Concept – Up As Pro

Discover latest project: predicting user scores from interaction logs with a quiz system! By analyzing user behavior and leveraging cutting-edge ML techniques like DistilBERT, curriculum learning, and LoRA, we’ve crafted a robust model to infer scores without needing correct answers or user choices. Dive into our innovative approach and see how we’re revolutionizing educational tools. #MachineLearning #AI #EdTech #Robotics #DistilBERT #CurriculumLearning #LoRA

Concept General Series Technology Videos

July 17, 2024 admin

Deepdive: pytorch profiler, standford transformer, XTuner, Luminal, DeepFaceLive

The PyTorch Profiler analyzes deep learning models’ performance by collecting timing and resource usage stats, helping identify bottlenecks and optimize memory and execution. Stanford’s CS25 lecture series, “Transformers United V4,” covers state-of-the-art transformer research and applications. XTuner offers a flexible toolkit for fine-tuning large models, supporting various algorithms and high training throughput. Luminal optimizes deep learning performance with ahead-of-time compilation and efficient execution on CUDA/Metal APIs. DeepFaceLive allows real-time face swaps from video streams, with options to train custom models and animate static faces.

Academic Code Concept Machine Learning Series

July 10, 2024 admin

Technow: torchtune, Boston Dynamics Atlas robot, Reka Core

Easily fine-tune large language models (LLMs) with torchtune, PyTorch’s new library. Torchtune offers modular and customizable fine-tuning recipes, supporting efficient workflows from data preparation to model evaluation. It integrates with popular tools like Hugging Face Hub and Weights & Biases and excels in memory efficiency, making it ideal for users with limited hardware. Designed for simplicity and extensibility, torchtune supports models like Llama 2 and Mistral, with future plans for models up to 70 billion parameters.

Academic Concept Machine Learning

July 5, 2024 admin

Deepdive: Many-Shot learning, AutoCrawler, Megalodon

Large language models (LLMs) are revolutionizing various fields, but their capabilities can be limited. This is where exciting new research comes in! By tackling challenges like limited learning from examples and information processing constraints, these advancements are making LLMs more adaptable, efficient, and powerful. Let’s dive into three key breakthroughs: Many-Shot In-Context Learning, AutoCrawler, and Megalodon.

Academic Code Concept Machine Learning

July 3, 2024 admin

Deepdive: Custom Collate in DataLoader, Gemini cookbook, Build LLM from scratch

Optimize your large language model (LLM) training with PyTorch by using a custom collate function in DataLoader. This technique dynamically pads text sequences in each batch to match the longest sequence, reducing wasted computation from unnecessary padding. Learn how to implement a custom collate function using pad_sequence from torch.nn.utils.rnn and integrate it with DataLoader for efficient handling of variable-length sequences in your LLM training workflow.

Academic Code Concept Videos

June 26, 2024 admin

Deep Dive: Multiprocessing with Pool, Pure C Implementation of GPT-2, Attention in Transformers

Unlock the power of Python’s multiprocessing library to execute tasks in parallel, effectively utilizing multiple CPU cores and bypassing the Global Interpreter Lock (GIL). By leveraging the Process and Pool classes, you can significantly reduce execution times for parallelizable tasks. Learn how to manage processes and facilitate inter-process communication through practical examples. Discover how multiprocessing can enhance your Python applications’ performance with this comprehensive guide.

Academic Concept paper Series

June 21, 2024 admin

Top papers: CIFAR-10 94% in 3.29 Sec, Gemini Infinite Context Method, Microsoft’s Vasa-1

Achieve 94% accuracy on CIFAR-10 in just 3.29 seconds using a single NVIDIA A100 GPU, scaling up to 96% in 46.3 seconds with advanced techniques. Integrate strategies like patch-whitening, identity initialization, higher learning rate for biases, Lookahead optimization, multicrop TTA, and alternating flip for augmentation. Utilizing torch.compile for efficient GPU usage, this method significantly speeds up ML experiments and reduces costs, showing a 1.9× speed boost over previous records. Learn how these techniques can generalize across small-scale tasks and contribute to rapid model training.

Academic Code Concept paper Series

June 19, 2024 admin

Deep dive: LLM priority for RAG, AIOS, More Agents Is All You Need

Ever wondered if large language models (LLMs) stick to the information they retrieve or if they rely on their internal knowledge? In this video, we dive into a recent paper from Stanford University that explores this very question. Discover the experiments they conducted, the surprising insights they uncovered, and what it means for the future of AI. We’ll also touch on AIOS, a revolutionary LLM Agent Operating System, and how it’s changing the way we interact with machines. Stay tuned to the end for the most interesting revelations about the performance of LLMs with manipulated data!

Code Concept FEATURED Machine Learning

June 14, 2024 admin

Fine-tune Llama 3 with PyTorch FSDP and Q-Lora

In this post, we will cover how to fine-tune Llama 3 70B with PyTorch FSDP, Q-Lora, and Flash Attention 2 (SDPA) using Hugging Face build for consumer-size GPUs (4x 24GB).

Academic Code Concept paper Series

June 7, 2024 admin

Top papers: Voicecraft, T-Rex, Mixture-of-Depths

This article examines three AI innovations: Voicecraft, T-Rex2, and Mixture-of-Depths (MoD). Voicecraft accelerates speech synthesis with a neural codec model, while T-Rex2 enhances zero-shot object detection with combined text and visual prompts. MoD improves processing efficiency by dynamically allocating computation in transformers, potentially cutting computational overhead by 50%. These advancements promise significant impacts across various industries.