Quantizing LLMs and Converting to GGUF Format for Faster and Smaller Models

Learn how to optimize large language models (LLMs) through quantization in this comprehensive video tutorial that demonstrates the process of converting and deploying models for resource-constrained environments. Master the techniques of model quantization using llama.cpp and Ollama while comparing performance metrics between full-precision and quantized versions. Follow along with hands-on demonstrations covering Google Colab setup, llama.cpp installation, GGUF conversion, model evaluation, and integration with Ollama. Gain practical insights into reducing model size and improving inference speed without significant accuracy loss, using a fine-tuned Llama 3.2 model as an example. Perfect for developers and machine learning practitioners looking to efficiently deploy LLMs on devices with limited computational resources.