---
license: apache-2.0
library_name: transformers
tags:
  - multimodal
  - vision-language
  - code-generation
  - tikz
  - geometric-reasoning
  - computer-vision
  - cvpr2026
  - internvl
  - state-of-the-art
  - large-multimodal-model
datasets:
  - SJY-1995/GeoTikz-Base
model-index:
  - name: GeoTikzBridge-Base-38B
    results:
      - task:
          type: image-to-text
          name: High-Precision Image-to-TikZ Code Generation
        dataset:
          name: GeoTikz-Base
          type: SJY-1995/GeoTikz-Base
        metrics:
          - type: CLIP-S
            value: 91.5
            name: CLIP-S
          - type: compilation_accuracy
            value: 95.2
            name: LaTeX Compilation Success Rate
---

# GeoTikzBridge-Base-38B

## Model Overview
GeoTikzBridge-Base-38B is the high-capacity flagship model of the GeoTikzBridge series, presented in the **CVPR 2026** accepted paper *GeoTikzBridge: Advancing Multimodal Code Generation for Geometric Perception and Reasoning*. Built on the state-of-the-art InternVL3.5-38B-Instruct multimodal architecture, this model is fully fine-tuned on the 2.5M-scale GeoTikz-Base dataset, delivering industry-leading performance in complex geometric figure perception and high-precision TikZ code generation. With 38 billion parameters, it achieves exceptional accuracy in fine-grained geometric structure restoration, complex multi-component figure parsing, and long-sequence standardized code generation, setting a new baseline for geometric multimodal code generation tasks.

## Model Details
### Core Architecture
- Backbone Foundation: Built on the InternVL3.5-38B-Instruct large multimodal model, leveraging its advanced vision-language alignment capability, long-context understanding, and robust code generation foundation.
- Parameter Scale: 38 billion parameters, optimized for high-precision geometric perception and complex code generation, with significantly enhanced representation learning ability for geometric spatial relationships and structural logic compared to smaller variants.
- Targeted Optimization: Specialized pre-training and fine-tuning for ultra-complex planar geometric figures, including multi-layer nested structures, dense annotation systems, multi-formula integration, and composite engineering schematics, with optimized long-sequence code generation stability.

### Core Capabilities
1.  **Ultra-High Precision Geometric Image-to-TikZ Conversion**: Delivers pixel-level accurate restoration of complex geometric figures, with generated TikZ code achieving near-perfect consistency with the input image after LaTeX rendering.
2.  **Complex Composite Figure Parsing**: Supports end-to-end parsing and code generation for multi-component nested geometric figures, dense engineering schematics, and academic paper illustrations with complex layouts and annotations.
3.  **Long-Sequence Stable Code Generation**: Maintains excellent syntactic correctness and structural standardization for long TikZ code sequences, with a near-zero syntax error rate for complex figure generation.
4.  **Fine-Grained Geometric Detail Restoration**: Accurately captures and reproduces tiny geometric details, including precise angle labels, dimensional tolerances, dashed/dotted line styles, and nested mathematical formula annotations.
5.  **Strong Cross-Scene Generalization**: Maintains outstanding generation performance across diverse scenarios, from basic geometric teaching materials to high-standard academic journal illustrations and professional planar engineering schematics.

## Intended Use & Limitations
### Intended Use Cases
- Core Scenarios: High-quality geometric illustration generation for top-tier academic journals and conference papers, professional textbook and monograph geometric material production, high-precision planar engineering schematic vectorization, and large-scale high-quality geometric dataset construction.
- Research Purposes: Serves as the state-of-the-art baseline model for geometric perception, multimodal code generation, and spatial reasoning research, supporting cutting-edge academic exploration in related fields.
- Industrial Applications: Can be integrated into professional publishing systems, CAD auxiliary tools, and intelligent education platforms to provide enterprise-level geometric figure processing capabilities.

### Out-of-Scope Use Cases
- Code generation for non-geometric images (e.g., natural images, portraits, complex 3D modeling, unstructured hand-drawn doodles).
- Generation of 3D mechanical drawings, BIM models, and other complex engineering content beyond the scope of TikZ planar drawing.
- High-risk industrial production drawings without professional compilation and manual verification.

