**A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone** [中文](./README_zh.md) | English Join our 💬 WeChat | View MiniCPM-V 📖 best practices

MiniCPM-V 2.6 🤗 🤖 | MiniCPM-Llama3-V 2.5 🤗 🤖 | MiniCPM-Llama3-V 2.5 Technical Report

MiniCPM-V is a series of end-side multimodal LLMs (MLLMs) designed for vision-language understanding. The models take image, video and text as inputs and provide high-quality text outputs. Since February 2024, we have released 5 versions of the model, aiming to achieve strong performance and efficient deployment. The most notable models in this series currently include:

• MiniCPM-V 2.6: 🔥🔥🔥 The latest and most capable model in the MiniCPM-V series. With a total of 8B parameters, the model surpasses GPT-4V in single image, multi-image and video understanding. It outperforms GPT-4o mini, Gemini 1.5 Pro and Claude 3.5 Sonnet in single image understanding, and advances MiniCPM-Llama3-V 2.5's features such as strong OCR capability, trustworthy behavior, multilingual support, and end-side deployment. Due to its superior token density, MiniCPM-V 2.6 can for the first time support real-time video understanding on end-side devices such as iPad.

• MiniCPM-V 2.0: The lightest model in the MiniCPM-V series. With 2B parameters, it surpasses larger models such as Yi-VL 34B, CogVLM-Chat 17B, and Qwen-VL-Chat 10B in overall performance. It can accept image inputs of any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving comparable performance with Gemini Pro in understanding scene-text and matches GPT-4V in low hallucination rates.

News

📌 Pinned

• [2024.08.17] 🚀🚀🚀 MiniCPM-V 2.6 is now fully supported by official llama.cpp! GGUF models of various sizes are available here.
• [2024.08.15] We now also support multi-image SFT. For more details, please refer to the document.
• [2024.08.14] MiniCPM-V 2.6 now also supports fine-tuning with the SWIFT framework!
• [2024.08.10] 🚀🚀🚀 MiniCPM-Llama3-V 2.5 is now fully supported by official llama.cpp! GGUF models of various sizes are available here.
• [2024.08.06] 🔥🔥🔥 We open-source MiniCPM-V 2.6, which outperforms GPT-4V on single image, multi-image and video understanding. It advances popular features of MiniCPM-Llama3-V 2.5, and can support real-time video understanding on iPad. Try it now!
• [2024.08.03] MiniCPM-Llama3-V 2.5 technical report is released! See here.
• [2024.07.19] MiniCPM-Llama3-V 2.5 supports vLLM now! See here.
• [2024.05.28] 💫 We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics here.
• [2024.05.23] 🔍 We've released a comprehensive comparison between Phi-3-vision-128k-instruct and MiniCPM-Llama3-V 2.5, including benchmarks evaluations, multilingual capabilities, and inference efficiency 🌟📊🌍🚀. Click here to view more details.
• [2024.05.23] 🔥🔥🔥 MiniCPM-V tops GitHub Trending and Hugging Face Trending! Our demo, recommended by Hugging Face Gradio’s official account, is available here. Come and try it out!

Contents

• MiniCPM-V 2.6
• MiniCPM-Llama3-V 2.5
• MiniCPM-V 2.0
• Chat with Our Demo on Gradio 🤗
• Install
• Inference
  • Model Zoo
  • Multi-turn Conversation
  • Chat with multiple images
  • In-context few-shot learning
  • Chat with video
  • Inference on Multiple GPUs
  • Inference on Mac
  • Deployment on Mobile Phone
  • Inference with llama.cpp
  • Inference with ollama
  • Inference with vLLM
• Fine-tuning
• FAQs

MiniCPM-V 2.6

MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:

• 🔥 Leading Performance. MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding.

• 🖼️ Multi Image Understanding and In-context Learning. MiniCPM-V 2.6 can also perform conversation and reasoning over multiple images. It achieves state-of-the-art performance on popular multi-image benchmarks such as Mantis-Eval, BLINK, Mathverse mv and Sciverse mv, and also shows promising in-context learning capability.

• 🎬 Video Understanding. MiniCPM-V 2.6 can also accept video inputs, performing conversation and providing dense captions for spatial-temporal information. It outperforms GPT-4V, Claude 3.5 Sonnet and LLaVA-NeXT-Video-34B on Video-MME with/without subtitles.

• 💪 Strong OCR Capability and Others. MiniCPM-V 2.6 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344). It achieves state-of-the-art performance on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V, and Gemini 1.5 Pro. Based on the the latest RLAIF-V and VisCPM techniques, it features trustworthy behaviors, with significantly lower hallucination rates than GPT-4o and GPT-4V on Object HalBench, and supports multilingual capabilities on English, Chinese, German, French, Italian, Korean, etc.

• 🚀 Superior Efficiency. In addition to its friendly size, MiniCPM-V 2.6 also shows state-of-the-art token density (i.e., number of pixels encoded into each visual token). It produces only 640 tokens when processing a 1.8M pixel image, which is 75% fewer than most models. This directly improves the inference speed, first-token latency, memory usage, and power consumption. As a result, MiniCPM-V 2.6 can efficiently support real-time video understanding on end-side devices such as iPad.

