Distributed Training Frameworks for LLMs

This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation library and a four-bit quantizer to reduce the GPU memory requirements needed to train large models. The framework utilizes four-bit quantization and low-rank adapters to enable model training on consumer-grade hardware. It further reduces the memory footprint through double quantization and a paged optimizer that offloads states to system RAM. The system supports distributed training across multiple GPUs to handle larger parameter scales and includes utilities for custom dataset loading. It also provides automated generation scoring to evaluate model performance against benchmarks.

DeepSpeedExamples is a collection of reference implementations and scripts for training, fine-tuning, and executing inference on large-scale AI models using DeepSpeed optimization. It provides a distributed model training guide and practical workflows for adapting large language models through memory-efficient techniques. The repository includes specialized implementations for pipeline parallelism to handle models exceeding single GPU memory and a suite of examples for ZeRO memory optimization to reduce per-device overhead. It also features standardized test suites for benchmarking the throughput and latency of models running on DeepSpeed inference engines. The project covers broad capability areas including GPU memory optimization, distributed AI benchmarking, and high-performance model inference. It demonstrates the use of weight compression and distributed optimization to scale neural networks across multiple computing nodes.

Monolith is a distributed recommendation model framework and asynchronous training engine designed to build and train large-scale deep learning architectures. It functions as a distributed model trainer that processes massive datasets across multiple compute nodes using asynchronous update mechanisms. The system features a dedicated embedding table manager that creates unique, feature-isolated tables to prevent representation collisions. It also includes a real-time weight updater to capture immediate changes in user interest and data hotspots through continuous parameter synchronization. The framework covers the orchestration of distributed compute nodes, parameter server administration, and the construction of deep learning model graphs for recommendation tasks. These capabilities support asynchronous gradient updates and the management of complex feature representations.

Corenet is a deep learning training framework and computer vision model library designed for developing neural networks across vision, text, and audio modalities. It functions as a distributed training orchestrator for scaling workloads across multiple compute nodes and provides a multimodal data pipeline for processing image, text, and video data. The project includes a model conversion toolkit for transforming weights and architectures between different machine learning frameworks. It also provides tools for optimizing model performance on Apple Silicon and reducing response latency in generative models. The framework covers a broad range of capabilities, including visual recognition tasks such as object detection, semantic segmentation, and image classification. It supports advanced training techniques such as parameter-efficient fine-tuning, contrastive language-image pre-training, and structural reparameterization. Training and evaluation pipelines are managed through YAML-based configuration files and recipes to ensure reproducibility across environments.

SpeechBrain is an all-in-one deep learning toolkit designed for speech and audio processing. Built as a modular library, it provides a structured environment for developing, training, and deploying neural network models across a wide range of tasks, including automatic speech recognition, speaker identification, and audio enhancement. The framework distinguishes itself through a configuration-driven approach that separates model architecture and training hyperparameters from application logic. By utilizing externalized configuration files and standardized recipes, it enables reproducible research and simplifies the orchestration of complex experiments. It integrates traditional digital signal processing techniques directly with deep learning components, allowing for end-to-end feature extraction and signal augmentation within a unified pipeline. The platform supports large-scale development by providing abstractions for data ingestion, preprocessing, and distributed multi-GPU training. It includes built-in utilities for managing training loops, state checkpointing, and mixed-precision execution, alongside specialized interfaces for running inference with pretrained models. The library is designed to accommodate advanced learning methods, including self-supervised and diffusion-based approaches, to facilitate the creation of conversational artificial intelligence systems.

Fairseq is a PyTorch toolkit for sequence-to-sequence modeling, specializing in neural machine translation, automatic speech recognition, and large-scale language model training. It provides a framework for processing and aligning diverse data sources, including text, audio, and video, to support tasks such as speech-to-text conversion and multimodal sequence learning. The project is distinguished by its distributed training capabilities, which utilize parameter sharding, mixed-precision training, and CPU offloading to handle models that exceed single-device memory. It also includes specialized tools for data engineering, such as parallel data mining for unsupervised learning and back-translation for expanding training corpora. Its capability surface extends to comprehensive inference and generation tools, including beam search and lexical constraint enforcement, as well as model compression techniques like layer pruning and product quantization. The toolkit also provides utilities for feature extraction, model evaluation via metrics like perplexity and BLEU scores, and a registry-based system for extending models and tasks. Training and evaluation workflows are managed through a command-line interface that orchestrates hyperparameter configuration and model execution.

