Projects sharing features with Llms From Scratch Cn

This project is a Transformer machine translation model and attention-based neural network implemented using the PyTorch deep learning framework. It functions as a text-to-text translation tool designed to convert source sequences into target language text. The implementation focuses on neural machine translation, covering the development of sequence-to-sequence architectures. It includes the full pipeline for translation, from text sequence preprocessing and vocabulary creation to model training and text generation inference. The system incorporates standard transformer components such as a

This project is a collection of deep learning research implementations and a reproduction kit designed to translate theoretical AI papers into working code. It provides a library of neural network architectures and reference implementations for reproducing seminal research concepts through interactive notebooks. The repository distinguishes itself through the implementation of AI theory and scaling laws, covering complexity dynamics, information theory, and the simulation of universal AI agents. It also includes a benchmarking suite for synthetic reasoning, allowing for the evaluation of mode

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Foundations-of-LLMs is an educational curriculum and technical resource designed to explain the mathematical and computational principles behind modern generative language models. It provides a structured guide for developers and practitioners to master the fundamental concepts, architectural designs, and training methodologies that enable these systems to function. The project covers the core mechanisms of transformer-based sequence modeling, including self-attention, subword tokenization, and autoregressive generation. It details the technical frameworks used in natural language processing

This project is an educational resource focused on the internal mechanics and design principles of transformer-based neural networks. It provides a structured guide to the fundamental components of generative artificial intelligence, including sequence modeling, semantic embeddings, and the mathematical foundations of large language models. The repository distinguishes itself through a heavy emphasis on visual documentation, utilizing diagrams and step-by-step explanations to clarify how data flows through complex neural architectures. It serves as a technical reference for developers seeking

This project is an educational resource and comprehensive guide for implementing and deploying deep learning models using the PyTorch framework. It provides a structured learning curriculum consisting of tutorials and notebooks that cover neural network architectures, data pipelines, and model optimization across multiple AI domains. The curriculum includes practical implementation guides for building convolutional networks, transformers, and recurrent models. It specifically focuses on workflows for computer vision, including image classification, object detection, and segmentation, as well

This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l

This project is an educational resource and software architecture framework focused on the technical foundations of large language model engineering. It provides a collection of guides and design patterns for building and maintaining professional, scalable systems using large language models. The resource outlines practical implementation patterns for orchestrating workflows that combine prompt engineering, model calls, and vector databases. It focuses on transforming prompt development into a structured engineering process to ensure reliable model outputs in production environments. The cov

PlugNPlay-Modules is a collection of reusable PyTorch computer vision modules and deep learning architectural components. It provides a library of standardized building blocks for constructing neural networks, focusing on attention mechanisms, signal processing layers, and feature fusion modules. The project is distinguished by its extensive variety of attention primitives, covering spatial, channel, and temporal weighting, as well as specialized variants like deformable, frequency-enhanced, and linear-complexity attention. It also implements advanced signal processing tools within the neural

This project is a PyTorch implementation of an attention-based neural network designed for sequence-to-sequence deep learning tasks. It serves as a library for constructing deep learning sequence models that utilize encoder and decoder structures to process natural language and sequential data. The implementation centers on a multi-head attention mechanism to capture diverse relationships between tokens without using recurrence. It includes sinusoidal positional encoding to maintain sequence order and point-wise feed-forward networks to transform token positions independently. The architectu

Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as self-contained Jupyter notebooks. It provides from-scratch constructions of foundational architectures including a complete Transformer model built from the original paper specification, a denoising diffusion probabilistic model for image generation, and a ReAct-style autonomous agent framework that equips an LLM with tools for planning and multi-step task execution. The project distinguishes itself by covering the full lifecycle of modern AI systems through hands-on implementa

This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation. The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image loc

Lit-llama is a PyTorch-based implementation framework for the LLaMA language model, providing a system for pre-training, fine-tuning, and high-performance inference. It includes a pre-training pipeline for creating foundational language models from scratch and tools for running pretrained weights to generate natural text and predict sequences. The project provides specialized toolkits for parameter-efficient fine-tuning using low-rank adaptation and lightweight adapters. It also includes a quantization library that reduces model memory footprints through four-bit and eight-bit precision to en

