Awesome GitHub RepositoriesSequence Generation

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.

Qwen2.5-VL est un transformeur multimodal autorégressif conçu pour traiter des séquences entrelacées de jetons de texte et visuels. Il intègre des intégrations de caractéristiques visuelles dans un espace de modèle de langage partagé pour effectuer un raisonnement transmodal et générer des réponses cohérentes ou du code de mise en page structuré. Le projet se distingue par la cartographie vision-langage-action, lui permettant de percevoir des interfaces visuelles et de traduire cette perception en commandes exploitables pour faire fonctionner des écrans numériques et du matériel robotique. Il utilise un encodage d'image à résolution dynamique et une indexation vidéo à trame temporelle pour gérer diverses tailles d'image et des séquences visuelles de longue durée. Le modèle couvre une large surface de capacités, notamment la reconnaissance optique de caractères multilingue pour la numérisation de documents, la mise à la terre spatiale pour localiser des objets via des boîtes englobantes, et l'analyse de contenu vidéo long format. Il prend également en charge le raisonnement mathématique multimodal pour résoudre des problèmes en utilisant des graphiques et des diagrammes, et étend sa compréhension à une longueur de contexte d'un million de jetons.

Transformers.js is a JavaScript library and web machine learning framework designed to run pretrained transformer models directly in the browser. It serves as a client-side inference engine and a wrapper for the ONNX Runtime, enabling the execution of multimodal AI tasks on user devices without the need for a backend server. The library distinguishes itself by providing a unified toolkit for processing text, image, and audio data locally. This architecture supports privacy-preserving model inference and reduces latency by performing all computations on the client's hardware. Its capabilities

This project is a large language model and general purpose natural language processing engine designed for text generation and linguistic analysis. It functions as a few-shot learning framework capable of solving diverse reasoning and language tasks using a small number of provided examples without requiring additional training. The system specializes in generating human-like synthetic text and long-form content, including news articles. It also provides capabilities for automated text reasoning to solve logic and arithmetic problems through direct interaction. The project includes tools for

Mistral Inference is a library for running Mistral large language models on a GPU, generating text from prompts with token streaming. It loads pretrained model weights from local disk or a remote registry into GPU memory, then produces output tokens one by one for real-time display in interactive applications. The library supports multimodal prompts that accept image URLs alongside text, enabling visual description and reasoning. It includes content safety guardrails that scan generated text against predefined policies to block or flag policy violations. For structured interactions, it provid

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing neural network architectures and a system for executing training loops and optimizing model weights across CPUs and GPUs. The project simplifies the process of building and training models through a modular interface and a high-level API for prototyping. It includes specialized utilities for deep learning visualization, allowing for the generation of graphical diagrams to analyze network structures, weights, gradients, and activations. The framework covers a broad range of capa

TinyLlama is a compact 1.1B parameter language model pretrained on a dataset of 3 trillion tokens. It is an edge AI model designed for high-performance text generation on memory-constrained devices. The project provides a distributed pretraining framework for training small language models across multiple GPUs and nodes. It also includes a finetuning toolkit for full-parameter weight adjustments to adapt the base model for chat and specific tasks. The system supports distributed large language model training and on-device text generation. Its architectural components include rotary positiona

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

This project is a deep learning text classification framework and neural text analysis library. It provides tools for categorizing textual data, adapting large language models through fine-tuning, and treating classification tasks as sequence generation problems using transformer architectures. The framework distinguishes itself through the implementation of ensemble learning, using boosting to combine predictions from multiple architectures to increase accuracy. It also includes a toolkit for fine-tuning pre-trained models via layer updates and the ability to restore model sessions for real-

GLM-130B is a pre-trained foundation model and bilingual large language model designed for natural language processing tasks in both English and Chinese. It functions as an autoregressive language model and text generator capable of producing long-form content and predicting missing phrases. The model utilizes an autoregressive blank-filling architecture and a bidirectional dense transformer to process text. This approach allows the system to transition between understanding context through masked language modeling and generating sequential text using specific mask tokens. The project covers

CodeGeeX2 is a large language model and AI programming assistant designed to generate, translate, and document source code across multiple programming languages. It functions as a multilingual code model that converts natural language prompts into executable code and technical documentation. The project provides a self-hosted AI inference endpoint, allowing the model to be exposed as a web-accessible service. This enables external development tools to integrate automated programming tasks via network calls. Its core capabilities cover multilingual code generation, automated source code docum

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

fun-rec is a learning guide and framework for building personalized recommendation systems, covering everything from deep learning ranking to generative recommendation paradigms. It provides instructional content on constructing industrial-grade architectures that span offline data processing and real-time online serving. The project distinguishes itself by focusing on generative recommendation, treating the suggestion process as a sequence-to-sequence task using large language models and transformer models to generate item identifiers rather than traditional ranking lists. It also emphasizes

DeepSeek-LLM est un grand modèle de langage et un modèle de langage causal conçu pour la génération de langage naturel. Il fonctionne comme un système multilingue capable de prédire le jeton suivant dans une séquence pour effectuer la complétion de texte et la génération conversationnelle. Le modèle est spécialisé dans le raisonnement logique, spécifiquement en tant que LLM pour le code et les mathématiques. Cela lui permet d'effectuer une résolution de problèmes complexe, ce qui inclut la génération de code exécutable et la résolution d'équations mathématiques par une analyse étape par étape. Les capacités plus larges du système couvrent l'IA conversationnelle, y compris la génération de complétions de chat et de séquences de texte dans plusieurs langues. Sa surface fonctionnelle s'étend à la génération de code automatisée et à la production de texte cohérent pour diverses tâches d'écriture.

This repository collects illustrated single-page cheat sheets that compress the core topics of Stanford's CS 230 deep learning course into visual reference summaries. The collection covers convolutional neural networks, recurrent neural networks, and practical training techniques, pairing schematic diagrams with mathematical notation to bridge intuition and formal understanding. The cheat sheets are organized by subject area and link related concepts across topics, such as connecting vanishing gradients to LSTM gates, to reinforce the full deep learning workflow. Practical training advice on

GLM-4 is a large language model and fine-tuning framework designed for human-like text production, complex reasoning, and multilingual conversation. It functions as a multimodal system capable of processing high-resolution visual content and as a long-context model designed to analyze documents with a context window of up to one million tokens. The project differentiates itself through a function calling interface that enables AI agent development by connecting the model to external APIs and real-time web browsing. It includes specialized capabilities for generating functional programming cod

Tensorpack est un framework de réseau de neurones TensorFlow de haut niveau et une bibliothèque de recherche conçue pour construire et entraîner des modèles de deep learning. Il fournit une collection d'architectures de réseaux de neurones reproductibles pour la vision par ordinateur, les tâches génératives, l'apprentissage par renforcement et le traitement du langage naturel. Le projet se distingue par un pipeline de données de deep learning spécialisé qui utilise du Python pur pour le chargement et le streaming de données en parallèle. Il inclut un orchestrateur d'entraînement multi-GPU pour distribuer les charges de travail via des stratégies de parallélisme de données et un toolkit d'interprétabilité dédié pour visualiser la saillance des modèles et les cartes d'activation. Le framework couvre un large éventail de capacités, incluant des pipelines de vision par ordinateur pour la détection d'objets et la segmentation sémantique, la modélisation de séquences pour la parole et le texte, et le développement d'agents d'apprentissage par renforcement. Il fournit également des outils d'optimisation de modèle pour la quantification des poids et l'entraînement en faible précision, ainsi que des utilitaires pour reproduire des articles de recherche académique et convertir des poids de modèles Caffe legacy.