All things open source at Ant Group. We aim to bring high caliber infrastructure FinTech OSS to the community.
Joined March 2024
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Ant Open Source retweeted
So happy we @ant_oss officially joined AAIF today. In the Agentic era, the core value of an open-source community isn't about code. It’s about who you're building with, and the Agent consensus you shape together.
4 new projects, 305 members, 15 industry sectors.
Mazin Gilbert, Executive Director of AAIF, is live at AGNTCon + MCPCon NA talking growth and what’s next for the agentic AI ecosystem.
Mazin is live right now. Don’t miss it. Join the livestream: bit.ly/3U6tMrS
Announcing Ant Group as Gold Member of Agentic AI Foundation @AgenticAIFdn.
@AntGroup has always been committed to leveraging cutting-edge technology to make everyday services—from finance and payments to healthcare—accessible to all. This vision drives our AI Lab @TheInclusionAI, with the mission of "AI built by everyone, for everyone."
As AI agents bring frontier LLM capabilities into real-world applications, we believe open community consensus is essential to ensuring they reliably serve everyone. Joining the Agentic AI Foundation enables us to collaborate more closely with the global community to make this a reality.
The Agentic AI Foundation (AAIF) continues to expand its global community, welcoming 305 new members across financial services, philanthropy, enterprise infrastructure, security, and technology.
Announced at AGNTCon + MCPCon North America, Gates Foundation, MongoDB, Nutanix, Royal Bank of Canada, and Vultr join as Gold Members, alongside new Silver and Associate Members.
Together, these organizations will help advance open standards for secure, interoperable, and trusted agentic AI, supporting adoption across industries.
Read the full announcement: bit.ly/3W0K8D7
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AI model for healthcare - introducing an exciting release of @AntLingAGI, meet Ling-3.0-flash-Sante!
✅ Professional healthcare tasks, built for real-world healthcare research and workflows
✅ Complex medical reasoning
✅ Deep research and multi-agent collaboration
✅ Evolving from a medical QA model into a more capable healthcare agent
Ling-3.0-flash-Sante is now open source. We look forward to building with the community🤗 huggingface.co/inclusionAI/L…
modelscope.cn/models/inclusi…
#openSource #inclusionAI
Today, we’re officially open-sourcing Ling-3.0-flash-Sante.
Sante is a healthcare-enhanced MoE model with 124B total parameters and only 5.1B active parameters per token, delivering leading performance among similarly sized models on professional healthcare tasks and complex medical reasoning.
Beyond answering medical questions, Sante is built for real-world healthcare research and workflows, with strengthened capabilities in deep research, multi-agent collaboration, and tool use.
The model weights are now available — we welcome researchers, developers, and healthcare practitioners to explore Sante and build with it.
More below ↓
Meet the Ming-3.0 speech families @TheInclusionAI @AntLingAGI:
✅ Ming-3.0-tts for stories, narration & voice assistants
✅ Ming-3.0-audio for meetings, interviews & voice-driven services, offering a more efficient deployment option
✅ Ming-3.0-asr for customer calls, voice search & domain terms. 4.41% average error across four ASR categories.
Try out now! huggingface.co/inclusionAI
#opensource #inclusionAI
Today, we're open-sourcing 5 Ming-3.0 models across 3 speech families.
- Ming-3.0-tts: 0.5B voice design & paralinguistic cues; Tiny 7.9B A1.3B long-form speech.
- Ming-3.0-audio: Flash 126B A6.5B understanding & tools; Tiny 9.3B A2.7B efficient dialogue.
- Ming-3.0-asr: Tiny 8.6B A2B dialects & context.
Ant Open Source retweeted
The FIRST Robbyant Embodied AI Challenge is live. 🤖🌍
We're inviting builders worldwide to push embodied intelligence forward — real hardware, real environments, real impact. 🦾
Be part of history in the making. Prizes. Recognition. The frontier. 🏆 Learn more & register → tianchi.aliyun.com/competiti…
#EmbodiedAI #Robotics
🚀 Ling-3.1-flash is now open source, turns rough briefs into pages, mobile UIs, and interactive prototypes.
📊560B total / 25B active / 1M context.
Weights & docs live on Hugging Face and ModelScope.
🔗huggingface.co/inclusionAI/L…
🔗modelscope.cn/models/inclusi…
#OpenSource #inclusionAI
See you in Bay Area! Ant Open Source and inclusionAI are hosting a happy hour for AI builders! Expect sharp technical conversations, food, drinks, and plenty of time to meet the community. Join us during #OpenSourceAIWeek 2026: luma.com/xs9sn0lp
Can we trust what the AI benchmarks says? Can an agent system be smarter than its best model?
Join inclusionAI at #OpenSourceAIWeek 2026 for tech talks, happy-hour drinks, food, and Token Shots 🍸
Palo Alto · RSVP: luma.com/xs9sn0lp
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📰Latest updates: welcome the new members of the @AntLingAGI family, meet Ming-Image-0.1-Design!
1️⃣ Ming-Image-0.1-Design: a 6B text-to-image model for UI, infographics, posters, and other text-rich visual designs
huggingface.co/inclusionAI/M…
2️⃣ Ming-Image-0.1-Design-Layer: decomposes a flattened design image into a requested number of RGBA layers using an image and a layer plan
huggingface.co/inclusionAI/M…
➕ and 2 Agent Skills for visual coding and presentations: the Ling UI Design Skill and the Image-to-Editable-PPT Skill.
