Projects sharing features with Step3

MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model.

Megatron-LM is a distributed transformer training library and large language model training framework designed to scale models across thousands of GPUs. It functions as a GPU-optimized deep learning toolkit and a scaling engine for mixture-of-experts architectures, enabling the training of models with hundreds of billions of parameters. The project implements multi-dimensional model parallelism, combining tensor, pipeline, data, expert, and context-based workload distribution. It specifically optimizes mixture-of-experts architectures through integrated memory and communication improvements t

An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

gpt-oss is an open-weight large language model and reasoning engine designed for complex reasoning and agentic workflows. It functions as an AI agent framework and model serving API, allowing for local deployment and the hosting of standardized interfaces to expose model completions and internal reasoning processes. The project distinguishes itself as a quantized inference engine, utilizing tensor parallelism and weight quantization to run high-parameter models on limited hardware. It features a reasoning model that employs chain-of-thought processing to solve multi-step logical tasks. The s

InternVL is a vision-language model framework that fuses a visual encoder with a large language model to translate image features into textual tokens for reasoning. It provides a system for multimodal inference and dialogue, enabling the processing of images and text to answer questions or generate descriptions. The project is distinguished by its high-resolution image processing, which uses dynamic tiling to maintain detail for images up to 4K resolution, and its chain-of-thought visual reasoning for solving complex mathematical and spatial problems. It also supports temporal frame sampling

Qwen2.5-Omni is an omnichannel multimodal large language model designed to process and generate content across text, audio, vision, and video. It functions as a real-time speech AI, utilizing an end-to-end architecture to maintain synchronous voice conversations with low-latency responses. The project emphasizes efficiency through quantized edge models, allowing for local inference on mobile hardware and resource-constrained devices. It employs 4-bit weight quantization, CPU-based process offloading, and on-demand weight loading to reduce GPU memory requirements. The system integrates specia

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✊ Unleashing the Power of Reinforcement Learning for Math and Code Reasoners 🤖

Skywork-R1V4

腾讯混元Turbo S模型已在腾讯云官网上架，开发者和企业用户可以通过API调用，今天开始在腾讯元宝灰度上线，供广大用户使用和体验。

GLM-4.5 is a multimodal large language model and advanced reasoning system. It functions as an AI coding assistant, an autonomous AI agent, and a multimodal content generator capable of processing and generating text, images, audio, and video within a single unified system. The project is distinguished by its deep reasoning capabilities, utilizing chain-of-thought processing to solve complex mathematical, logical, and technical problems. It features an agentic architecture that allows for autonomous task execution, long-horizon goal planning, and the ability to interact with external tools an

👋 Hi, everyone! We are ByteDance Seed Team.

GLM-4.6V/4.5V/4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Explanation to key concepts in ML

🤗Intern-S2 Model Collections • 🤗Intern-S1 Model Collections • ModelScope • 📜Technical Report(S1) • 📜Technical Report(S1-Pro) • 💬Online Chat

This project is a comprehensive, community-driven repository that serves as a centralized catalog for computer vision research and development. It functions as a structured index of academic papers, open-source software libraries, public datasets, and educational tutorials, providing a navigation point for the complex landscape of modern vision technology. The repository distinguishes itself through a taxonomy-based indexing system that maps the relationships between foundational research, influential academic figures, and their corresponding software implementations. By utilizing a lightweig