Megatron-LM is a distributed transformer training library and large language model training framework designed to scale models across thousands of GPUs. It functions as a GPU-optimized deep learning toolkit and a scaling engine for mixture-of-experts architectures, enabling the training of models with hundreds of billions of parameters. The project implements multi-dimensional model parallelism, combining tensor, pipeline, data, expert, and context-based workload distribution. It specifically optimizes mixture-of-experts architectures through integrated memory and communication improvements t
DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of massive AI models. It serves as a model parallelism orchestrator and a toolkit for scaling large language models across multiple GPUs and compute nodes. The project distinguishes itself through 3D parallelism orchestration, which combines data, pipeline, and tensor parallelism. It utilizes ZeRO-based memory partitioning to eliminate redundant storage and employs CPU-offload memory management to move weights and optimizer states to system RAM. Additionally, it provides special
This project is a multimodal model trainer and machine learning fine-tuning tool that provides a containerized workflow for adapting pre-trained models to specific tasks. It features a no-code web interface and a dashboard for training large language models and other machine learning datasets without writing code. The system distinguishes itself by integrating a no-code interface with remote GPU orchestration, allowing users to deploy containerized training environments on cloud infrastructure or local hardware. It includes a dedicated integrator for uploading trained model weights and config
Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large language models. It functions as a comprehensive orchestrator for distributed training, enabling users to manage complex workflows across multi-node and multi-GPU environments. By utilizing structured configuration files, the platform streamlines the setup of training parameters, dataset paths, and hardware distribution strategies. The project distinguishes itself through its support for diverse training methodologies, including full-parameter tuning, parameter-efficient adaptation,
Efficient Training (including pre-training and fine-tuning) for Big Models
Principalele funcționalități ale openbmb/bmtrain sunt: Language Model Libraries, Model Training, Model Training Frameworks.
Alternativele open-source pentru openbmb/bmtrain includ: nvidia/megatron-lm — Megatron-LM is a distributed transformer training library and large language model training framework designed to… microsoft/deepspeed — DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of… jax-ml/jax — This project is a high-performance numerical computing library designed for large-scale scientific and machine… axolotl-ai-cloud/axolotl — Axolotl is a configuration-driven framework designed for the fine-tuning, evaluation, and quantization of large… huggingface/nanotron — Minimalistic large language model 3D-parallelism training. hpcaitech/colossalai — ColossalAI is a distributed deep learning framework designed for training and deploying massive artificial…