Medium·2yUnderstanding LLMs from scratch using middle school math
This post explains how large language models (LLMs) function using basic math concepts. It covers various components like neural networks, embeddings, self-attention, softmax, and the GPT and transformer architectures. The approach is highly educational, using simplified explanations and visual aids to make the concepts accessible to those with minimal mathematical background.
3Blue1Brown·2yLarge Language Models explained briefly
The post explains large language models (LLMs), how they function, and the complexities behind their training. LLMs predict the next word in a sequence based on probabilities, using vast amounts of text data for training. The introduction of transformers in 2017 allowed for parallel processing of text, enhancing computation efficiency. Pre-training is supplemented by reinforcement learning with human feedback to refine model predictions. The sheer scale of data and computation involved is formidable, taking advantage of specialized hardware like GPUs.
gitconnected·2yLet’s Build our own GPT Model from Scratch with PyTorch
Learn how to build a basic Generative Pre-trained Transformer (GPT) model from scratch using PyTorch. This tutorial covers auto-regressive models, character-level tokenization, data batching, and training using text in the style of William Shakespeare. It provides a detailed implementation of a bi-gram language model including the use of multi-head attention, forward and training operations, and generating new text tokens.
GoPenAI·2yA Step-by-Step Guide to Creating a Large Language Model from scratch…
This post provides a step-by-step guide to creating a Large Language Model (LLM) from scratch using the Transformer architecture and TensorFlow/Keras. It also explains how to implement transfer learning with Hugging Face.
Machine Learning News·2yFrom RAG to ReST: A Survey of Advanced Techniques in Large Language Model Development
Large Language Models (LLMs) face challenges like temporal limitations, complex computations, and inaccuracies. Researchers are integrating LLMs with external data sources to address these issues. Transformer architecture, with self-attention mechanisms, has outperformed previous models. Various transformer-based models serve specific tasks. Techniques like RAG and PAL enhance LLMs' real-time information access and computational accuracy. Fine-tuning methods like LoRA and prompt tuning make LLMs more efficient. Reinforcement Learning techniques like RLHF and ReST are used for aligning models with human preferences. Scaling and fine-tuning strategies are discussed for improved model performance.
Hugging Face·2yFine-tuning LLMs to 1.58bit: extreme quantization made easy
As large language models (LLMs) grow, reducing their computational and energy costs via quantization becomes crucial. BitNet, a new transformer architecture from Microsoft Research, drastically cuts computational costs by representing parameters with ternary values (-1, 0, 1) at 1.58 bits per parameter. The post details how existing models, like Llama3, can be fine-tuned using BitNet, achieving efficient performance while maintaining accuracy. The article also covers the implementation, optimization, and benchmarking of custom inference kernels, making LLMs more scalable and practical.
bycloud·2yHow A State-of-the-Art AI Chatbot Is Made [ft. Llama-3.1 405B]
The Llama 3.1 AI model by Meta is touted as an engineering marvel rather than a groundbreaking research piece. This state-of-the-art language model boasts 405 billion parameters, making it slightly superior to ChatGPT and nearly as good as the leading model, Claude 3.5. Unlike previous versions, Llama 3.1 focuses on extensive engineering details and optimization techniques like Group-Query Attention and 4D parallelism. Meta has provided an in-depth 90-page research paper explaining their training process, which is now publicly available and indicates that with enough resources, the model can be replicated or downloaded for free.
Machine Learning Mastery·2y5 Essential Free Tools for Getting Started with LLMs
This post introduces 5 essential free tools for getting started with LLMs: Transformers, LlamaIndex, Langchain, Ollama, and Llamafile. Each tool has its own unique set of tasks, advantages, and features to help beginners grasp the subtleties of LLM development and interact with it.
Hacker News·2yJoinMusic/fish: YouTube video to chords, lyrics, beat and melody.
An AI-powered multimodal project employs various transformer models to generate chords, beats, lyrics, melody, and tabs for any song from YouTube videos. The system includes models like U-Net for audio separation, Pitch-Net for melody tracking, Beat-Net for tempo tracking, and Chord-Net for chord recognition. It supports multiple languages and allows for editable sheet music creation. Utilizing a combination of STFT, MFCC, and chroma features, it ensures better generalization with minimal training data.
Daily Dose of Data Science | Avi Chawla | Substack·2yThe Evolution of Embeddings
The post discusses the evolution of embeddings in natural language processing. It explores the shift from static embeddings like Glove and Word2Vec to contextualized embeddings powered by Transformer models such as BERT, DistilBERT, and ALBERT. The latter can generate context-aware representations, addressing limitations where a word's meaning changes based on context. Examples and comparisons illustrate how these models capture word semantics and syntactics more effectively.
Towards AI·2yTransformer Architecture Part -1
Transformers have revolutionized deep learning, excelling in language and vision tasks. The core architecture consists of identical encoder and decoder blocks, each featuring self-attention, feed-forward neural networks, add & norm layers, and residual connections. The process begins with tokenization, text vectorization, and positional encoding. Multi-head attention then contextualizes these vectors, followed by normalization and passing through feed-forward networks. The architecture ensures efficient handling of complex data patterns while maintaining consistent dimensionality for smooth training.
