🚀 Transformers Low-Level API | 4-bit Quantization & Memory Optimization | LLM | Code Infinity

Learn how to efficiently run large language models like Llama 3.1, Phi-3, and Gemma 2 on consumer hardware using Hugging Face Transformers' low-level API. This tutorial covers 4-bit quantization, memory optimization techniques, real-time text generation, and a deep dive into transformer internals. Perfect for AI enthusiasts and developers looking to optimize inference speed and memory usage. 📌 GitHub Repository: https://github.com/ankitmalik84/youtube/tree/main/lowLevelApiOfTransformers What you'll learn in this tutorial: Run Llama, Phi-3, and Gemma models on GPUs with just 6GB VRAM Use BitsAndBytesConfig for 4-bit quantization (up to 75% memory savings) Explore transformer architecture internals Implement streaming output for real-time generation Compare model performance and memory usage 💡 If you find this tutorial helpful, like, share, and subscribe for more deep dives into AI and transformers.