🤗💗Explore and discuss with us! Join the Ant Ling community on Discord: discord.com/invite/GNaQc8WC5…
#inclusionAI #opensource #AgentSkills #multimodal
We’re open-sourcing the Ming-Image-0.1-Design family:
• Ming-Image-0.1-Design, 6B
• Ming-Image-0.1-Design-Layer, 6B
• Two open-source Agent Skills: the Ling UI Design Skill and the Image-to-Editable-PPT Skill
Ming-Image-0.1-Design ranks #1 among open-weight models on Artificial Analysis’s UI/UX Design leaderboard. 🧵
Three quantized versions of @AntLingAGI Ling-3.0-flash-Fin are now available: FP8, FP4 and INT4.
If you’re building real-world financial workflows, you can choose the version that best fits your infrastructure, memory and efficiency needs. 🧵
Now available on HuggingFace and ModelScope:
Hugging Face:
🔗huggingface.co/inclusionAI/L…
🔗huggingface.co/inclusionAI/L…
🔗huggingface.co/inclusionAI/L…
ModelScope: 🔗modelscope.cn/models/inclusi…
🔗modelscope.cn/models/inclusi…
🔗modelscope.cn/models/inclusi…
Ant Open Source retweeted
Today, we are releasing Ling-3.0-flash-VL, built on Ling-3.0-flash with visual understanding and visual agent capabilities. It performs well across visual perception, STEM reasoning, document intelligence, multimodal agent tasks, frontend coding, and medical report interpretation.
👋 LLaDA-Image combines the LLaDA2.0-mini dLLM understanding backbone with a 6B DiT for high-quality image generation and instruction-guided editing. Both backone and Image-Gen are diffusion models, trained in a unified framework.
🔑 Image-first training at scale:
Of ~220M cumulative generation-training samples, >90% use image-only supervision. Image-text pairs are introduced later for language alignment.
The result is a model family that covers high-quality generation, instruction-guided editing, and fast 2-4-step inference with LLaDA-Image-Turbo.
#OpenSource #inclusionAI #dLLM
🔑One model for generation and editing, plus a faster Turbo version:
LLaDA-Image combines a dLLM-based VLM, built on LLaDA2.0-mini, with a single-stream 6B DiT for image generation. In editing, reference-image features bypass the VLM and go directly to the DiT.
Generation and editing share one backbone. TwinFlow further distills the 50-step Base model to 2-4 steps, providing quality-first and speed-first deployment profiles within the same model family.
🔑 Benchmark results and open-source resources:
On Qwen-Image-Bench, LLaDA-Image scores 53.53 on the English track and 53.38 on the Chinese track, advance rankings on both among the open-source models listed in the technical report.
🔧Try out and explore LLaDA-Image with models, code, and technical report:
🤗 Hugging Face: huggingface.co/collections/i…
💻 Code: github.com/inclusionAI/LLaDA…
📄 Technical Report: arxiv.org/pdf/2609.03796
Given human demo videos, it infers objects, interactions & task flows to generate robot‑executable motions.
Check out the latest ICL work from Robbyant Research.
Can robots learn new tasks directly from human‑operation videos?✅
Meet Zero‑WAM: video‑based in‑context learning for robotics.
No heavy retraining, no verbose text instructions.
Given human demo videos, it infers objects, interactions & task flows to generate robot‑executable motions.
- HumanGen dataset: 8.6K tasks / 74.2K human‑robot samples
- IFP training for long‑range video task understanding
- 46.95% avg success rate on 7 unseen RoboTwin 2.0 tasks
Paper out now, code releasing soon.
📄 arxiv.org/abs/2608.26103
🌐 robbyant-research.github.io/…
Sharing a new collaboration with the #SGLang community. lmsys.org/blog/2026-08-28-in…
Infer-forge uses Harness + Loop + Graph to keep 28-hour median task lifetimes fully traceable. Peak tasks in flight: 2→9. A full DeepSeek-V4-Pro serving project split into 38 pieces, each verified like a human engineer. And the harness catches kernel silent corruption on its own.
🚀 Infer-forge: Harness, Loop, and Graph Engineering Around SGLang
@ant_oss built a three-layer system for running long inference optimization work through agents without losing provenance: Harness for execution, Task Loop for one Task's contract, Task Graph for verified Handoffs.
Highlights:
- Peak Tasks in flight rose from 2 to 9, median Task lifetime from 10h to 28h
- The agent ran a full serving project on DeepSeek-V4-Pro by itself. Splitting the work into 38 pieces and verifying each one like a human engineer.
- The harness catches kernel silent corruption on its own.
Read the full blog below 👇
Meet Ling-3.0-flash-dspark➡️🤗We adapted the public DSpark recipe to Ling with distribution aligned data, architecture ablations, and an acceptance aware loss.
The checkpoint and serving recipe are now available. 🔗Full engineering details: lmsys.org/blog/2026-08-21-li…
🔗Hugging Face: huggingface.co/inclusionAI/L…
🔗ModelScope: modelscope.cn/models/inclusi…
Ant Open Source retweeted
🧵 We’ve open-sourced 6 Base Model checkpoints for Ling-3.0-tiny & Ling-3.0-flash, covering pre-trained, mid-trained, and WSM-merged stages. None has undergone post-training, giving researchers flexible starting points for continued pre-training, fine-tuning, and further research.
Two key highlights:
- We use WSM to replace LR decay with weighted checkpoint merging, making the training process better suited for continual pre-training while enabling offline exploration of different LR decay strategies.
- With one shared training recipe, the community can validate strategies on tiny-base, then scale them to flash-base.
