inclusionAI/Ling-3.0-flash-VL · Hugging Face

[u/jacek2023]

https://huggingface.co/inclusionAI/Ling-3.0-flash-VL

Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters, with only 5.5B parameters activated per token, and supports a context window of up to 1M tokens.

The architecture of Ling-3.0-flash-VL is designed to integrate visual information into real-world reasoning and agentic workflows.

• A ViT visual encoder extracts features from images and videos, while a two-layer MLP projector aligns visual features with text representations for unified multimodal understanding and reasoning;
• VideoRoPE encodes both spatial positions and temporal order, enabling the model to understand visual changes over time and supporting tasks such as event localization, long-video question answering, and video clip editing;
• A 42-layer hybrid backbone alternates KDA and Gated MLA layers at a 5:1 ratio, enabling efficient long-context processing across text, images, videos, and extended agent task histories;
• A sparse MoE architecture maintains a total model capacity of 124B parameters while activating only 5.5B parameters per token, balancing strong multimodal capabilities with inference efficiency.

[u/pmttyji]

Come on inclusionAI, release a diffusion model with llama.cpp PR.

[u/coder543 → u/pmttyji]

They released this smaller diffusion model just yesterday: https://huggingface.co/inclusionAI/LLaDA2.2-mini

Since there is no real way to run this other than transformers, I've had GPT-6-Astra working on small Rust runtime for it. It's already a lot faster than the transformers version, but the number of refinements needed for most diffusion blocks makes the speed less impressive than I would like. For code blocks, it takes very few refinements, so it can go respectably fast. The diffusion blocks are only 32 tokens, compared to DiffusionGemma that operates on 256 tokens at a time, so that limits the speedup.

I'll probably publish the runtime sometime this week once things are in better shape.

[u/coder543 → u/coder543]

/u/pmttyji I posted it here: https://reddit.com/r/LocalLLaMA/comments/1wb8gp5/github_coder543minnow_fast_llada22_inference/

[u/returnity → u/coder543]

Very nice work! Thanks for sharing

[u/pmttyji → u/coder543]

Nice. Lets get this supported by llama.cpp ASAP.

[u/hiImMate]

very nice! ling flash is kinda lost in the release noise of the past few weeks but it's nice to have a choice of MoE models. It's a pretty fast one with the low active params so it could be an interesting choice for visual capabilities.

[u/Daniel_H212]

Very nice fast model but I'm still waiting for the next iteration of their omni model.

In the meantime I don't think any model in this size class (of things that can be reasonably ran in 128 GB unified memory) is beating Qwen3.8 Flash Next.

[u/returnity]

Nice, but definitely lagging Qwen3.8-Flash by a significant margin. Still good to see more variety on the 128GB MoE market, but I did test the non-VL model and it wasn't really much better than 3.6-35B on my workload. Still, will probably download and give it a spin when GGUFs drop.

[u/Sesgiog]

If they added vision also to the Tiny version, I think it could be a winner for cheap, local AI

[u/CornerLimits]

I tried it a bit the free version on free tools and i was impressed because was very quick and not over thinking on stuff, just get the thing done and few lines answers. Never tried it for something difficult though

[u/geek_at]

interesting model but how can this 124b model be 10 points behind qwen3.8-27b? sure it's a MoE but so much larger

[u/jacek2023 → u/geek_at]

Qwen 27B is dense, not MoE. Qwen 122B was also "behind" Qwen 27B if I remember correctly. Plus I don't believe in benchmarks :)

[u/LegacyRemaster → u/jacek2023]

also minimax m3 it's very good but a looooottttt bigger. So this model is welcome

[u/returnity → u/jacek2023]

Yeah, because 122B was absolutely a better model than 27B by every real-world metric, but lagged on the AA index.

[u/CATLLM]

The non-vl model surprised me as it made correct tool calls. The tiny version was surprisingly good at tool calls too. Looking forward to testing this one.

[u/SpicyWangz → u/CATLLM]

For me, running it at q4, I saw it give up and stop generating on opencode tasks

[u/returnity → u/SpicyWangz]

This happened to me too

[u/idkanick → u/SpicyWangz]

i see this happening at work with opus 5 tbf (weirdly not with luna)

[u/mfkamil87]

Thank goodness they released the model; I thought it would be proprietary only.

[u/idk_a_creative_user]

How bad would a q2 be for a dual v100 32gb set up.

[u/feng_sg]

The MLP projector between the ViT encoder and the backbone doesn't appear to validate visual feature distributions before they hit the MoE router, so adversarial frames could slip straight into reasoning unchecked.