Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
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Updated
Nov 16, 2024 - Python
Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
An unofficial https://bgm.tv ui first app client for Android and iOS, built with React Native. 一个无广告、以爱好为驱动、不以盈利为目的、专门做 ACG 的类似豆瓣的追番记录,bgm.tv 第三方客户端。为移动端重新设计,内置大量加强的网页端难以实现的功能,且提供了相当的自定义选项。 目前已适配 iOS / Android / WSA、mobile / 简单 pad、light / dark theme、移动端网页。
Mixture-of-Experts for Large Vision-Language Models
PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538
⛷️ LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-training (EMNLP 2024)
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
Tutel MoE: An Optimized Mixture-of-Experts Implementation
中文Mixtral混合专家大模型(Chinese Mixtral MoE LLMs)
MindSpore online courses: Step into LLM
Official LISTEN.moe Android app
Inferflow is an efficient and highly configurable inference engine for large language models (LLMs).
A libGDX cross-platform API for InApp purchasing.
ModuleFormer is a MoE-based architecture that includes two different types of experts: stick-breaking attention heads and feedforward experts. We released a collection of ModuleFormer-based Language Models (MoLM) ranging in scale from 4 billion to 8 billion parameters.
MoH: Multi-Head Attention as Mixture-of-Head Attention
MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
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