This project is a distributed training infrastructure designed for aligning large language models through reinforcement learning. It functions as an end-to-end engine for complex alignment tasks, including proximal policy optimization, direct preference optimization, and iterative self-play. By providing a unified framework for multi-turn interactions and tool-use scenarios, it enables the development of models capable of reasoning and external environment engagement. The framework distinguishes itself through a decoupled architecture that separates model training from sample generation. This asynchronous design allows for continuous throughput by partitioning compute resources between actor, reference, and rollout models. It supports large-scale distributed execution across multi-node clusters, utilizing high-performance communication primitives to synchronize model states and aggregate losses while maintaining stability through advanced policy clipping and variance reduction techniques. Beyond its core reinforcement learning capabilities, the system includes comprehensive infrastructure for data management, reward modeling, and performance optimization. It features modular interfaces for integrating custom tools and external reward servers, alongside built-in support for sequence parallelism, low-precision training, and hardware-specific acceleration. Observability is integrated throughout the pipeline, providing tools for profiling distributed tasks, monitoring policy divergence, and tracking GPU memory usage. The project is implemented in Python and provides a containerized environment for deployment across diverse hardware architectures.

SLIME is a distributed reinforcement learning framework for large language model post-training that bridges Megatron training with SGLang inference servers. It orchestrates scalable RL loops across GPU clusters, decoupling training and inference into independent processes that communicate over HTTP and NCCL for independent scaling and fault tolerance. The system supports multi-agent reinforcement learning workflows with parallel agent instances, customizable rollout strategies, and personalized agent serving that improves models from prior conversations without disrupting API serving. The framework distinguishes itself through byte-level delta weight synchronization that transfers only changed positions between training and inference servers, reducing bandwidth for cross-cluster deployments. It offers prefill-decode disaggregation with heterogeneous GPU group configurations, multi-token speculative decoding using the model's own prediction layer, and dynamic token-limited batching that maximizes throughput while preserving per-sample loss computation. A plugin-based customization interface exposes hooks for replacing generation, reward, and data-processing logic without modifying the core pipeline, with CPU-only contract tests validating custom implementations. The system provides comprehensive configuration and extensibility across agent systems, custom loss functions, reward computation, data filtering and formatting, rollout generation, and training hooks. It supports mixed-precision training with BF16 and FP8 inference, Mixture-of-Experts models with routing decision replay, multi-token prediction layer training, and supervised fine-tuning. Deployment capabilities include multi-node scaling via Ray, environment separation for training and serving, automatic rollout server recovery, and co-located training and inference on shared GPUs.

Sonnet is a modular machine learning framework and TensorFlow library used for building, training, and managing deep learning models. It functions as a system for composing neural networks from reusable modules and layers that encapsulate their own parameters and internal states. The project provides specialized tools for distributed model training, enabling the synchronization of gradients across multiple hardware devices. It also serves as a model state management system, allowing for the persistence of neural network weights and the export of portable models that separate the computation graph from the learned weights. The framework covers a broad range of development capabilities, including parameter management for optimization processes and the construction of computation graphs for hardware acceleration.

nanoGPT is a lightweight engine for training and fine-tuning transformer-based language models from scratch. It provides a minimalist codebase designed for educational exploration and rapid experimentation with neural network architectures, utilizing self-attention and feed-forward layers to process sequences and predict subsequent elements. The project distinguishes itself through a focus on high-speed data ingestion and hardware-accelerated performance. It includes a dedicated pipeline for transforming raw text into memory-mapped binary files, which enables efficient streaming during training. To maximize throughput, the system supports distributed data parallelism across multiple graphics processing units and employs just-in-time kernel compilation to optimize mathematical operations for specific hardware. Beyond core training capabilities, the repository provides a command-line interface for generative text inference, allowing users to sample sequences from trained models using configurable parameters. It also includes integrated benchmarking tools to measure iteration speeds and identify hardware bottlenecks, ensuring efficient model development across various configurations.

