--- title: Ming-Lite-Omni canonical_url: "https://www.modelscope.cn/models/inclusionAI/Ming-Lite-Omni" md_url: "https://www.modelscope.cn/models/inclusionAI/Ming-Lite-Omni.md" repository: inclusionAI/Ming-Lite-Omni last_updated: 2025-12-30 license: mit pipeline_tag: any-to-any tasks: - any-to-any base_model: - inclusionAI/Ling-lite base_model_relation: finetune parameters: 19.1B tensor_type: - F32 library_name: - pytorch - transformer - onnx - safetensors - diffusers downloads: 3128 stars: 17 --- # Ming-Lite-Omni > Ming-Lite-Omni - inclusionAI 在 ModelScope 开源的模型。📑 Technical Report ｜📖 Project Page ｜🤗 Hugging Face ｜ 🤖 ModelScope inclusionAI/Ming-Lite-Omni 是 ModelScope 魔搭社区上的 19.1B 参数any-to-any模型，采用 mit 许可，基于 inclusionAI/Ling-lite 构建。 - **Repository**: inclusionAI/Ming-Lite-Omni - **License**: mit - **Tasks**: any-to-any - **Parameters**: 19.1B - **Base model**: inclusionAI/Ling-lite - **Downloads**: 3128 - **Stars**: 17 - **Last updated**: 2025-12-30 Source: https://www.modelscope.cn/models/inclusionAI/Ming-Lite-Omni --- # Ming-Lite-Omni

📑 Technical Report｜📖Project Page ｜🤗 Hugging Face｜ 🤖 ModelScope ## Introduction Ming-lite-omni, a light version of Ming-omni, which is derived from [Ling-lite](https://github.com/inclusionAI/Ling) and features 2.8 billion activated parameter. Ming-lite-omni is a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation. Ming-lite-omni employs dedicated encoders to extract tokens from different modalities, which are then processed by Ling, an MoE architecture equipped with newly proposed modality-specific routers. This design enables a single model to efficiently process and fuse multimodal inputs within a unified framework, thereby facilitating diverse tasks without requiring separate models, task-specific fine-tuning, or structural redesign. Importantly, Ming-lite-omni extends beyond conventional multimodal models by supporting audio and image generation. This is achieved through the integration of an advanced audio decoder for natural-sounding speech and Ming-Lite-Uni for high-quality image generation, which also allow the model to engage in context-aware chatting, perform text-to-speech conversion, and conduct versatile image editing. Our experimental results showcase Ming-lite-omni offers a powerful solution for unified perception and generation across all modalities. Notably, Ming-lite-omni is the first open-source model we are aware of to match GPT-4o in modality support, and we release all code and model weights to encourage further research and development in the community.

## 📌 Updates * [2025.06.12] 🔥 Our [Technical Report](https://arxiv.org/abs/2506.09344) is in public on arxiv. * [2025.05.28] 🔥 The official version of Ming-lite-omni is released, with better performance and image generation support. * [2025.05.04] 🔥 We release the test version of Ming-lite-omni：[Ming-lite-omni-Preview](https://github.com/inclusionAI/Ming/tree/Ming-Lite-Omni-Preview). ## Key Features - **Unified Omni-Modality Perception**: Ming-lite-omni, built on [Ling](https://github.com/inclusionAI/Ling), an MoE architecture LLM, resolves task conflicts and ensures coherent integration of tokens from different modalities through modality-specific routers. - **Unified Perception and Generation**: Ming-lite-omni achieves unified understanding and generation, enabling the model to interpret multimodal instructions and user intent during generation, which helps enhance generation quality and improves usability across multiple tasks. - **Innovative Generation Capabilities**: Ming-lite-omni can perceive all modalities and generate high-quality text, real-time speech, and vivid images simultaneously, delivering exceptional cross-modal performance across diverse tasks including image perception, audio-visual interaction, and image generation. ## Evaluation Ming-lite-omni delivers exceptional cross-modal performance, as validated across image perception, audio-visual interaction, and image generation tasks. Specifically, in the image perception task, Ming-lite-omni attained performance comparable to that of Qwen2.5-VL-7B by activating only 2.8B parameters. It delivers superior performance in end-to-end speech understanding and instruction following, surpassing Qwen2.5-Omni and Kimi-Audio. It also supports native-resolution image generation, editing, and style transfer, achieving a GenEval score of 0.64, outperforming mainstream models such as SDXL. In terms of FID, Ming-lite-omni reaches 4.85, setting a new SOTA across existing methods.

