Update README.md

README.md

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----
-license: apache-2.0
----
+---
+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}
+}
+```