Artificial Intelligence
AI trends, techniques, and real-world implementations
Sora: OpenAI's Text-to-Video Model and What It Means for Creators
Sora generates 1080p video up to 60 seconds from text prompts using a diffusion transformer on spacetime patches - and its consistency across frames marks a genuine leap over prior text-to-video approaches.
DeepSeek R1: The Open-Source Reasoning Model That Beat o1
DeepSeek R1 achieves 79.8% on AIME 2024 (vs o1-mini at 63.6%) using pure reinforcement learning without supervised fine-tuning, and it's MIT licensed.
Mamba: The State Space Model That Challenges the Transformer
Mamba introduces selective state space models with linear scaling in sequence length and constant-time inference, offering a genuine alternative to the Transformer for long-sequence tasks.
QLoRA: Fine-Tune a 65B LLM on a Single 48GB GPU
QLoRA combines 4-bit quantization with LoRA to make fine-tuning 65B parameter models accessible on consumer hardware, introducing NF4 quantization and paged optimizers.
LoRA Explained: How 0.1% of Parameters Can Match Full Fine-Tuning
LoRA fine-tunes LLMs by training tiny low-rank decomposition matrices instead of updating billions of weights, cutting VRAM requirements from hundreds of GB to a few GB.
RLHF Explained: How InstructGPT Taught GPT-3 to Follow Instructions
InstructGPT showed that a 1.3B model trained with RLHF outperforms 175B GPT-3 on instruction following. Here is how the three-step pipeline actually works.
Attention Is All You Need: What the 2017 Paper Actually Says and Why It Still Matters
The Transformer paper by Vaswani et al. replaced recurrent networks with self-attention and became the foundation of every modern LLM. Here is what the original paper actually says.
Mastering LLM Fine-Tuning with LoRA and QLoRA for Production AI Systems
Optimize enterprise LLMs with parameter-efficient fine-tuning. Master dataset curation, LoRA rank tuning, 4-bit quantization, and evaluation benchmarks.