### Image benchmark

| Benchmarks | Ming-lite-omni | Qwen2.5-VL-7B-Instruct | InternVL2.5-8B-MPO | |:------------------|:--------------:|:----------------------------:|:------------------:| | AI2D | 83.1 | 84.4 | 84.5 | | HallusionBench | 55.0 | 55.8 | 51.7 | | MMBench_TEST_V11 | 80.8 | 82.8 | 82.0 | | MMMU | 56.3 | 56.6 | 54.8 | | MMStar | 64.7 | 65.3 | 65.2 | | MMVet | 71.3 | 71.6 | 68.1 | | MathVista | 71.6 | 68.1 | 67.9 | | OCRBench | 88.4 | 87.8 | 88.2 | | Average | 71.4 | 71.5 | 70.3 |
#### Encyclopedia Benchmarks
| Object Recognition | Ming-lite-omni | Qwen2.5-VL-7B-Instruct | |:---------------------|:--------------:|:------------------------:| | Plants | **54.96** | 47.8 | | Animals | **56.7** | 50.85 | | Vehicles | 41.91 | **42.29** | | Food & Ingredients | **62.28** | 54.09 | | Dishes | **44.3** | 39.07 | | General | 91.08 | **92.42** | | Average | **58.54** | 54.43 |
### Video benchmark
| Benchmarks | Ming-lite-omni | Qwen2.5VL-7B-Instruct | |:------------------------|:--------------:|:---------------------:| | VideoMME | 67.0 | 67.3 | | MVBench | 67.7 | 67.4 | | Video-MMMU | 46.3 | 47.4 | | LongVideoBench | 56.6 | 54.7 | | Average | 59.4 | 59.2 |
Note: All models are evaluated based on 128 uniformly sampled frames. ### Audio benchmark #### SpeechQA
| Model | Average | AlpacaEval | CommonEval | SD-QA | MMSU | OpenBookQA | IFEval | AdvBench | |:-----------------|:-------------:|:-----------:|:-----------:|:------------:|:------------:|:------------:|:------------:|:-------------:| | Qwen2-Audio-chat | 3.545 | 3.69 | 3.40 | 35.35 | 35.43 | 49.01 | 22.57 | 98.85 | | Baichuan-Audio | 3.695 | 4.00 | 3.39 | 49.64 | 48.80 | 63.30 | 41.32 | 86.73 | | GLM-4-Voice | 3.77 | 4.06 | 3.48 | 43.31 | 40.11 | 52.97 | 24.91 | 88.08 | | Kimi-Audio | 4.215 | 4.46 | 3.97 | 63.12 | 62.17 | 83.52 | 61.10 | 100.00 | | Qwen2.5-Omni | 4.21 | 4.49 | 3.93 | 55.71 | 61.32 | 81.10 | 52.87 | 99.42 | | Ming-lite-omni | 4.34 | 4.63 | 4.06 | 58.84 | 47.53 | 61.98 | 58.36 | 99.04 |
#### ASR
| Model | aishell1 | aishell2_android | aishell2_ios | cv15_zh | fleurs_zh | wenetspeech_meeting | wenetspeech_net | librispeech_test_clean | librispeech_test_other | multilingual_librispeech | cv15_en | fleurs_en | voxpopuli_v1.0_en | |:--------------:|:--------:|:----------------:|:------------:|:--------:|:---------:|:-------------------:|:---------------:|:----------------------:|:----------------------:|:------------------------:|:--------:|:---------:|:--------------------:| | Ming-lite-omni | 1.47 | **2.55** | **2.52** | 6.31 | 2.96 | 5.95 | 5.46 | 1.44 | 2.80 | **4.15** | **6.89** | **3.39** | **5.80** | | Qwen2.