### Model Limitations
- The model has higher requirements for deployment hardware, requiring a GPU with sufficient video memory for efficient inference (quantization is supported for deployment on consumer-grade GPUs).
- While achieving extremely high compilation accuracy, the generated TikZ code for extremely complex engineering schematics still requires professional manual verification and fine-tuning.
- The model only supports English and mathematical symbol annotation generation, and does not support text annotations in other languages for now.
- Generation quality may degrade for severely blurred, distorted, or heavily occluded input images.

## Quick Start
### Environment Setup
Install basic dependencies:
```bash
pip install transformers torch pillow accelerate bitsandbytes
```
For full training/inference dependencies, please refer to the official project repository: [GeoTikzBridge GitHub](sslocal://flow/file_open?url=https%3A%2F%2Fgithub.com%2Fsjy-1995%2FGeoTikzBridge%2F&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=)

### Inference
```python
from transformers import AutoProcessor, AutoModelForCausalLM
import torch
from PIL import Image

# Load model and processor
model_name = "SJY-1995/GeoTikzBridge-Base-38B"
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto"
)

# Load input geometric image
image = Image.open("your_complex_geometric_figure.png").convert("RGB")

# Build inference prompt
prompt = ""
inputs = processor(text=prompt, images=image, return_tensors="pt").to(model.device)

# Generate TikZ code
with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=8192,
        temperature=0.1,
        top_p=0.95,
        do_sample=False
    )

# Decode and output the generated result
tikz_code = processor.decode(output[0], skip_special_tokens=True)
print("Generated TikZ Code:\n", tikz_code)
```

## Training Details
### Training Dataset
The model is fully fine-tuned on the **GeoTikz-Base** dataset, which contains approximately 2.5 million high-quality geometric image-TikZ code paired samples. The dataset covers a wide range of scenarios, from basic planar geometric figures to complex composite illustrations, engineering schematics, and academic paper figures, building a comprehensive and high-precision vision-code aligned training corpus.

Dataset Link: [SJY-1995/GeoTikz-Base](sslocal://flow/file_open?url=https%3A%2F%2Fhuggingface.co%2Fdatasets%2FSJY-1995%2FGeoTikz-Base&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=)

### Key Training Hyperparameters
| Hyperparameter | Configuration |
|----------------|---------------|
| Global Batch Size | 64 |
| Peak Learning Rate | 2e-7 |
| Training Epochs | 3 |
| Max Sequence Length | 12800 |
| Training Precision | BF16 |

### Training Framework & Scripts
Model training is implemented based on the official InternVL training framework. The core fine-tuning scripts and complete training pipeline can be found in the project repository:
- Project Repository: [GeoTikzBridge GitHub](sslocal://flow/file_open?url=https%3A%2F%2Fgithub.com%2Fsjy-1995%2FGeoTikzBridge%2F&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=)

## Model Family
The GeoTikzBridge series includes multiple models with different specifications and capabilities to adapt to various scenario requirements:
| Model Name | Parameter Size | Core Capability | Model Link |
|------------|----------------|-----------------|------------|
| GeoTikzBridge-Base-8B | 8B | Lightweight, efficient basic geometric image-to-TikZ code generation | [🤗 Hugging Face](sslocal://flow/file_open?url=https%3A%2F%2Fhuggingface.co%2FSJY-1995%2FGeoTikzBridge-Base-8B&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=) |
| GeoTikzBridge-Base-38B | 38B | Flagship high-precision complex geometric figure TikZ code generation | [🤗 Hugging Face](sslocal://flow/file_open?url=https%3A%2F%2Fhuggingface.co%2FSJY-1995%2FGeoTikzBridge-Base-38B&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=) |
| GeoTikzBridge-Instruct-8B | 8B | Instruction following, auxiliary line generation, interactive geometric reasoning | [🤗 Hugging Face](sslocal://flow/file_open?url=https%3A%2F%2Fhuggingface.co%2FSJY-1995%2FGeoTikzBridge-Instruct-8B&flow_extra=eyJsaW5rX3R5cGUiOiJjb2RlX2ludGVycHJldGVyIn0=) |

## Citation
If you use this model, related datasets or code in your research or projects, please cite the following paper:
```bibtex
@inproceedings{
  geotikzbridge,
  title={GeoTikzBridge: Advancing Multimodal Code Generation for Geometric Perception and Reasoning},
  author={Jiayin Sun and Caixia Sun and Boyu Yang and Hailin Li and Xiao Chen and Yi Zhang and Errui Ding and Liang Li and Chao Deng and Junlan Feng},
  booktitle={2026 IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}
```