# FusionLLM: An Efficient Hybrid Architecture Large Model Training Framework Integrating MLA, GDN, and MoE

> FusionLLM is a research-grade, production-ready large language model pre-training framework that integrates modern architectural innovations such as Multi-Head Latent Attention (MLA), Gated Delta Net (GDN), and Mixture of Experts (MoE) into a unified training system.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-09T14:14:34.000Z
- 最近活动: 2026-06-09T14:26:47.022Z
- 热度: 163.8
- 关键词: LLM, MLA, GDN, MoE, MTP, Transformer, 状态空间模型, 混合专家, 预训练, PyTorch
- 页面链接: https://www.zingnex.cn/en/forum/thread/fusionllm-mlagdn-moe
- Canonical: https://www.zingnex.cn/forum/thread/fusionllm-mlagdn-moe
- Markdown 来源: floors_fallback

---

## Introduction / Main Floor: FusionLLM: An Efficient Hybrid Architecture Large Model Training Framework Integrating MLA, GDN, and MoE

FusionLLM is a research-grade, production-ready large language model pre-training framework that integrates modern architectural innovations such as Multi-Head Latent Attention (MLA), Gated Delta Net (GDN), and Mixture of Experts (MoE) into a unified training system.

## Original Author and Source

- **Original Author/Maintainer**: atandra2000
- **Source Platform**: GitHub
- **Original Title**: FusionLLM
- **Original Link**: https://github.com/atandra2000/FusionLLM
- **Release Date**: June 9, 2026

## Project Overview

FusionLLM is a research-grade, production-ready large language model pre-training framework aiming for efficient inference with approximately 2.5 billion active parameters under a total parameter scale of around 7 billion. The project systematically integrates several key architectural innovations in the LLM field in recent years, including Multi-Head Latent Attention (MLA), Gated Delta Net (GDN), Mixture of Experts (MoE), and Multi-Token Prediction (MTP).

The core idea behind this hybrid architecture design is: different architectural components have their own advantages in computational efficiency and expressive power; through a carefully designed layer scheduling strategy, inference costs can be significantly reduced while maintaining model capacity.

## Multi-Head Latent Attention (MLA)

MLA (Multi-Head Latent Attention) is a key technology introduced by DeepSeek-V2, which greatly reduces memory usage during inference through low-rank KV compression. Traditional multi-head attention requires storing complete key-value pairs for each head, while MLA compresses KV into a smaller latent space via projection, significantly reducing cache requirements with almost no loss of performance.

## Gated Delta Net (GDN)

GDN (Gated Delta Net) is a state space model implementation in the style of Qwen3-Next, providing inference capabilities with constant time complexity. Unlike the quadratic complexity attention mechanism of Transformers, GDN achieves linear complexity through incremental state updates, making it particularly suitable for handling long sequences. FusionLLM uses GDN as a complement to MLA, providing efficient sequence modeling capabilities in specific layers.

## Mixture of Experts (MoE)

FusionLLM implements a fine-grained DeepSeekMoE architecture, configured with 64 routing experts and 6 active experts. This design allows the model to significantly expand its parameter scale without increasing inference computation. Through group-restricted routing and unbiased Sigmoid gating, the MoE layer can intelligently route input tokens to the most relevant subset of experts.

## Multi-Token Prediction (MTP)

MTP is another key feature of FusionLLM, allowing the model to predict the next 1, 2, or 3 tokens simultaneously. This multi-step prediction mechanism not only accelerates training convergence but also significantly improves the model's inference capabilities. Through auxiliary prediction heads, the model learns to understand sequence structures from a more global perspective.

## Layer Scheduling Strategy

The most unique design of FusionLLM lies in its hybrid layer scheduling strategy. The project uses a 5:1 layer ratio, meaning 1 GDN layer is inserted after every 5 MLA layers, totaling 30 layers. The considerations behind this design are:

- **MLA layers** provide strong context modeling capabilities, suitable for handling complex semantic relationships
- **GDN layers** provide efficient sequence modeling, especially suitable for capturing long-range dependencies
- **Hybrid scheduling** gradually introduces the efficiency advantages of state space models while maintaining Transformer-level expressive power

Users can adjust the layer ratio according to specific needs; the framework supports multiple scheduling configurations such as 5:1, 6:1, and 8:1.