-Omni | 1.18 | 2.75 | 2.63 | **5.20** | 3.00 | **5.90** | 7.70 | 1.80 | 3.40 | 7.56 | 7.60 | 4.10 | **5.80** | | Qwen2-Audio | 1.53 | 2.92 | 2.92 | 6.90 | 7.50 | 7.16 | 8.42 | 1.60 | 3.60 | 5.40 | 8.60 | 6.90 | 6.84 | | Kimi-Audio | **0.60** | 2.64 | 2.56 | 7.21 | **2.69** | 6.28 | **5.37** | **1.28** | **2.42** | 5.88 | 10.31 | 4.44 | 7.97 |
### Information-Seeking Benchmark
| Model | InfoSeek_H-mean | InfoSeek_unseen_question | InfoSeek_unseen_entity | |:---------------|:---------------:|:------------------------:|:----------------------:| | GPT-4o | 36.05 | - | - | | PaLI-X | 22.06 | 23.5 | 20.8 | | Qwen2.5-vl-32B | 19.35 | 20.55 | 18.28 | | Ming-lite-omni | 27.7 | **30.4** | **25.4** |
### OCR
| Model | Ming-lite-omni | Qwen2.5-VL-7B-Instruct | |:-------------------|:--------------:|:-----------------------:| | ChartQA_TEST | 85.1 | 87.3 | | DocVQA_TEST | 93 | 95.7 | | OCRBenchV2_en/zh | 53.3/52 | 56.3/57.2 | | OmniDocBench↓ | 34/34.4 | 30.8/39.8 | | TextVQA_VAL | 82.8 | 84.9 |
### GUI
| Model | Ming-lite-omni | InternVL3 8B | Qwen2.5-VL-7B-Instruct | |:---------------------------|:--------------:|:------------:|:----------------------:| | ScreenSpot | 82.1 | 79.5 | 78.9* | | ScreenSpot-V2 | 84.1 | 81.4 | - | | AITZ(EM) | 66.6 | - | 57.6* |
Note: * denotes the reproduced results. ### Unified Generation Benchmark
| Model | single_object | two_object | counting | colors | position | color_attr | GENEVAL | DPGBench | FID↓ | |:---------------|:-------------:|:----------:|:----------:|:--------:|:--------:|:----------:|:--------:|:---------:|:-------------:| | Ming-lite-omni | **0.9875** | **0.7727** | **0.6812** | 0.7872 | 0.31 | 0.29 | **0.64** | 81.72 | **4.85** | | Metaquery-XL | - | - | - | - | - | - | 0.61 | **82.05** | 6.02 | | SDv2.1 | 0.98 | 0.51 | 0.44 | **0.85** | 0.07 | 0.17 | 0.50 | 68.09 | 26.96 | | Emu3-Gen | 0.98 | 0.71 | 0.34 | 0.81 | 0.17 | 0.21 | 0.54 | 80.60 | - | | SDXL | 0.98 | 0.74 | 0.39 | **0.85** | 0.15 | 0.23 | 0.55 | 74.65 | 8.76 | | Janus | 0.97 | 0.68 | 0.30 | 0.84 | **0.46** | **0.42** | 0.61 | 79.68 | 10.10 | | JanusFlow | - | - | - | - | - | - | 0.63 | 80.09 | 9.51 |
Please refer to our technical report for more comprehensive evaluation results. ## Model Downloads You can download the model from both Huggingface and ModelScope.
