inclusionAI (Ant Group) Open Models: Model List, Licenses and Articles

September 26, 2026September 27, 2026

About inclusionAI (Ant Group)

inclusionAI is the AGI (artificial general intelligence) initiative of China’s Ant Group. Its inclusionAI organization on Hugging Face calls itself the “home for Ant Group’s Artificial General Intelligence (AGI) initiative" and releases language models, reinforcement learning systems and more. With 212 models, it is a publisher that updates frequently.

What It Releases

• Ling (language models): the latest Ling-3.0 is a hybrid MoE combining linear attention (Kimi Delta Attention) with MLA. Ling-3.0-flash has 124B total and 5.1B active parameters, and its model card says it matches or outperforms the previous 1T-class flagship Ring-2.6-1T. The small Ling-3.0-tiny has 7.9B total and 1.3B active parameters, and its model card says it was validated on NVIDIA DGX Spark, Apple Silicon MacBooks and the Mac mini.
• LLaDA (diffusion language models): language models that generate text by diffusion rather than one token at a time. LLaDA2.2 adds editing with delete and insert control tokens, its first step toward agent use. LLaDA-Image extends the approach to image generation.
• Ming (multimodal): including the Ming-Image image generation models.
• Others include UI-Venus for screen-operating agents, ArmorOCR for OCR and SingGuard for safety classification.

Licenses

The organization card says “all projects (models) in our organization are based on MIT or Apache licenses". Indeed, most models in the table are MIT (Ling, Ming-Image) or Apache-2.0 (LLaDA and others). Some models have an empty or other license field in the table, however, so read each model’s license text before use. This summary is not legal advice.

Running Them Locally

• There are official GGUF builds. inclusionAI itself publishes GGUF files for Ling-3.0-flash, Ling-3.0-tiny, ArmorOCR and others, usable with llama.cpp-based tools. Ling-3.0 also has official FP8, INT4 and FP4 builds.
• For consumer hardware, Ling-3.0-tiny is the realistic choice. Ling-3.0-flash activates 5.1B but needs all 124B parameters loaded.
• Diffusion language models (LLaDA) use a different inference mechanism from ordinary language models. llama.cpp and Ollama may not support them; check the “Can you run it locally?" section of our articles.

Sources: the inclusionAI organization card; model cards of inclusionAI/Ling-3.0-flash, inclusionAI/Ling-3.0-tiny and inclusionAI/LLaDA2.2-flash; the model list of the inclusionAI organization on Hugging Face (all as of 2026-09-26).

Our Coverage and Data

Local Model Watch has published 1 article(s) on inclusionAI (Ant Group)’s own models, 1 on third-party fine-tunes and quantized builds based on them, and 1 from its official blog. The model list below is collected by code from Hugging Face every day. Part of our publisher index; model families link to their family pages.

At a Glance

Recent Models on Hugging Face

The publisher lists 212 models in total on Hugging Face. Below are up to 15 of the most recent generative models (text, image, video, audio and so on), the scope of this site, per organization. Classifiers, feature extractors, research components and repositories without a model card are omitted, and the publisher’s own quantized builds and helper variants are folded into the row of the original model. Parameter counts are left blank for repositories that pack weights into integer types, where Hugging Face’s count is not the real parameter count.

From the Hugging Face API. Last changed 2026-09-26 (JST). Collected by code.

Licenses of Its Own Models (as recorded in our articles)

The license can differ from model to model even within one publisher. This is not legal advice; check each model’s license text before use.

By Model Family

Articles on Its Own Models

Fine-tunes and Builds by Others

From Its Official Blog

• 2026-09-24 inclusionAI Publishes Training Content Summaries for EU Compliance

Last updated 2026-09-26 (JST). The explanation at the top of this page was written with the help of AI from the primary sources it cites. The tables and lists under “Our Coverage and Data" are assembled by code from our article log and the Hugging Face API.