Reading
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.
Section 01
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.
Section 02
Section 03
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.
Section 04
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.
Section 05
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.
Section 06
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.
Section 07
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.
Section 08
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:
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.
Keep going with more reads from the same topic.
Nornir MCP Server is an enterprise-level server based on the Model Context Protocol (MCP). It seamlessly integrates large language models (such as Claude) with the Nornir network automation framework, supporting natural language orchestration for multi-vendor network devices (Cisco, Arista, Juniper, etc.), and providing production-grade features like a dual-engine architecture (NAPALM + Netmiko), intelligent filtering, and a secure sandbox.
Bibliothèque Française LLM is a structured indexing and annotation project for French public domain literature designed specifically for large language models (LLMs). It integrates multiple authoritative sources such as DraCor, Common Corpus, and Wikisource, providing metadata indexing categorized by genre, author, and era, as well as in-depth annotations for dramatic texts (including characters, lines, stage directions, etc.). Its aim is to enable LLMs to efficiently read and understand classic French literary works.
Splinter is a minimalist, high-performance key-value (KV) and vector storage system enabling zero-latency inter-process communication via shared memory and atomic operations. With only 766 lines of core code, it supports millions of operations per second and 768-dimensional vector storage, offering a new architectural approach for local LLM inference and data-intensive applications.
libmlxforge is an embeddable MLX large language model (LLM) inference engine designed specifically for Apple Silicon. It provides a unified C ABI interface, supports calls from Node.js, Swift, and Rust, and features continuous batching, streaming output, JSON-constrained structured output, and embedding vector generation.