| **Model** | **Input modality** | **Oput modality** | **Download** | |:---------------| :---------------------: | :---------------: |:----------------------------------------------------------------------------------------------------------------------------------------------------:| | Ming-Lite-Omni | Image,text,viedio,audio | Image,text,audio | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ming-Lite-Omni)
[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ming-Lite-Omni) |
If you're in mainland China, we strongly recommend you to download our model from 🤖 ModelScope. ## Use Cases Additional demonstration cases are available on our project [page](https://lucaria-academy.github.io/Ming-Omni/). ## Example Usage Please download our model following [Model Downloads](#model-downloads), then you can refer to the following codes to run Ming-lite-omni model. Python environment dependency installation. ```shell pip install -r requirements.txt pip install data/matcha_tts-0.0.5.1-cp38-cp38-linux_x86_64.whl pip install diffusers==0.33.0 pip install nvidia-cublas-cu12==12.4.5.8 # for H20 ``` Note: We test following examples on hardware of NVIDIA H800-80GB with CUDA 12.2. Loading inclusionAI/Ming-Lite-Omni in bfloat16 takes about 40890MB memory. ```python import os import torch from transformers import AutoProcessor, GenerationConfig from modeling_bailingmm import BailingMMNativeForConditionalGeneration # build model model = BailingMMNativeForConditionalGeneration.from_pretrained( "inclusionAI/Ming-Lite-Omni", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True ).to("cuda") assets_path = YOUR_ASSETS_PATH # build processor processor = AutoProcessor.from_pretrained("inclusionAI/Ming-Lite-Omni", trust_remote_code=True) ``` ```python # qa messages = [ { "role": "HUMAN", "content": [ {"type": "text", "text": "请详细介绍鹦鹉的生活习性。"} ], }, ] # Output: # 鹦鹉是一种非常聪明和社交性强的鸟类，它们的生活习性非常丰富和有趣。以下是一些关于鹦鹉生活习性的详细介绍： # ### 1. **栖息地** # 鹦鹉主要分布在热带和亚热带地区，包括非洲、亚洲、澳大利亚和南美洲。它们通常生活在森林、草原、沙漠和城市环境中。不同种类的鹦鹉对栖息地的要求有所不同，但大多数鹦鹉喜欢有丰富植被和水源的地方。 # ### 2. **饮食** # 鹦鹉是杂食性动物，它们的饮食非常多样化。它们的食物包括种子、坚果、水果、蔬菜、花蜜和昆虫。鹦鹉的喙非常强壮，能够轻松地打开坚硬的果壳和坚果。一些鹦鹉还会吃泥土或沙子，以帮助消化和补充矿物质。 # ...... ``` ```python # image qa messages = [ { "role": "HUMAN", "content": [ {"type": "image", "image": os.path.join(assets_path, "flowers.jpg")}, {"type": "text", "text": "What kind of flower is this?"}, ], }, ] # Output: # The flowers in this image are forget-me-nots. These delicate blooms are known for their small, five-petaled flowers that come in various shades of blue, pink, and white. ``` To enable thinking before response, adding the following system prompt before your question: ```python cot_prompt = "SYSTEM: You are a helpful assistant. When the user asks a question, your response must include two parts: first, the reasoning process enclosed in ... tags, then the final answer enclosed in ... tags. The critical answer or key result should be placed within \\boxed{}.\n" # And your input message should be like this: messages = [ { "role": "HUMAN", "content": [ {"type": "image", "image": os.path.join(assets_path, "reasoning.png")}, {"type": "text", "text": cot_prompt + "In the rectangle $A B C D$ pictured, $M_{1}$ is the midpoint of $D C, M_{2}$ the midpoint of $A M_{1}, M_{3}$ the midpoint of $B M_{2}$ and $M_{4}$ the midpoint of $C M_{3}$. Determine the ratio of the area of the quadrilateral $M_{1} M_{2} M_{3} M_{4}$ to the area of the rectangle $A B C D$.