Model reference · open weights
Ming-flash-omni-2.1 is an open-weight language model from andrewheins55. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | andrewheins55 |
|---|---|
| Type | Language models |
| Task | Omni (any→any) |
| Parameters (lead) | 104.2B |
| Runs with | diffusers |
| Released | 2026-02-15 |
| Popularity | 581 downloads / month |
| Licence | Open weights |
About
The newly released Ming-flash-omni 2.0 leverages the Ling-2.0 architecture—a Mixture-of-Experts (MoE) framework comprising 100B total and 6B active parameters. Representing a generational advancement over its predecessor, it establishes new State-of-the-Art (SOTA) benchmarks among open-source omni-MLLMs. Ming-flash-omni 2.0 effectively synergizes foundational abilities with specialized domain expertise. In particular, it exhibits superior performance in visual encyclopedic knowledge, immersive speech synthesis, and high-dynamic image generation and manipulation.
Compared to Ming-flash-omni Preview, Ming-flash-omni 2.0 focuses on optimizing capabilities across the following key domains:
Expert-level Multimodal Cognition: It accurately identifies plants and animals, recognizing cultural references (from regional cuisines to global landmarks), and delivering expert-level analysis of artifacts, including era, form, and craftsmanship. By synergizing high-resolution visual capture with a vast knowledge graph, the model achieves "vision-to-knowledge" synthesis, enabling superior knowledge understanding.
Immersive and Controllable Unified Acoustic Synthesis: Ming-flash-omni 2.0 introduces a unified end-to-end acoustic generation pipeline that integrates Speech, Audio, and Music within a single channel. Leveraging Continuous Autoregression coupled with a Diffusion Transformer (DiT) head, the model enables zero-shot voice cloning and nuanced attribute control (e.g., emotion, timbre, and ambient atmosphere). This architecture facilitates a transition from simple text-to-speech to highly expressive, emotionally resonant, and immersive auditory experiences.
High-Dynamic Controllable Image Generation and Manipulation: Ming-flash-omni 2.0 features a native multi-task architecture that unifies segmentation, generation, and editing, allowing for sophisticated spatiotemporal semantic decoupling. It excels in high-dynamic content creation, including atmospheric reconstruction, seamless scene composition, and context-aware object removal. By maintaining texture coherence and spatial depth consistency, Ming-flash-omni 2.0 achieves state-of-the-art precision in complex image manipulation tasks.
Enhanced Multimodal Cognition & Free Modality Switching
Streaming Video Conversation
Audio Context ASR & Dialect ASR
Controllable Image Generation
You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-flash-omni-Preview, Please refer to this link.
| Model | Input modality | Output modality | Download |
|---|---|---|---|
| Ming-flash-omni 2.0 | Image,text,video,audio | Image,text,audio | 🤗 HuggingFace 🤖 ModelScope |
| If you're in mainland China, we strongly recommend you to download our model from 🤖 ModelScope. |
pip install modelscope
modelscope download --model inclusionAI/Ming-flash-omni-2.0 --local_dir inclusionAI/Ming-flash-omni-2.0 --revision master
Note: This download process will take several minutes to several hours, depending on your network conditions.
pip install -r requirements.txt
pip install nvidia-cublas-cu12==12.4.5.8 # for H20 GPU
We provide a step-by-step running example:
Step 1 - Download the source code
git clone https://github.com/inclusionAI/Ming.git
cd Ming
Step 2 - Download the model weights and create a soft link to the source code directory
Download our model following Model Downloads
mkdir inclusionAI
ln -s /path/to/inclusionAI/Ming-flash-omni-2.0 inclusionAI/Ming-flash-omni-2.0
Step 3 - Enter the code directory, you can refer to the following codes to run the Ming-flash-omni model.
jupyter notebook cookbook.ipynb
We also provide a simple example on the usage of this repo. For detailed usage, please refer to [cookbook.ipynb](https://github.com/inclusionAI/Ming/bl
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys ming-flash-omni-2-1 for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ming-flash-omni-2-1 below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"ming-flash-omni-2-1","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.