#Mixture-of-Experts

DBRX is a robust open-source language model by Databricks, featuring a Mixture-of-Experts architecture with 132 billion parameters suited for AI development. Offering resources like inference examples and model code, it integrates with platforms such as You.com and Perplexity Labs. It's optimized with libraries like Composer and MegaBlocks and supports both full and LoRA finetuning. Discover its broad capabilities within AI ecosystems through the Hugging Face repository for easy integration and customization.

ESFT optimizes Large Language Models (LLMs) performance and efficiency using Mixture-of-Experts (MoE) architecture. By focusing on task-relevant components, it reduces resource and storage needs and enhances model adaptability to various datasets. This method suits industries looking for efficient LLM deployment with specialized tuning. Recently accepted at EMNLP 2024, ESFT provides open-source training code for integration and testing on personal models and data, facilitating effective model customization with decreased computational demand.

The LLaMA-MoE project features open-source Mixture-of-Experts models optimized for efficient deployment with 3.0~3.5B active parameters. These models are continually pre-trained using LLaMA's FFNs on selected datasets, improving performance through methods like random and clustering for expert construction and utilizing gating strategies such as TopK Noisy and Switch Gating. With FlashAttention-v2 integration, the project offers swift continual pre-training and dynamic weight sampling, ensuring adaptability and optimization for diverse AI applications.

DeepSeek-V2 is an advanced MoE language model featuring efficient operation with only a fraction of its total parameters engaged, leading to a 42.5% reduction in training costs and a 93.3% decrease in KV cache. Pretrained on a vast dataset and fine-tuned for excellence, it delivers superior performance on diverse benchmarks, including English and Chinese, coding, and long-form dialogue tasks. Discover innovations in its architecture and utilize it through Chat, API platforms, or local deployment for enhanced productivity.

This project enhances the Mixtral-8x7B model using instruction-tuning to improve its Chinese conversational performance. By employing machine-generated data and focusing on specific datasets, it offers advancements in sparse model performance, as demonstrated in benchmark tests such as C-Eval. The Aurora model stands out for its ability to manage tasks without human instructions, overcoming the limitations of traditional language models.

Hivemind enables decentralized deep learning with PyTorch, facilitating large-scale model training without a central server. It offers fault-tolerant backpropagation and decentralized parameter averaging for flexible network training. Used in projects like Training Transformers Together, it supports Linux, macOS, and Windows 10+, and integrates with PyTorch Lightning for handling distributed, unreliable peers.

Tutel MoE provides an efficient implementation of Mixture-of-Experts, including 'No-penalty Parallelism' for adaptable training and inference. It is compatible with PyTorch and supports CUDA and ROCm GPUs as well as various CPU formats. Recent updates feature new benchmarks, tensorcore options, and improved communication. Tutel enables seamless configuration changes without additional costs and offers straightforward installation and testing processes. It supports distributed modes across multi-node and multi-GPU setups, making it suitable for developers looking to improve performance and scalability in machine learning frameworks.

DeepSeekMoE 16B, utilizing a Mixture-of-Experts architecture, enhances computational efficiency with a reduction to 40% of operations. Matching the performance of models like LLaMA2 7B, its Base and Chat versions support English and Chinese, enabling deployment on a single GPU without quantization. Available under specific licensing for research and commercial applications.