\nChoices:\n(A) $\frac{7}{16}$\n(B) $\frac{3}{16}$\n(C) $\frac{7}{32}$\n(D) $\frac{9}{32}$\n(E) $\frac{1}{5}$"}, ], }, ] # Output: # \\nOkay, so I have this problem about a rectangle ABCD ... (thinking process omitted) ... So, the correct answer is C.\n\\n\\\boxed{C}\\n\n ``` ```python # video qa messages = [ { "role": "HUMAN", "content": [ {"type": "video", "video": os.path.join(assets_path, "yoga.mp4")}, {"type": "text", "text": "What is the woman doing?"}, ], }, ] # Output: # The image shows a woman performing a yoga pose on a rooftop. She's in a dynamic yoga pose, with her arms and legs extended in various positions. ``` ```python # multi-turn chat messages = [ { "role": "HUMAN", "content": [ {"type": "text", "text": "中国的首都是哪里？"}, ], }, { "role": "ASSISTANT", "content": [ {"type": "text", "text": "北京"}, ], }, { "role": "HUMAN", "content": [ {"type": "text", "text": "它的占地面积是多少？有多少常住人口？"}, ], }, ] # Output: # 北京市的总面积约为16,410.54平方公里，常住人口约为21,542,000人。 ``` ```python # Preparation for inference text = processor.apply_chat_template(messages, add_generation_prompt=True) image_inputs, video_inputs, audio_inputs = processor.process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, audios=audio_inputs, return_tensors="pt", ) inputs = inputs.to(model.device) for k in inputs.keys(): if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats": inputs[k] = inputs[k].to(dtype=torch.bfloat16) # call generate generation_config = GenerationConfig.from_dict({'no_repeat_ngram_size': 10}) generated_ids = model.generate( **inputs, max_new_tokens=512, use_cache=True, eos_token_id=processor.gen_terminator, generation_config=generation_config, ) generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] print(output_text) ``` ### Audio tasks ```python # ASR messages = [ { "role": "HUMAN", "content": [ {"type": "text", "text": "Please recognize the language of this speech and transcribe it. Format: oral."}, {"type": "audio", "audio": 'data/wavs/BAC009S0915W0292.wav'}, ], }, ] # we use whisper encoder for ASR task, so need modify code above inputs = processor( text=[text], images=image_inputs, videos=video_inputs, audios=audio_inputs, return_tensors="pt", audio_kwargs={'use_whisper_encoder': True} ) outputs = model.generate( **inputs, max_new_tokens=512, use_cache=True, eos_token_id=processor.gen_terminator, generation_config=generation_config, use_whisper_encoder=True ) ``` ```python # speech2speech messages = [ { "role": "HUMAN", "content": [ {"type": "audio", "audio": 'data/wavs/speechQA_sample.wav'}, ], }, ] generation_config = GenerationConfig.from_dict({ 'output_hidden_states': True, 'return_dict_in_generate': True, 'no_repeat_ngram_size': 10} ) outputs = model.generate( **inputs, max_new_tokens=512, use_cache=True, eos_token_id=processor.gen_terminator, generation_config=generation_config, use_whisper_encoder=False ) generated_ids = outputs.sequences generated_ids_trimmed = [ out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) ] # speechQA result output_text = processor.batch_decode( generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False )[0] # for TTS from modeling_bailing_talker import AudioDetokenizer model_name_or_path = model.config._name_or_path audio_detokenizer = AudioDetokenizer( f'{model_name_or_path}/talker/audio_detokenizer.yaml', flow_model_path=f'{model_name_or_path}/talker/flow.pt', hifigan_model_path=f'{model_name_or_path}/talker/hift.pt' ) spk_input = torch.load('data/spks/luna.pt') thinker_reply_part = outputs.hidden_states[0][0] + outputs.hidden_states[0][-1] # Setting thinker_reply_part to None allows the talker to operate as a