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Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
SGLang is a fast serving framework for large language models and vision language models.
<img alt="electron" src="https://github.githubassets.com/images/icons/emoji/electron.png?v8" /> 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
MoBA: Mixture of Block Attention for Long-Context LLMs
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)
Tutel MoE: Optimized Mixture-of-Experts Library, Support DeepSeek FP8/FP4
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models
An open-source solution for full parameter fine-tuning of DeepSeek-V3/R1 671B, including complete code and scripts from training to inference, as well as some practical experiences and conclusions. (DeepSeek-V3/R1 满血版 671B 全参数微调的开源解决方案，包含从训练到推理的完整代码和脚本，以及实践中积累一些经验和结论。)
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).
MoH: Multi-Head Attention as Mixture-of-Head Attention
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.
[ICLR 2025] MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts