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s1 is a reasoning training framework and GPU cluster orchestrator designed to build and refine large language models. It provides a system for executing supervised fine-tuning on distributed hardware, utilizing gradient checkpointing and hardware optimization to improve model reasoning. The project features a synthetic data generator and dataset builder that produce high-quality training sets. This workflow collects questions, generates model reasoning traces, and applies automated grading loops to filter for correct answers. The framework includes an evaluation suite to compute accuracy and
Open-r1 is a framework designed for the large-scale training, distillation, and optimization of language models focused on complex reasoning and programming tasks. It provides a comprehensive suite of tools for managing distributed training jobs across multi-node clusters, enabling the development of high-performance models through reinforcement learning and supervised fine-tuning. The project distinguishes itself by integrating secure, containerized code execution environments directly into the training and evaluation lifecycle. By allowing models to run and verify code snippets against test
Efficient-AI-Backbones is a lightweight neural network library and computer vision model zoo. It provides a collection of optimized deep learning backbones designed to minimize computational overhead and memory usage for artificial intelligence tasks. The project implements specialized architectures such as GhostNet and MLP to reduce processing requirements. It features a modular backbone design and the distribution of pretrained weights to accelerate the development and deployment of vision models. The library covers efficient neural network design and edge device AI optimization. Its capab
GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce computation and memory overhead. It serves as a computer vision backbone and a lightweight vision transformer, optimizing the balance between predictive accuracy and inference speed. The project focuses on reducing resource consumption for deployment on mobile devices and edge hardware. It achieves this through the use of lightweight vision transformer implementations and architectures that minimize the total number of parameters. The codebase covers a range of capabilities for i
TinyRecursiveModels is a recursive training framework for small neural networks designed to solve complex logical tasks. It functions as a parameter-efficient model trainer and a reasoning dataset generator, enabling the optimization of models that refine their answers through iterative reasoning steps.
The main features of samsungsailmontreal/tinyrecursivemodels are: Latent State Recursive Refiners, Latent State Refinement, State Trackers, Iterative Prediction Refiners, Reasoning Model Training Suites, Synthetic Reasoning Data Generators, Efficient Neural Architectures, Recursive Answer Refiners.
Open-source alternatives to samsungsailmontreal/tinyrecursivemodels include: simplescaling/s1 — s1 is a reasoning training framework and GPU cluster orchestrator designed to build and refine large language models.… huggingface/open-r1 — Open-r1 is a framework designed for the large-scale training, distillation, and optimization of language models… huawei-noah/ghostnet — GhostNet provides a set of efficient AI model architectures and neural network design patterns designed to reduce… jiayi-pan/tinyzero — TinyZero is a reinforcement learning framework and implementation designed to train language models to develop… huawei-noah/efficient-ai-backbones — Efficient-AI-Backbones is a lightweight neural network library and computer vision model zoo. It provides a collection… pageman/sutskever-30-implementations — This project is a collection of deep learning research implementations and a reproduction kit designed to translate…