| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | base_model: |
| | - Qwen/Qwen2.5-Coder-7B |
| | pipeline_tag: text-generation |
| | tags: |
| | - code |
| | datasets: |
| | - DiagramAgent/DiagramGenBenchmark |
| | --- |
| | |
| | [📑paper link](https://arxiv.org/abs/2411.11916) |
| |
|
| |
|
| |
|
| | ## Model Card: DiagramAgent/Code_Agent |
| | |
| | ### 1. Model Overview |
| | |
| | - **Name**: DiagramAgent/Code_Agent |
| | - **Description**: |
| | The Code_Agent is the core module responsible for converting processed user instructions—provided by the Plan Agent—into executable diagram-specific code. It supports both diagram generation and editing tasks by producing structured, logically coherent code that can be easily compiled into diagrams and further modified if needed. |
| | |
| | ### 2. Intended Use |
| |
|
| | - Primary Tasks: |
| | - Transform complete textual instructions into diagram code. |
| | - Modify existing diagram code based on user-provided editing commands. |
| | - Work in tandem with the Check Agent to ensure the generated code is syntactically correct and logically sound. |
| | - Application Scenarios: |
| | - Automated generation of structured diagrams (e.g., flowcharts, model architecture diagrams, mind maps). |
| | - Rapid prototyping and iterative editing of visual representations. |
| | - Research, education, and industrial applications requiring precise and modifiable diagram construction. |
| |
|
| | ### 3. Architecture and Training Details |
| |
|
| | - **Base Model**: Built upon the Qwen2.5-Coder-7B model. |
| | - Training Process: |
| | - Fine-tuned on the DiagramGenBenchmark dataset, which covers a variety of diagram types. |
| | - Trained for 4 epochs with a maximum input length of 8192 tokens. |
| | - The training objective is to minimize the discrepancy between the generated code and reference code using a tailored loss function. |
| | - **Module Interaction**: |
| | Collaborates closely with the Plan Agent (for interpreting instructions) and the Check Agent (for debugging and verification) to complete both diagram generation and editing workflows. |
| | |
| |
|
| | ### 4.Usage Examples |
| |
|
| | ```py |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | model_name = "DiagramAgent/Code_Agent" |
| | |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_name, |
| | torch_dtype="auto", |
| | device_map="auto" |
| | ) |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | |
| | messages = [ |
| | {"role": "user", "content": "your input"} |
| | ] |
| | text = tokenizer.apply_chat_template( |
| | messages, |
| | tokenize=False, |
| | add_generation_prompt=True |
| | ) |
| | model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| | |
| | generated_ids = model.generate( |
| | **model_inputs, |
| | max_new_tokens=8192 |
| | ) |
| | generated_ids = [ |
| | output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
| | ] |
| | |
| | response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| | ``` |
| |
|
| | ### 5. Citation |
| |
|
| | If you find our work helpful, feel free to give us a cite. |
| |
|
| |
|
| | ``` |
| | @inproceedings{wei2024wordsstructuredvisualsbenchmark, |
| | title={From Words to Structured Visuals: A Benchmark and Framework for Text-to-Diagram Generation and Editing}, |
| | author={Jingxuan Wei and Cheng Tan and Qi Chen and Gaowei Wu and Siyuan Li and Zhangyang Gao and Linzhuang Sun and Bihui Yu and Ruifeng Guo}, |
| | booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| | year={2025} |
| | } |
| | ``` |
