ziyanwould @AIForAI_EN

Es una pena que el 80% de la gente solo use ChatGPT. Cuando pueden acceder a ChatGPT, DeepSeek-R1, Claude, Gemini, Midjourney, Flux, Perplexity y Luma en un solo lugar. Aquí te enseño cómo:

Mixture of Experts (MoE) is a powerful approach in deep learning that allows models to scale efficiently by leveraging sparse activation. Instead of activating all parameters for every input, MoE selects a subset of experts using a router, leading to better computational efficiency and improved generalisation. MoE has been widely adopted in large-scale models like Switch Transformer, DeepSeek and GShard. 🔹 What Are Experts? Experts in MoE are independent feedforward neural networks (MLPs or other architectures) that specialize in different types of data. Instead of using a monolithic model for all inputs, MoE dynamically selects the most relevant experts per token, allowing specialization and better parameter efficiency. Many people often misunderstand experts as specialists in specific domains like biology or chemistry. However, in this context, experts specialize in different aspects of sentence structure and syntax, such as complex words, punctuation, visual descriptions, verbs, etc. 🔹 Routing Mechanism A key component of MoE is the router, which determines which experts handle a given input. The router is typically a learned function, often implemented as a small neural network or a simple linear transformation followed by a softmax. The routing process involves: 1.Computing Expert Scores: Each input is assigned a probability distribution over experts using a router network. 2.Top-k Selection: Instead of using all experts, MoE selects the top-k highest scoring experts for each token. 3.Dispatching & Processing: The selected experts process the token, and the final output is a weighted sum of expert outputs. 🔹 Load Balancing in MoE One of the biggest challenges in MoE is load balancing—ensuring that all experts receive a roughly equal number of tokens. Without proper balancing, some experts might be overloaded while others remain underutilized, leading to inefficient computation and degraded performance. To address this, various auxiliary loss functions are used: •Auxiliary Load Balancing Loss: Encourages uniform token distribution across experts by penalizing imbalanced routing decisions. •Router Z-Loss: Helps stabilize router learning by preventing overconfidence in expert selection. •Importance Factor: Measures how frequently each expert is selected and is used to guide training to balance utilization. 🔹 Shared Experts in DeepSeek-MoE DeepSeek-MoE introduces an interesting variation where experts are shared across multiple layers, rather than each layer having its own separate set of experts. This reduces parameter redundancy and improves efficiency while maintaining MoE’s benefits.

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🚀 Introducing Hunyuan-TurboS – the first ultra-large Hybrid-Transformer-Mamba MoE model! Traditional pure Transformer models struggle with long-text training and inference due to O(N²) complexity and KV-Cache issues. Hunyuan-TurboS combines: ✅ Mamba's efficient long-sequence processing ✅ Transformer's strong contextual understanding 🔥 Results: - Outperforms GPT-4o-0806, DeepSeek-V3, and open-source models on Math, Reasoning, and Alignment - Competitive on Knowledge, including MMLU-Pro 1/7 lower inference cost than our previous Turbo model 📌 Post-Training Enhancements: - Slow-thinking integration improves math, coding, and reasoning - Refined instruction tuning boosts alignment and agent execution - English training optimization for better general performance 🎯 Upgraded Reward System: - Rule-based scoring & consistency verification - Code sandbox feedback for higher STEM accuracy - Generative-based reward improve QA and creativity, reducing reward hacking The future of AI is here! 🚀

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Microsoft launched free courses on AI. No payment needed. Here are 10 courses you don't want to miss in 2025:

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