Ling: Open-Source Mixture-of-Experts LLMs by InclusionAI

RepositoryinclusionAI/LingResource

InclusionAI has open-sourced Ling, a family of Mixture-of-Experts (MoE) large language models. The suite is offered in two sizes: Ling-lite with 16.8 billion total parameters (2.75 billion activated) and Ling-plus with 290 billion total parameters (28.8 billion activated).

Key points

  1. MoE Architecture: Ling-lite operates on 16.8B parameters with 2.75B active; Ling-plus features 290B parameters with 28.8B active.
  2. Context Length: Ling-lite-1.5 handles up to 128K tokens context, while Ling-plus-base supports at least 64K tokens.
  3. Incremental Updates: Releases including Ling-lite-0415 and version 1.5 brought targeted enhancements in coding, mathematics, and reasoning.
  4. Open Availability: Released under the MIT license, with model weights accessible via Hugging Face and ModelScope.

Why it matters

MoE architectures significantly reduce runtime compute costs because only a fraction of total parameters are activated per token. With only 2.75B active parameters and a 128K context window, Ling-lite presents an efficient candidate for resource-constrained inference.

Analysis

The repository is aimed at AI practitioners and researchers seeking open MoE foundations for diverse reasoning and generation tasks. With around 267 GitHub stars, community adoption remains in an early stage, but regular model releases in 2025 show active maintenance and clear progression.

Mentioned links

Found in

Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

Related entries

Similar in content, found by the AI search.

  • Link:inclusionAI

    Ling-3.0-tiny: Lightweight Hybrid Reasoning MoE Model by inclusionAI

    inclusionAI has introduced Ling-3.0-tiny, a compact MoE model with 7.9B total parameters activating only 1.3B parameters per token. Utilizing a hybrid architecture of linear and latent attention mechanisms, it aims to deliver efficient reasoning and agentic workloads on local hardware.

    AI & LLMs· Tool

  • Link:Ant Group

    inclusionAI: Ant Group's AI Initiative on Hugging Face

    inclusionAI serves as the Hugging Face hub for Ant Group's Artificial General Intelligence (AGI) initiative, hosting open-source LLMs, image generation models, and reinforcement learning systems.

    AI & LLMs· Resource

  • Article:Thinking Machines Lab

    Thinking Machines Lab Releases Open-Weights Model Inkling

    Thinking Machines Lab has introduced Inkling, an open-weights Mixture-of-Experts model featuring 975B total parameters (41B active) and a 1M token context window. It is built as a generalist foundation optimized for customization via the Tinker platform.

    AI & LLMs· Announcement

  • Repository:JustVugg/colibri

    Colibrì: Running Frontier MoE Models via Disk-Streamed Experts

    Colibrì is a lightweight inference engine written in pure C with zero dependencies, designed to run frontier Mixture-of-Experts (MoE) models ranging from 744B to 2.8T parameters on consumer and heterogeneous hardware. It treats VRAM, RAM, and storage as a unified memory hierarchy, streaming experts on demand.

    37.7kstarsC

    AI & LLMs· Tool

  • Article:Zhidx

    Ant Group open-sources compact MoE model Ling-3.0-tiny

    Ant Group's Bailian team has released Ling-3.0-tiny, an open-source lightweight mixture-of-experts model. It leverages DeepSeek's MLA and Kimi's KDA architectures with 7.9B total parameters, activating just 1.3B parameters per token for local edge deployment on hardware like Apple Silicon Macs and DGX systems.

    AI & LLMs· Announcement

Back to the knowledge base

Let's talk about your project

Get in touch

Locations

  • Mattersburg
    Johann Nepomuk Bergerstraße 7/2/14
    7210 Mattersburg, Austria
  • Vienna
    Ungargasse 64-66/3/404
    1030 Wien, Austria

Parts of this content were created with the assistance of AI.