Code – 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

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 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 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.

Academic Code Concept Machine Learning Series Uncategorized

June 5, 2024 admin

Deep dive: speed up data processing, Code Instruct 3B, Mixture of expert

This exploration into AI advancement covers PyTorch’s num_workers and pin_memory for data processing, Stability AI’s Stable Code Instruct 3B for AI-driven code generation, and the efficiencies and complexities of Mixture of Experts (MoE) models in AI development. It outlines how these innovations are reshaping the computational landscape for developers and businesses, indicating a stride towards superior efficiency and scalability in machine learning models.

Code Concept paper Series

May 31, 2024 admin

Top papers: Deleting 40% Without Accuracy Drop, Turbo Sketch, AnimateDiff

Recent research by Meta, Cisco, and MIT shows that pruning 40-50% of Large Language Model layers can maintain accuracy, offering faster, cost-effective AI. Novel techniques like Img2Img Turbo Sketch and AnimateDiff-Lightning demonstrate efficient image creation and rapid video generation from text. These developments suggest smaller AI models can be equally effective, heralding a potential shift in resource utilization and training methods.

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Code Technology

May 26, 2024 admin

Technow: Lightning LLM booster, Anthropic Prompt Library, AI Agent

Thunder by Lightning offers a 40% faster training speed for Large Language Models, using operation fusion techniques in PyTorch. Meanwhile, Anthropic’s Claude Library enables better AI chatbot interactions, and AI agents, with their advanced functions including planning, tool use, and collaboration, represent a significant trend in AI development, advocating new frameworks and autonomous capabilities promising to reshape technological progress.