• 💫 Easy Usage. MiniCPM-V 2.6 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) int4 and GGUF format quantized models in 16 sizes, (3) vLLM support for high-throughput and memory-efficient inference, (4) fine-tuning on new domains and tasks, (5) quick local WebUI demo setup with Gradio, and (6) online web demo.

Evaluation

Examples

Click to view more cases.

We deploy MiniCPM-V 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition.

MiniCPM-Llama3-V 2.5

Click to view more details of MiniCPM-Llama3-V 2.5

**MiniCPM-Llama3-V 2.5** is the latest model in the MiniCPM-V series. The model is built on SigLip-400M and Llama3-8B-Instruct with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-V 2.0. Notable features of MiniCPM-Llama3-V 2.5 include: - 🔥 **Leading Performance.** MiniCPM-Llama3-V 2.5 has achieved an average score of 65.1 on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. **With only 8B parameters, it surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Claude 3 and Qwen-VL-Max** and greatly outperforms other Llama 3-based MLLMs. - 💪 **Strong OCR Capabilities.** MiniCPM-Llama3-V 2.5 can process images with any aspect ratio and up to 1.8 million pixels (e.g., 1344x1344), achieving a **700+ score on OCRBench, surpassing proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max and Gemini Pro**. Based on recent user feedback, MiniCPM-Llama3-V 2.5 has now enhanced full-text OCR extraction, table-to-markdown conversion, and other high-utility capabilities, and has further strengthened its instruction-following and complex reasoning abilities, enhancing multimodal interaction experiences. - 🏆 **Trustworthy Behavior.** Leveraging the latest [RLAIF-V](https://github.com/RLHF-V/RLAIF-V/) method (the newest technique in the [RLHF-V](https://github.com/RLHF-V) [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves a **10.3%** hallucination rate on Object HalBench, lower than GPT-4V-1106 (13.6%), achieving the best-level performance within the open-source community. [Data released](https://huggingface.co/datasets/openbmb/RLAIF-V-Dataset). - 🌏 **Multilingual Support.** Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from [VisCPM](https://github.com/OpenBMB/VisCPM), MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to **over 30 languages including German, French, Spanish, Italian, Korean etc.** [All Supported Languages](./assets/minicpm-llama-v-2-5_languages.md). - 🚀 **Efficient Deployment.** MiniCPM-Llama3-V 2.5 systematically employs **model quantization, CPU optimizations, NPU optimizations and compilation optimizations**, achieving high-efficiency deployment on end-side devices. For mobile phones with Qualcomm chips, we have integrated the NPU acceleration framework QNN into llama.cpp for the first time. After systematic optimization, MiniCPM-Llama3-V 2.5 has realized a **150x acceleration in end-side MLLM image encoding** and a **3x speedup in language decoding**. - 💫 **Easy Usage.** MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) [llama.cpp](https://github.com/OpenBMB/llama.cpp/blob/minicpm-v2.5/examples/minicpmv/README.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.5/examples/minicpm-v2.5) support for efficient CPU inference on local devices, (2) [GGUF](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) format quantized models in 16 sizes, (3) efficient [LoRA](https://github.com/OpenBMB/MiniCPM-V/tree/main/finetune#lora-finetuning) fine-tuning with only 2 V100 GPUs, (4) [streaming output](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5#usage), (5) quick local WebUI demo setup with [Gradio](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_2.5.py) and [Streamlit](https://github.com/OpenBMB/MiniCPM-V/blob/main/web_demo_streamlit-2_5.py), and (6) interactive demos on [HuggingFace Spaces](https://huggingface.co/spaces/openbmb/MiniCPM-Llama3-V-2_5). ### Evaluation

Click to view results on TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench.

