modelscope/ms-swift

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.
• 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.

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