid code along with an enhancement prompt, update the code accordingly.
3. Code Enhancement via Description: Given a Mermaid code snippet along with a natural language description of the target image, enhance the code to accurately reflect the changes present in the description.
4. Positive/Negative Image–Code Pair Q&A: Predict given image–code pair constitutes a valid match or a mismatch.
5. Partial Match Image–Code Pair Q&A: Identify partial matches between incomplete and complete image-code pairs.

Hyperparameters Details

per_device_train_batch_size: 1
gradient_accumulation_steps: 1
learning_rate: 2e-5
weight_decay: 0.05
num_train_epochs: 2
lr_scheduler_type: cosine
warmup_ratio: 0.2
bf16: True
lora_rank: 32
lora_alpha: 16
target_modules: QKV
lora_dropout: 0.2
use_rslora: True

Evaluation Results

The radar charts present ROUGE-L performance across the three primary tasks on the D1 test set, comparing LLama-VL-TUG against baselines of comparable model size. Detailed results are provided in our paper, TechING: Towards Real World Technical Image Understanding via VLMs.

Loading the Model

To load the model using the huggingface:

from transformers import MllamaForConditionalGeneration
from peft import LoraConfig, get_peft_model, PeftModel
import torch

base_model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
base_model = MllamaForConditionalGeneration.from_pretrained(
        base_model_id,
        torch_dtype=torch.bfloat16,
        device_map="auto"
)
peft_model_repo = "Exploration-Lab/LLama-VL-TUG"
model = PeftModel.from_pretrained(base_model, peft_model_repo)

Citation

TechING: Towards Real World Technical Image Understanding via VLMs, In the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL) to be held in Rabat, Morocco, from March 24–29, 2026.

@misc{nadeem2026techingrealworldtechnical,
      title={TechING: Towards Real World Technical Image Understanding via VLMs}, 
      author={Tafazzul Nadeem and Bhavik Shangari and Manish Rai and Gagan Raj Gupta and Ashutosh Modi},
      year={2026},
      eprint={2601.18238},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2601.18238}, 
}

License

TechING follows CC-BY-NC license. Thus, users can share and adapt the dataset/codebase if they give credit to the authors and do not use the dataset/codebase for any commercial purposes.

*Equal Contribution