Keras is a high-level deep learning framework designed for constructing and training neural networks through the composition of modular, functional layers. It serves as a comprehensive modeling toolkit that provides standardized procedures for defining, evaluating, and deploying complex architectures. By utilizing a directed acyclic graph approach, the framework allows users to build intricate models with multiple inputs, outputs, and shared layers, ensuring consistent numerical execution through functional state management. The project distinguishes itself as a multi-backend machine learning engine that decouples high-level model definitions from low-level execution logic. This backend-agnostic architecture enables users to author model code once and deploy it across diverse hardware accelerators and tensor processing frameworks without rewriting core logic. Users can dynamically switch between different computational engines to optimize performance, while native utilities support large-scale distributed training by separating model topology from hardware-specific sharding and parallelism requirements. Beyond its core modeling capabilities, the framework includes an extensive ecosystem for specialized tasks such as hyperparameter optimization, recommendation system development, and the integration of pre-trained generative models for text and image synthesis. It supports both functional composition and object-oriented subclassing, allowing for the creation of custom layers and models that maintain compatibility with standard training loops, data streaming, and callback management. The framework is distributed as a Python package and provides a unified interface for managing the entire training lifecycle, from data pipeline preparation to model serialization and export.

Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and performance, including techniques like quantization, speculative decoding, and paged memory management for key-value caches. It provides native integration for distributed training across multi-node clusters, as well as flexible APIs for serving models via compatible inference servers. Developers can also utilize built-in utilities for model patching, custom kernel execution, and automated documentation generation to streamline development workflows.

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 cases, the framework improves accuracy in mathematical and logical problem-solving. It further supports advanced reasoning capabilities through group relative policy optimization and automated synthetic data pipelines, which curate and filter high-quality reasoning traces for model updates. The system utilizes modular, configuration-driven recipes to streamline complex workflows, including data decontamination, dataset composition, and multi-node orchestration. It includes standardized benchmarking tools to measure performance across reasoning and coding domains, ensuring that training processes remain reproducible and data-centric. The framework is built to handle the full lifecycle of model improvement, from initial synthetic data generation to final performance evaluation on high-performance computing clusters.

DeepSpeedExamples is a collection of reference implementations for training and deploying large scale AI models using the DeepSpeed optimization library. It provides Python code examples for training massive models across multiple GPUs through distributed optimization techniques. The repository includes optimized patterns for deploying and running large language model predictions in production environments. It also serves as a guide for model compression to reduce memory footprints and as a source for performance benchmarks to measure execution speed and resource utilization. The project covers distributed AI optimization, large scale model training, and model inference. These implementations incorporate memory management, pipeline-parallel execution, and quantization-based compression.

pix2pixHD is a conditional generative adversarial network designed to transform semantic label maps into high-resolution photorealistic images. It functions as a high-resolution image synthesizer and an image-to-image translation model capable of producing synthetic images at 2048x1024 resolution. The system includes a semantic image editor that allows for the modification of high-resolution visuals by updating the underlying semantic label maps. This enables interactive image editing and the generation of photorealistic images based on source images or discrete label maps. The framework provides tools for image translation model training using custom datasets. It incorporates training acceleration through automatic mixed precision and multi-GPU data parallelism to manage high-resolution tensors.

ipex-llm is an acceleration library and inference engine designed to optimize the execution and finetuning of large language models on Intel GPUs and NPUs. It provides a HuggingFace compatible model backend and a dedicated quantization toolkit for converting model weights into low-bit precision formats. The project facilitates distributed inference by splitting large model workloads across multiple accelerators using pipeline and tensor parallelism. It enables the deployment of models on Intel Arc, Flex, and Max GPUs to increase throughput and reduce latency. The library covers a broad range of optimization capabilities, including low-precision finetuning for local model updates and the loading of diverse community model formats. It also includes tools for measuring model predictive performance using standard perplexity metrics.

This project provides a transformer-based object detection model that treats the task as a direct set prediction problem. It implements a vision system capable of predicting bounding boxes and class labels for objects within an image, as well as frameworks for instance and panoptic segmentation. The architecture utilizes a transformer encoder and decoder to perform end-to-end set prediction, employing a Hungarian matcher to assign predicted boxes to ground truth objects. It incorporates a convolutional backbone for feature extraction and a system of learnable object queries to probe image locations. The project includes capabilities for distributed training across multiple GPUs and compute nodes, as well as tools for computing accuracy metrics such as Average Precision. It also provides utilities for bounding box coordinate conversion and the integration of pre-trained backbones and external datasets.

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks, including node and graph classification, link prediction, and graph generation. It supports diverse domain applications such as molecular property prediction, 3D point cloud analysis, knowledge graph embedding, and spatio-temporal forecasting. The framework includes a suite of tools for performance measurement, data parallel GPU training, and the management of on-disk chunked storage for massive datasets.