This guide helps engineers build and ship LLM products by covering the full technical stack. It moves from core mechanics (tokenization, embeddings, attention) to training methodologies (pretraining, SFT, RLHF/DPO) and deployment optimizations (LoRA, quantization, vLLM). The focus is on managing critical production tradeoffs between accuracy, latency, memory, and cost
This article details the performance of Unsloth Dynamic GGUFs on the Aider Polyglot benchmark, showcasing how it can quantize LLMs like DeepSeek-V3.1 to as low as 1-bit while outperforming models like GPT-4.5 and Claude-4-Opus. It also covers benchmark setup, comparisons to other quantization methods, and chat template bug fixes.
This article announces a comprehensive course on fine-tuning large language models (LLMs) offered on the freeCodeCamp.org YouTube channel. The course, developed by Krish Naik, covers topics such as QLORA, LORA, quantization with LLama2, gradient, and Google Gemma Model, among others. The course aims to help learners deepen their understanding of machine learning and artificial intelligence.
