EasyR1

EasyR1 este un sistem distribuit de antrenare a modelelor și un framework de învățare prin consolidare (reinforcement learning) pentru modele mari de limbaj și modele multimodale (text-imagine). Funcționează ca un antrenor multimodal și o implementare a unui pipeline de Proximal Policy Optimization, conceput pentru a rafina capacitățile de raționament și percepție ale modelelor care procesează atât text, cât și imagini.

Sistemul se specializează în distribuirea sarcinilor de învățare prin consolidare pe mai multe noduri de calcul pentru a gestiona cerințele mari de memorie. Optimizează utilizarea hardware-ului prin antrenare fără padding și fine-tuning pentru a potrivi modele mari pe unitățile de procesare grafică (GPU) disponibile.

Framework-ul acoperă învățarea prin consolidare și orchestrarea modelelor de recompensă, inclusiv fluxuri de lucru de învățare prin consolidare din feedback uman (RLHF). Suprafața sa tehnică include paralelismul distribuit al datelor, antrenarea cu precizie hibridă și pipeline-uri de intrare multimodale pentru date intercalate de text și imagine.

Proiectul include utilitare pentru recuperarea stării bazată pe checkpoint-uri și se integrează cu instrumente externe de logare pentru urmărirea progresului antrenării și a metricilor de performanță.

Features

Multimodal Reinforcement Learning - Uses RL algorithms to refine the outputs of vision and language models through scalable training.
Data-Parallel Training - Synchronizes model gradients across multiple compute nodes to enable training of models exceeding single-node memory.
Distributed Training - Provides a framework for distributing large-model RL workloads across multiple compute nodes.
Multi-Node Training Scaling - Distributes large-scale model training across multiple hardware nodes to manage GPU resources and memory.
Multimodal Model Trainers - Implements a training pipeline designed to optimize reasoning and perception in multimodal vision-language models.
Multimodal Training Pipelines - Provides end-to-end workflows for processing interleaved text and image data streams for vision-language model training.
Distributed Training - Scales the training of large language models across multiple compute nodes to increase processing speed.
RL Post-Training - Offers a scalable system for RL post-training of large language and vision-language models.
Vision-Language Training - Runs reinforcement learning pipelines to improve reasoning and perception in models processing both text and images.
Reinforcement Learning Training Pipelines - Orchestrates scalable reinforcement learning pipelines to improve reasoning in multimodal models.
RLHF Training Pipelines - Coordinates the interaction between policy models, reward models, and value functions for iterative model refinement.
PPO Implementations - Provides a concrete implementation of Proximal Policy Optimization for refining generative multimodal models.
Checkpoint-Based Recovery - Implements mechanisms to save and restore model weights and optimizer states for training stability.
Sequence Packing - Packs variable-length sequences into single dense tensors to eliminate wasteful compute cycles during training.
Mixed Precision Training - Employs mixed-precision floating point formats to reduce graphics memory usage and accelerate training.
Large Model Optimizations - Reduces hardware requirements through padding-free training and fine-tuning to fit large models on available GPUs.
Reinforcement Learning Optimizers - Executes reinforcement learning algorithms using text and image datasets to refine model outputs.
Training Checkpointing - Saves training progress and state to ensure fault tolerance and the ability to resume training.
Training Memory Optimizers - Implements padding-free training and fine-tuning techniques to reduce graphics memory requirements for large-scale model training.
Reasoning Models - User-friendly framework for reasoning model training.
Reinforcement Learning Frameworks - Simplified training pipeline for reasoning-focused models.

Istoric stele

hiyougaEasyR1

Name: hiyouga/easyr1
Author: hiyouga

View on GitHub

5,034 stele372 fork-uriPythonApache-2.015 vizualizăriverl.readthedocs.io

EasyR1

Features

Multimodal Reinforcement Learning - Uses RL algorithms to refine the outputs of vision and language models through scalable training.
Data-Parallel Training - Synchronizes model gradients across multiple compute nodes to enable training of models exceeding single-node memory.
Distributed Training - Provides a framework for distributing large-model RL workloads across multiple compute nodes.
Multi-Node Training Scaling - Distributes large-scale model training across multiple hardware nodes to manage GPU resources and memory.
Multimodal Model Trainers - Implements a training pipeline designed to optimize reasoning and perception in multimodal vision-language models.
Multimodal Training Pipelines - Provides end-to-end workflows for processing interleaved text and image data streams for vision-language model training.
Distributed Training - Scales the training of large language models across multiple compute nodes to increase processing speed.
RL Post-Training - Offers a scalable system for RL post-training of large language and vision-language models.
Vision-Language Training - Runs reinforcement learning pipelines to improve reasoning and perception in models processing both text and images.
Reinforcement Learning Training Pipelines - Orchestrates scalable reinforcement learning pipelines to improve reasoning in multimodal models.
RLHF Training Pipelines - Coordinates the interaction between policy models, reward models, and value functions for iterative model refinement.
PPO Implementations - Provides a concrete implementation of Proximal Policy Optimization for refining generative multimodal models.
Checkpoint-Based Recovery - Implements mechanisms to save and restore model weights and optimizer states for training stability.
Sequence Packing - Packs variable-length sequences into single dense tensors to eliminate wasteful compute cycles during training.
Mixed Precision Training - Employs mixed-precision floating point formats to reduce graphics memory usage and accelerate training.
Large Model Optimizations - Reduces hardware requirements through padding-free training and fine-tuning to fit large models on available GPUs.
Reinforcement Learning Optimizers - Executes reinforcement learning algorithms using text and image datasets to refine model outputs.
Training Checkpointing - Saves training progress and state to ensure fault tolerance and the ability to resume training.
Training Memory Optimizers - Implements padding-free training and fine-tuning techniques to reduce graphics memory requirements for large-scale model training.
Reasoning Models - User-friendly framework for reasoning model training.
Reinforcement Learning Frameworks - Simplified training pipeline for reasoning-focused models.

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Întrebări frecvente

Ce face hiyouga/easyr1?

Care sunt principalele funcționalități ale hiyouga/easyr1?

Principalele funcționalități ale hiyouga/easyr1 sunt: Multimodal Reinforcement Learning, Data-Parallel Training, Distributed Training, Multi-Node Training Scaling, Multimodal Model Trainers, Multimodal Training Pipelines, RL Post-Training, Vision-Language Training.

Care sunt câteva alternative open-source pentru hiyouga/easyr1?

Alternativele open-source pentru hiyouga/easyr1 includ: internlm/xtuner — xtuner is a comprehensive training engine for large language models, offering a toolkit for pre-training, supervised… zhaochenyang20/awesome-ml-sys-tutorial — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning… open-mmlab/mmagic — mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and… mlfoundations/open_clip — Open CLIP is an open source framework for training and deploying Contrastive Language-Image Pre-training models. It… pytorch/torchtune — Torchtune is a PyTorch-native library for fine-tuning, aligning, and quantizing large language models. It provides a… inclusionai/areal — AReaL is a system for agent orchestration, distributed model training, and parameter-efficient tuning. It provides a…

EasyR1

Features

Istoric stele

EasyR1

Features

Alternative open-source pentru EasyR1

InternLM/xtuner

zhaochenyang20/Awesome-ML-SYS-Tutorial

open-mmlab/mmagic

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Întrebări frecvente

Istoric stele

Alternative open-source pentru EasyR1

InternLM/xtuner

zhaochenyang20/Awesome-ML-SYS-Tutorial

open-mmlab/mmagic

mlfoundations/open_clip

Întrebări frecvente