Lumiere: Space-Time Diffusion Model for Text-to-Video Generation
Dive into Google's Lumiere text-to-video model, exploring its innovative space-time UNet architecture and MultiDiffusion design for creating temporally consistent video generation.
Machine Learning Career Roadmap 2024 - From Beginner to Expert
Discover a comprehensive YouTube learning path for mastering Machine Learning and AI, from mathematical foundations to specialized areas like NLP and Computer Vision - all through free resources.
Simple Quantization of LLMs - A Hands-on Guide to Absolute Max and Zero Point Methods
Master practical LLM quantization techniques through hands-on implementation of Absolute Max and Zero Point methods, building foundational skills for advanced approaches like GPTQ and AWQ.
Getting Started with HuggingFace Transformers - Pipeline, Tokenizer, and Model Development
Master HuggingFace Transformers library fundamentals, from pipelines and tokenizers to models and Hub integration, enabling you to effectively work with and fine-tune Large Language Models.
QLoRA: Efficient Training of Large Language Models Using Quantization and Low-Rank Adaptation
Discover how QLoRA enables efficient training of large language models on a single GPU through innovative techniques like NormalFloat, Double Quantization, and Paged Optimizers.
LoRA: Low-Rank Adaptation for Parameter-Efficient Large Language Model Fine-Tuning
Discover how LoRA enables efficient fine-tuning of large language models through low-rank adaptation, from fundamental concepts to practical implementation in transformer architectures.
Stable Video Diffusion: Model Architecture and Training Pipeline
Dive into the technical architecture and training methodology of Stable Video Diffusion, exploring its three-stage process, data curation pipeline, and practical applications in AI-powered video generation.
ControlNet: Adding Conditional Control to Text-to-Image Diffusion Models
Delve into the groundbreaking ControlNet paper, exploring its architecture, classifier-free guidance, and spatial control capabilities in text-to-image diffusion models through detailed technical explanations.
Understanding Meta AI's Segment Anything Model (SAM) - Architecture, Data Engine, and Results
Dive into Meta AI's Segment Anything Model (SAM), exploring its architecture, innovative data engine, and zero-shot transfer learning capabilities for advanced image segmentation tasks.
Reinforcement Learning-Based Fine-Tuning of Diffusion Models for Drug Discovery
Dive into advanced reinforcement learning techniques for fine-tuning diffusion models, focusing on accelerated drug discovery, bioinformatics, and molecular compound predictions.
Multi-Agent and Multi-Modal AI for Physics-Based Material Design and Discovery
Discover how AtomAgents, a multi-agent AI system, revolutionizes material science through physics-based modeling and advanced machine learning for efficient alloy design and analysis.
Coding Multi-Agent Reinforcement Learning with PyTorch and JAX - Including ReDel and AgentScope Frameworks
Dive into practical multi-agent reinforcement learning implementation using PyTorch and JAX, featuring new frameworks ReDel and AgentScope for advanced distributed systems and LLM-powered simulations.
LoftQ: Understanding LoRA-Fine-Tuning-aware Quantization for LLMs
Dive into LoftQ, a groundbreaking LLM quantization method that combines with LoRA to achieve superior performance and efficiency in large language models.
Stable Diffusion XL with T2I-Adapter and ControlLoRA - Tutorial and Demo
Master advanced image generation techniques using SDXL with T2I adapters and ControlLoRA, featuring hands-on demonstrations and practical implementations for enhanced AI art creation.
Fine-tuning LLama 2 with PEFT, LoRA, 4-bit Quantization, TRL and SFT
Master advanced LLama2 fine-tuning techniques using PEFT, LoRA, and 4-bit quantization while creating synthetic datasets with GPT-4 for customized model training.
Fine-tuning Flan-T5 LLM with HuggingFace Accelerate - Tutorial
Learn how to fine-tune Flan-T5 LLM using HuggingFace Accelerate for multi-GPU environments, with step-by-step guidance on preprocessing and implementation for specific downstream tasks.
Fine-tune T5 Language Model for Text Summarization on Google Colab
Master fine-tuning techniques for T5 language models using HuggingFace's latest code, focusing on text summarization tasks with hands-on implementation in Google Colab's free GPU environment.
Stanford ALPACA 7B LLM: Fine-tuning Guide with Code and Datasets
Discover how to fine-tune and implement Stanford's ALPACA 7B language model using LLaMA, exploring practical applications and cost-effective methods for creating custom AI solutions.
Fine-tuning FLAN-T5 Models for Text Summarization with PyTorch
Master Flan-T5 model fine-tuning for text summarization through hands-on PyTorch implementation, complete with practical code examples and real-time demonstrations.
Fine-tuning T5 LLM for Text Generation and Summarization - Complete Tutorial
Master T5 LLM fine-tuning techniques for text summarization through hands-on coding in Google Colab, using practical examples and real-time implementation with Hugging Face.
Reversible Transformer - GPU Memory Optimization Using ReFORMER and Reversible Residual Layers
Delve into GPU memory optimization techniques for transformers, exploring reversible residual layers and locality-sensitive hashing to enhance deep neural network efficiency and performance.