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modelscope/ms-swift

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Ms Swift

This project is a comprehensive toolkit designed for the full lifecycle management of large language and multimodal models. It functions as a unified orchestrator that handles the entire development process, ranging from dataset preparation and supervised fine-tuning to advanced reinforcement learning alignment and production-ready inference deployment.

The platform distinguishes itself through a specialized reinforcement learning library that supports complex optimization algorithms, including group relative policy optimization and leave-one-out techniques, to improve model instruction-following and safety. It provides extensive support for training stability through sequence-level importance sampling, token-level loss normalization, and uncertainty-based weighting, ensuring reliable policy updates during the alignment phase.

Beyond its core training capabilities, the framework integrates high-performance inference backends and model quantization to facilitate efficient production access. It supports diverse data modalities—including text, image, video, and audio—and offers a modular interface for registering custom model architectures, dialogue templates, and training callbacks. Users can manage these complex workflows through a centralized configuration system or a web-based graphical interface that simplifies task execution and performance monitoring.

Features

  • Large Language Model Fine-Tuning Frameworks - The platform adapts pre-trained language and multimodal models to specific tasks using various training techniques including supervised fine-tuning and parameter-efficient methods.
  • Reinforcement Learning Alignment - The platform optimizes model performance through alignment techniques such as PPO, DPO, and GRPO to improve reasoning and instruction-following capabilities during training.
  • LLM Fine-Tuning Engines - A comprehensive toolkit for supervised fine-tuning, reinforcement learning, and alignment of large language and multimodal models.
  • Multimodal Training Platforms - A framework that supports the training and inference of models across text, image, video, and audio data modalities.

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  • Reinforcement Learning Alignment - Optimizes model behavior through iterative policy updates and reward-based feedback loops to improve instruction following and safety.
  • Training Pipelines - The platform coordinates the entire model lifecycle, including dataset preparation, training, evaluation, quantization, and model distribution through a unified interface.
  • Data-Parallel Training - Scales large-scale model training across multiple hardware resources using integrated parallelization frameworks.
  • Model Training Pipelines - Coordinates the entire model lifecycle from dataset preparation and preprocessing to training, evaluation, and distribution.
  • Transformer Reinforcement Learning Libraries - A platform for optimizing model alignment using PPO, DPO, GRPO, and RLOO algorithms with integrated training stability techniques.
  • Machine Learning Pipelines - Coordinates the entire model lifecycle including dataset preparation, training, and evaluation through a unified interface.
  • Model Training Dashboards - The platform configures, launches, and monitors model training and deployment tasks through a graphical interface while maintaining persistent background processes for continuous operation.
  • Large-Scale Training Frameworks - Distributes large-scale model training across multiple hardware resources using parallel processing frameworks.
  • Large Scale Training Suites - Scaling large-scale model training across multiple hardware resources using parallel processing frameworks and optimized memory management.
  • Preference-Based Model Alignments - The platform trains models using reinforcement learning by sampling multiple outputs per prompt and calculating advantages based on normalized reward statistics to improve response quality.
  • Training Progress Monitoring - The platform tracks and logs comprehensive performance statistics, including reward distributions, divergence, and entropy, to evaluate model training progress.
  • High-Performance Inference Modes - Serves fine-tuned models using optimized kernels and quantization for efficient production access.
  • Model Quantization Frameworks - A training suite that optimizes memory usage and performance through model quantization and high-performance hardware-specific kernels.
  • Model Quantization - The platform lowers the memory footprint and accelerates inference speeds by applying compression methods to model weights during the loading or export process.
  • Multimodal Training - Integrates text, image, video, and audio data into training pipelines by mapping media content to model inputs.
  • Reinforcement Learning Optimizers - The platform trains models using group relative policy optimization to improve stability by calculating relative advantages within groups and incorporating divergence penalties.
  • Leave-One-Out Advantage Estimators - The platform trains models using reinforcement learning by calculating an unbiased advantage baseline through the leave-one-out technique to improve the quality of generated outputs.
  • Training Stability Techniques - The platform applies a specific loss function during reinforcement learning to stabilize policy updates by constraining the divergence between current and reference model distributions.
  • Configuration-Driven Orchestrators - Orchestrates complex training and deployment workflows using centralized configuration files and metadata.
  • Model Performance Benchmarking - The platform assesses model quality using standard evaluation backends to measure accuracy and performance on specific datasets and benchmarks.
  • Model Inference and Serving - Serving fine-tuned models for production use through high-performance backends with support for quantization and streaming interfaces.
  • Model Inference Servers - Launches a dedicated web application for model inference that handles loading and provides a streaming interface.
  • Data Preprocessing - Transforms raw data into model-ready formats using registered custom functions and standardized mapping logic.
  • Model Performance Optimization - The platform reduces memory usage and increases processing speed by applying quantization and hardware-specific kernels to improve efficiency during training and inference tasks.
  • Language Model Development - Lightweight infrastructure for deep learning model fine-tuning.
  • Training Visualization Interfaces - The platform displays real-time logs and performance charts including loss, accuracy, and learning rates during active training sessions within the interface.
  • Plugin-Based Architectures - Extends core functionality by allowing users to register custom model architectures, reward functions, and training callbacks.
  • Custom Model Integrations - Integrates external model architectures and loading logic into the pipeline via metadata definitions.
  • Custom Training Loops - Extends core functionality by registering custom model architectures, datasets, and training callbacks for specialized research.
  • Sequence Importance Sampling - The platform adjusts importance sampling weights at the sequence level rather than the token level to stabilize gradient estimates and prevent training collapse during reinforcement learning.
  • Dataset Preprocessing Utilities - Converts various local or remote data formats into a standardized structure for training and inference.
  • Gradient Optimization Techniques - The platform applies a temperature-controlled soft gate function to smooth gradient attenuation during off-policy training for improved model stability.
  • Inference Acceleration Techniques - The platform speeds up the generation of text completions during reinforcement learning by integrating high-performance inference engines directly into the training loop.
  • Reinforcement Learning Data Filters - The platform excludes responses that were forcibly truncated during generation to prevent reward noise from negatively impacting the learning process of the model.
  • Policy Clipping - The platform adjusts upper and lower bounds of policy update limits independently to encourage model exploration while maintaining training stability during reinforcement learning.
  • Dataset Configuration Systems - Manages dataset sources, subsets, and column mappings through centralized configuration files.
  • Agentic Tool-Use Frameworks - Structures training data for tool-use tasks by mapping tool definitions and interaction sequences into standardized formats.
  • Reinforcement Learning Sampling - The platform skips samples with uniform rewards and continues generation until a diverse batch is achieved to prevent vanishing gradients during the training process.
  • Loss Aggregation - The platform calculates training loss based on individual tokens rather than entire sentences to eliminate bias introduced by varying response lengths during training.
  • Dataset Management Tools - Provides access to a curated library of datasets with pre-calculated token statistics for model training.
  • Streaming Inference Processors - Enables incremental streaming of model responses for real-time token consumption during inference.
  • Length Penalization - The platform imposes multi-stage penalties on generated outputs that exceed defined length thresholds to improve control over the size and efficiency of model responses.
  • Loss Weight Schedulers - The platform adjusts the influence of supervised signals over time by decaying the balancing coefficient from a peak value to a final target value.
  • Custom Preprocessing Registrations - Defines specialized logic for transforming raw data into model-ready formats by registering custom functions.
  • Dialogue Interaction Engines - Configures custom conversation formats by specifying prompt structures, turn separators, and system message handling.
  • Graphical User Interfaces - The platform provides a web-based dashboard for configuring and executing training and deployment tasks without writing code to lower the barrier for model development.

