mohannad-tazi/Llama-3.1-8B-Instruct-text-to-svg

Model Card for Model ID

lama-3.1-8B-Instruct-text-to-svg, is a fine-tuned version of Llama 3.1 8B Instruct designed for text-to-SVG generation. It takes natural language descriptions of images or shapes (e.g., "Draw a simple star in SVG.") and generates corresponding SVG (Scalable Vector Graphics) code. This can be useful for:

• Design Automation: Quickly generating visual elements from text prompts.

• Web Development: Creating SVGs for UI components.

• Creative Applications: Supporting artists and designers with concept visualization.

Model Details

Model Description

Llama-3.1-8B-Instruct-text-to-svg is a fine-tuned variant of the Llama 3.1 8B Instruct model specifically adapted for converting natural language prompts into SVG (Scalable Vector Graphics) code. This model leverages the robust language understanding of its base and has been refined on a dataset containing paired examples of text descriptions and their corresponding SVG outputs. As a result, it can interpret user instructions—such as "draw a simple star"—and generate syntactically correct SVG code to render the described graphic.

The model is particularly useful for designers, web developers, and creative professionals who wish to streamline the process of graphic creation. By automating the conversion of textual ideas into vector graphics, it enables rapid prototyping and design iteration without requiring in-depth knowledge of SVG syntax. This integration of language understanding and graphic generation offers a novel approach to design automation, potentially reducing both development time and manual effort.

This model card has been automatically generated on the 🤗 Hub, and while certain details (e.g., development credits or specific training hyperparameters) may be refined over time, the core functionality of translating text to SVG remains a powerful tool for creative and technical applications.

• Developed by: TAZI Mohannad

• Model type: safetensors

• Language(s) (NLP): En

• Finetuned from model [optional]: Llama-3.1-8B-Instruct

Uses

This model is intended to convert natural language descriptions into SVG code, making it easier to create vector graphics directly from text prompts. Its primary users include:

• Designers & Illustrators: Quickly prototype visual ideas or generate design assets.

• Web Developers: Automate the creation of SVG elements for web interfaces.

• Creative Professionals: Experiment with visual concepts without extensive knowledge of graphic coding.

Direct Use

Users can directly input text prompts (e.g., "draw a simple star") to receive corresponding SVG code, which can then be further refined or integrated into larger design workflows. This enables rapid prototyping and lowers the barrier to generating scalable graphics.

Bias, Risks, and Limitations

While the model provides a novel approach to text-to-SVG generation, there are some considerations to keep in mind:

• Technical Limitations: The generated SVG code might occasionally contain syntactic errors or produce designs that do not fully match the prompt. Manual review or post-processing may be necessary.

• Data Bias: As the model is trained on paired text and SVG data, its outputs may reflect any biases or limitations inherent in the training dataset.

• Use-Case Scope: The model is primarily designed for basic SVG generation and might not be suited for highly complex or customized graphic design tasks.

Recommendations

Verification: Always review and test generated SVG code before using it in production environments.

Fine-Tuning: For specialized design needs, consider additional fine-tuning or post-processing.

Feedback Loop: Engage with users to gather feedback on any recurring issues or unexpected behaviors to further refine the model.

How to Get Started with the Model

To quickly test the model, load it using the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer from the repository
model_name = "mohannad-tazi/Llama-3.1-8B-Instruct-text-to-svg"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate SVG code from a text prompt
input_text = "draw a simple star in SVG"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
svg_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(svg_code)

Training Details

Training Data

The training data comprises paired examples of natural language descriptions and their corresponding SVG code. This dataset is designed to cover a variety of basic graphic elements and design instructions. Detailed documentation for the dataset is available in the associated Dataset Card.

Training Procedure

The model was fine-tuned on this paired dataset to learn the mapping from text prompts to SVG syntax. The procedure involved optimizing the model with standard language modeling techniques, adapted to generate structured SVG code.

Summary

In summary, Llama-3.1-8B-Instruct-text-to-svg has been fine-tuned to convert textual design descriptions into SVG code. Its intended use is to simplify and automate the graphic design process by leveraging natural language instructions, while being mindful of its technical limitations and potential biases inherited from its training data.