standalone TTS model, independent of the language model. audio_tokens = model.talker.omni_audio_generation( output_text, thinker_reply_part=thinker_reply_part, **spk_input) waveform = audio_detokenizer.token2wav(audio_tokens, save_path='out.wav', **spk_input) ``` ```python # zero-shot TTS from modeling_bailing_talker import AudioDetokenizer from audio_detokenizer.cli.frontend import TTSFrontEnd from hyperpyyaml import load_hyperpyyaml model_name_or_path = model.config._name_or_path audio_detokenizer = AudioDetokenizer( f'{model_name_or_path}/talker/audio_detokenizer.yaml', flow_model_path=f'{model_name_or_path}/talker/flow.pt', hifigan_model_path=f'{model_name_or_path}/talker/hift.pt' ) with open(f'{model_name_or_path}/talker/audio_detokenizer.yaml', 'r') as f: configs = load_hyperpyyaml(f) audio_frontend = TTSFrontEnd( configs["feat_extractor"], f'{model_name_or_path}/talker/campplus.onnx', f'{model_name_or_path}/talker/speech_tokenizer_v1.onnx', ) tts_text = "这是一条测试语句。" spk_input = audio_frontend.frontend_zero_shot(prompt_text="感谢你的认可。", prompt_wav_path="data/spks/prompt.wav") audio_tokens = model.talker.omni_audio_generation(tts_text, **spk_input) waveform = audio_detokenizer.token2wav(audio_tokens, save_path='out.wav', **spk_input) ``` For detailed usage for ASR, SpeechQA, and TTS tasks, please refer to `test_audio_tasks.py` ### Image Generation & Edit Ming-omni natively supports image generation and image editing. To use this function, you only need to add the corresponding parameters in the generate function. ```python # Image generation mode currently limits the range of input pixels. gen_input_pixels = 451584 processor.max_pixels = gen_input_pixels processor.min_pixels = gen_input_pixels def generate(messages, processor, model, **image_gen_param): text = processor.apply_chat_template(messages, add_generation_prompt=True) image_inputs, video_inputs, audio_inputs = processor.process_vision_info(messages) inputs = processor( text=[text], images=image_inputs, videos=video_inputs, audios=audio_inputs, return_tensors="pt", ).to(model.device) for k in inputs.keys(): if k == "pixel_values" or k == "pixel_values_videos" or k == "audio_feats": inputs[k] = inputs[k].to(dtype=torch.bfloat16) print(image_gen_param) image = model.generate( **inputs, image_gen=True, **image_gen_param, ) return image ``` Text-to-image ```python messages = [ { "role": "HUMAN", "content": [ {"type": "text", "text": "Draw a girl with short hair."}, ], } ] image = generate( messages=messages, processor=processor, model=model, image_gen_cfg=6.0, image_gen_steps=20, image_gen_width=480, image_gen_height=544 ) image.save("./t2i.jpg") ``` Edit ```python messages = [ { "role": "HUMAN", "content": [ {"type": "image", "image": "samples/cake.jpg"}, {"type": "text", "text": "add a candle on top of the cake"}, ], } ] image = generate( messages=messages, processor=processor, model=model, image_gen_cfg=6.0, image_gen_steps=20, image_gen_width=512, image_gen_height=512 ) image.save("./edit.jpg") ``` ## License and Legal Disclaimer This code repository is licensed under the [MIT License](../LICENSE), and the Legal Disclaimer is located in the [LEGAL.md file](../LEGAL.md) under the project's root directory. ## Citation If you find our work helpful, feel free to give us a cite. ```bibtex @misc{Mingomni2025, title = {Ming-Omni: A Unified Multimodal Model for Perception and Generation}, author = {Inclusion AI}, year = {2025}, eprint = {2506.09344}, archivePrefix = {arXiv}, url = {https://arxiv.org/abs/2506.09344} } ```