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

This project is a high-performance library for converting raw text into tokens and IDs for machine learning models. It functions as a fast text encoder and a text preprocessing pipeline designed to transform strings into numerical representations with high throughput for research and production. The library includes a subword tokenizer trainer used to analyze text datasets and create custom vocabularies using algorithms such as byte-pair encoding and wordpiece. It provides capabilities for subword vocabulary training and text alignment, allowing character offsets to be tracked during normaliz

gpt-neox is a distributed training system and framework for building large-scale autoregressive language models. It implements the transformer architecture and provides a toolkit for training models with billions of parameters by distributing weights across compute clusters. The framework distinguishes itself through extensive support for distributed model parallelism, including pipeline and sequence parallelism, to overcome single-device memory limits. It further supports sparse model architectures using a mixture of experts system with Sinkhorn-based routing. The project covers a broad ran

Qwen is a comprehensive framework for large language model development, serving, and deployment. It provides a complete ecosystem for transformer-based sequence modeling, offering base models alongside specialized tools for instruction-tuned alignment, fine-tuning, and long-context inference. The project is designed to support both research and production environments, enabling users to train, optimize, and host generative models locally or across distributed hardware. The framework distinguishes itself through its focus on high-performance serving and extensibility. It features a high-perfor

llm-foundry is a training framework for large language models, providing a system for foundation model pre-training and supervised fine-tuning. It includes a distributed trainer for scaling workloads across multiple nodes and GPUs, a dataset streaming pipeline for loading data from cloud storage, and a parameter-efficient fine-tuning implementation. The framework distinguishes itself through its use of parameter sharding and high-throughput data streaming to maintain stability during large-scale training. It incorporates low-rank adaptation to reduce computational costs and uses eight-bit flo

This project is a TensorFlow implementation of a transformer model, providing a text-to-text deep learning framework designed to recognize and generate sequence patterns. It functions as an attention-based sequence model and a neural machine translation framework for converting text from one language to another. The system implements the transformer network architecture, utilizing multi-head attention and positional encoding to process sequential data. It provides the necessary tools for transformer model training and machine translation inference, allowing for the execution of trained models

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

FlashMLA is an LLM attention kernel library and inference acceleration library providing a collection of high-performance CUDA kernels. It implements multi-head latent attention mechanisms designed to reduce memory overhead and increase throughput during the forward and backward passes of large language model inference. The library utilizes quantized cache attention kernels to improve computation efficiency across both sparse and dense token processing. It specifically optimizes the prefill and decoding phases of model inference through these latent attention implementations. The project cov

This project is a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.

tiny-llm is a large language model inference engine and transformer model implementation. It serves as a quantized model runtime and paged key-value cache manager, providing a specialized inference stack optimized for Apple Silicon. The system distinguishes itself through high-throughput execution techniques, including continuous batching and paged attention. It utilizes a paged memory system to eliminate fragmentation during token generation and employs on-the-fly dequantization of compressed weights to reduce the memory footprint during matrix multiplication. The project covers a broad ran

This project provides a comprehensive educational curriculum and research resource for deep learning, focusing on the theoretical and technical foundations of neural network implementation. It serves as a structured academic guide for building and training complex models from scratch, covering the essential mathematical primitives, computational graph construction, and automatic differentiation mechanisms required for modern machine learning. The repository distinguishes itself through its extensive coverage of generative modeling and specialized neural architectures. It includes practical im

YaLM-100B is a large language model and open-weights AI model designed for generating and processing natural language text. It functions as a multilingual text generator optimized for producing and understanding human language content specifically in English and Russian. The model is built for large scale language modeling and open source AI research, providing a foundation for text-based machine learning tasks. It utilizes a decoder-only transformer architecture with a multilingual embedding space to map English and Russian text into a shared vector space. Its broader capabilities cover nat

This project is a collection of educational examples and code for implementing deep learning architectures using the PyTorch framework. It serves as a tutorial and implementation guide for building various neural network architectures for machine learning tasks. The project provides practical implementations for computer vision, including image classification and neural style transfer, as well as natural language processing examples for building sequence models and language predictors. It also covers generative models using adversarial and variational networks to synthesize or transform visua

MMSegmentation is an open-source semantic segmentation toolbox built on PyTorch that provides a modular, configurable framework for building, training, evaluating, and deploying segmentation models. At its core, it offers a config-driven pipeline that assembles training, evaluation, and inference workflows by parsing hierarchical configuration files, with a modular component registry that enables plug-and-play composition of neural network modules, optimizers, datasets, and metrics. The framework supports the full model lifecycle through a unified runner interface that controls training, testi