Machine Learning News·2yLaMMOn: An End-to-End Multi-Camera Tracking Solution Leveraging Transformers and Graph Neural Networks for Enhanced Real-Time Traffic Management
Researchers from the University of Tennessee at Chattanooga and Leibniz University Hannover developed LaMMOn, a multi-camera tracking model using transformers and graph neural networks. LaMMOn integrates modules for object detection, tracking, trajectory clustering, and generating object embeddings from text. It addresses challenges in manual labeling and new matching rules, achieving high performance on datasets like CityFlow and TrackCUIP with competitive real-time processing speeds.
Towards Data Science·2yUnderstanding Transformers
Transformers, introduced in 2017, revolutionized sequence transduction models by relying entirely on the attention mechanism and allowing for parallel processing, which significantly improved training efficiency and long-term dependency handling compared to previous models like RNNs, LSTMs, and CNNs. Key components of a transformer include tokenization, embedding, the attention mechanism, the encoder, and the decoder. GPT models, which stem from transformers, focus on generative tasks and omit the encoder stack, demonstrating high effectiveness in tasks like generating text after being pre-trained on large corpora of text.
freeCodeCamp·2yHow to Get Started with Hugging Face – Open Source AI Models and Datasets
Hugging Face is a platform that offers AI models, datasets, and demo apps. It allows users to collaborate, create their own models, and use existing datasets. The platform also offers tools for learning AI skills and creating portfolios. To get started, users need to create a Hugging Face account and set up their development environment. They can then use pre-trained models in Hugging Face by visiting the PyPI page, downloading and using the models, and using the pipeline() method.
GoPenAI·2yTransformer from Scratch in TF Part 1: Embedding and Positional Encoding
This post, the first part of a series, explores how to build a Transformer model from scratch using TensorFlow 2, focusing on embedding and positional encoding. It covers text tokenization using TensorFlow's TextVectorization layer, transforming text into numerical formats, and embedding words into vectors for machine language comprehension. The post also explains positional encoding to incorporate sequence information into embedding outputs, essential for the Transformer architecture. Through code demonstrations and visualizations, key concepts are clarified. Future posts will explore the Scaled Dot-Product Attention mechanism, a pivotal component of Transformers.
Real Python·2yHugging Face Transformers Quiz – Real Python
Test your understanding of Hugging Face Transformers with this 6-question interactive quiz. This popular library is used for transformer models in natural language processing, computer vision, and other machine learning tasks. There's no time limit and you'll receive a score at the end, with a maximum of 100%. Good luck!
GoPenAI·2yA Comprehensive Analysis of LoRA Variants
LoRA (Low-Rank Adaptation) techniques optimize large language models by significantly reducing trainable parameters while maintaining performance. Variants like DoRA, QLoRA, AdaLoRA, and HyperLoRA offer enhanced flexibility, computational efficiency, and adaptability for different tasks. Each variant has its specific pros and cons, and the choice depends on factors like task complexity, available computational resources, and memory constraints.
AIModels.fyi·2yGet ready to lose to Transformers on Lichess
An innovative study trains large transformer models to play chess by generalizing strategies rather than memorizing moves, using a dataset called ChessBench with 10 million human games. These transformers achieved near-grandmaster level without search-based tactics, showing potential to revolutionize AI in strategic planning tasks.
Community Picks·2yBART Model for Text Summarization
BART (Bidirectional and Auto-Regressive Transformers) is a pre-training method combining the strengths of BERT and GPT models. It's designed as a denoising autoencoder useful for various NLP tasks, especially text summarization. BART follows a sequence-to-sequence paradigm, excelling in both comprehension and fine-tuned text generation tasks. HuggingFace provides easy access to pre-trained BART models for text summarization.
Towards AI·2yTransformers For Images!!
This post explores the application of transformers in image processing within the field of computer vision, detailing three main methods: Pixel Transformers, Vision Transformers (ViT) by Google Brain, and Swin Transformers by Microsoft. It highlights the limitations of CNNs and offers solutions to computational inefficiencies, such as using image patches and techniques like window attention and hierarchical patches.
KDnuggets·2yHow to Build and Train a Transformer Model from Scratch with Hugging Face Transformers
A step-by-step guide for building and training a transformer-based language model using Hugging Face Transformers. The process covers installing necessary libraries, loading and tokenizing the dataset, initializing and configuring the model (BERT for sequence classification), setting up the training loop with TrainingArguments and Trainer, and finally training the model. Emphasis is given on the computational resources required for training and troubleshooting common issues.
Stack Overflow Blog·2yExplaining generative language models to (almost) anyone
Generative AI has gained significant attention, making it crucial for researchers and engineers to communicate its nuances clearly. Generative language models use the transformer architecture, self-supervised learning for pretraining, and alignment techniques to meet human expectations. Understanding these components helps demystify AI and prevents public skepticism and overly-restrictive regulations.
GoPenAI·2yTransformer from Scratch in TF Part 2: Encoder
This post provides a detailed, step-by-step explanation of the Transformer Encoder Block using TensorFlow, focusing on the Multi-Head Attention mechanism. It covers the creation of Queries, Keys, and Values, the Scaled Dot-Product Attention mechanism, and the addition of residual connections and Layer Normalization. The final component, the Feed-Forward Network (FFN), is also detailed. Code examples in TensorFlow are provided throughout to illustrate key concepts.