LModel-Optimization 🧠⚡

Optimizing AI Models for Real-World Performance

This project is focused on making AI models faster, smaller, and more efficient — especially for deployment in resource-constrained environments. The goal is to reduce inference time and memory footprint without sacrificing accuracy, making models practical for real-world use cases such as production APIs, mobile, and edge devices.

🔗 Read the full article for a deep dive into the theory, motivation, and implementation steps:
Optimizing AI Models for Real-World Performance: Tuning for Faster Inference

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🚀 What This Project Covers

This repo contains practical examples and code implementations for:

• Quantization — reducing model size and speeding up inference by converting weights from float32 to int8/float16.
• Pruning — removing unnecessary weights and neurons while maintaining performance.
• Knowledge Distillation — training smaller “student” models using outputs from a larger “teacher” model.
• ONNX Export & Runtime Optimization — converting models to ONNX format and using inference-optimized runtimes.
• Benchmarking Tools — to compare inference speed and model sizes before and after optimization.

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🧩 Structure

LModel-Optimization/
├── base_bot.py
├── chatbot.py
├── load_model.py
├── optim_bot.py
├── optimum_optimize.py
└── requirements

🧪 Experimenting

📦 Installation

git clone https://github.com/EddyEjembi/LModel-Optimization.git
cd LModel-Optimization
pip install -r requirements.txt

Running the Code

• load_model.py — Downloads the meta-llama/Llama-3.2-1B-Instruct model to serve as the base model.

• optimum_optimize.py — Optimizes and quantizes the model using HuggingFace Optimum.

• base_bot.py — Runs inference on the base model.

• optim_bot.py — Runs inference on the optimized model of your choice:

  # Load Models
  onnx_model_path = "onnx_model" # Directory for ONNX Model
  optimized_onnx_model_path = "optimized_onnx_model"  # Directory for Optimized ONNX model
  quantized_model_path = "quantized_onnx_model" # Directory to Quantized Model
  
  model = ORTModelForCausalLM.from_pretrained(optimized_onnx_model_path)
  

📚 Learn More

For a full walkthrough and real-world context, check out the article:

👉 Optimizing AI Models for Real-World Performance

🙌 Contributing

Pull requests are welcome! If you have better optimization techniques, benchmarks, or use cases — feel free to fork and contribute and connect with me on any of the platform:

• 🐦 X (Twitter)
• 💼 LinkedIn
• 📬 Mail