    • Star 历史

    modelscope/ms-swift 的 Star 历史图表modelscope/ms-swift 的 Star 历史图表

    常见问题解答

    modelscope/ms-swift 是做什么的？

    This project is a comprehensive toolkit designed for the full lifecycle management of large language and multimodal models. It functions as a unified orchestrator that handles the entire development process, ranging from dataset preparation and supervised fine-tuning to advanced reinforcement learning alignment and production-ready inference deployment.

    modelscope/ms-swift 的主要功能有哪些？

    modelscope/ms-swift 的主要功能包括：Large Language Model Fine-Tuning Frameworks, Reinforcement Learning Alignment, LLM Fine-Tuning Engines, Multimodal Training Platforms, Training Pipelines, Data-Parallel Training, Model Training Pipelines, Transformer Reinforcement Learning Libraries。

    modelscope/ms-swift 有哪些开源替代品？

    modelscope/ms-swift 的开源替代品包括： verl-project/verl — This project is a distributed training infrastructure designed for aligning large language models through… zhaochenyang20/awesome-ml-sys-tutorial — This project provides a comprehensive technical guide and framework for engineering large-scale machine learning… axolotl-ai-cloud/axolotl — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large… hiyouga/llama-efficient-tuning — This project is a fine-tuning framework and training pipeline designed to optimize and adapt large language and vision… ymcui/chinese-llama-alpaca — This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a… sgl-project/sglang — Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It…

    Ms Swift 的开源替代方案

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    • zhaochenyang20/awesome-ml-sys-tutorialzhaochenyang20 的头像

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    • axolotl-ai-cloud/axolotlaxolotl-ai-cloud 的头像

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  • hiyouga/llama-efficient-tuninghiyouga 的头像

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    • 查看 Ms Swift 的所有 30 个替代方案→