Model	Size	OCRBench	TextVQA val	DocVQA test	Open-Compass	MME	MMB test (en)	MMB test (cn)	MMMU val	Math-Vista	LLaVA Bench	RealWorld QA	Object HalBench
Proprietary
Gemini Pro	-	680	74.6	88.1	62.9	2148.9	73.6	74.3	48.9	45.8	79.9	60.4	-
GPT-4V (2023.11.06)	-	645	78.0	88.4	63.5	1771.5	77.0	74.4	53.8	47.8	93.1	63.0	86.4
Open-source
Mini-Gemini	2.2B	-	56.2	34.2*	-	1653.0	-	-	31.7	-	-	-	-
Qwen-VL-Chat	9.6B	488	61.5	62.6	51.6	1860.0	61.8	56.3	37.0	33.8	67.7	49.3	56.2
DeepSeek-VL-7B	7.3B	435	64.7*	47.0*	54.6	1765.4	73.8	71.4	38.3	36.8	77.8	54.2	-
Yi-VL-34B	34B	290	43.4*	16.9*	52.2	2050.2	72.4	70.7	45.1	30.7	62.3	54.8	79.3
CogVLM-Chat	17.4B	590	70.4	33.3*	54.2	1736.6	65.8	55.9	37.3	34.7	73.9	60.3	73.6
TextMonkey	9.7B	558	64.3	66.7	-	-	-	-	-	-	-	-	-
Idefics2	8.0B	-	73.0	74.0	57.2	1847.6	75.7	68.6	45.2	52.2	49.1	60.7	-
Bunny-LLama-3-8B	8.4B	-	-	-	54.3	1920.3	77.0	73.9	41.3	31.5	61.2	58.8	-
LLaVA-NeXT Llama-3-8B	8.4B	-	-	78.2	-	1971.5	-	-	41.7	37.5	80.1	60.0	-
Phi-3-vision-128k-instruct	4.2B	639*	70.9	-	-	1537.5*	-	-	40.4	44.5	64.2*	58.8*	-
MiniCPM-V 1.0	2.8B	366	60.6	38.2	47.5	1650.2	64.1	62.6	38.3	28.9	51.3	51.2	78.4
MiniCPM-V 2.0	2.8B	605	74.1	71.9	54.5	1808.6	69.1	66.5	38.2	38.7	69.2	55.8	85.5
MiniCPM-Llama3-V 2.5	8.5B	725	76.6	84.8	65.1	2024.6	77.2	74.2	45.8	54.3	86.7	63.5	89.7

* We evaluate the officially released checkpoint by ourselves.

Evaluation results of multilingual LLaVA Bench

### Examples

MiniCPM-V 2.0

Legacy Models

Chat with Our Demo on Gradio 🤗

We provide online and local demos powered by Hugging Face Gradio , the most popular model deployment framework nowadays. It supports streaming outputs, progress bars, queuing, alerts, and other useful features.

Online Demo

Click here to try out the online demo of MiniCPM-V 2.6 | MiniCPM-Llama3-V 2.5 | MiniCPM-V 2.0.

Local WebUI Demo

You can easily build your own local WebUI demo with Gradio using the following commands.

pip install -r requirements.txt

# For NVIDIA GPUs, run:
python web_demo_2.6.py --device cuda

Install

1. Clone this repository and navigate to the source folder

git clone https://github.com/OpenBMB/MiniCPM-V.git
cd MiniCPM-V

2. Create conda environment

conda create -n MiniCPM-V python=3.10 -y
conda activate MiniCPM-V

3. Install dependencies

pip install -r requirements.txt

Inference

Model Zoo

Model	Device	Memory	Description	Download
MiniCPM-V 2.6	GPU	17 GB	The latest version, achieving state-of-the-art end-side performance for single image, multi-image and video understanding.	🤗
MiniCPM-V 2.6 gguf	CPU	6 GB	The gguf version, lower memory usage and faster inference.	🤗
MiniCPM-V 2.6 int4	GPU	7 GB	The int4 quantized version, lower GPU memory usage.	🤗
MiniCPM-Llama3-V 2.5	GPU	19 GB	Strong end-side multimodal performance.	🤗
MiniCPM-Llama3-V 2.5 gguf	CPU	6 GB	The gguf version, lower memory usage and faster inference.	🤗
MiniCPM-Llama3-V 2.5 int4	GPU	8 GB	The int4 quantized version, lower GPU memory usage.	🤗
MiniCPM-V 2.0	GPU	8 GB	Light version, balance the performance the computation cost.	🤗
MiniCPM-V 1.0	GPU	7 GB	Lightest version, achieving the fastest inference.	🤗

Multi-turn Conversation

Please refer to the following codes to run.

import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

torch.manual_seed(0)

model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True,
    attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager
model = model.eval().cuda()
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True)

image = Image.open('./assets/airplane.jpeg').convert('RGB')

# First round chat 
question = "Tell me the model of this aircraft."
msgs = [{'role': 'user', 'content': [image, question]}]

answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)

# Second round chat 
# pass history context of multi-turn conversation
msgs.append({"role": "assistant", "content": [answer]})
msgs.append({"role": "user", "content": ["Introduce something about Airbus A380."]})

answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)

You will get the following output:

"The aircraft in the image is an Airbus A380, which can be identified by its large size, double-deck structure, and the distinctive shape of its wings and engines. The A380 is a wide-body aircraft known for being the world's largest passenger airliner, designed for long-haul flights. It has four engines, which are characteristic of large commercial aircraft. The registration number on the aircraft can also provide specific information about the model if looked up in an aviation database."

"The Airbus A380 is a double-deck, wide-body, four-engine jet airliner made by Airbus. It is the world's largest passenger airliner and is known for its long-haul capabilities. The aircraft was developed to improve efficiency and comfort for passengers traveling over long distances. It has two full-length passenger decks, which can accommodate more passengers than a typical single-aisle airplane. The A380 has been operated by airlines such as Lufthansa, Singapore Airlines, and Emirates, among others. It is widely recognized for its unique design and significant impact on the aviation industry."

Chat with multiple images

In-context few-shot learning

Chat with video

Inference on Multiple GPUs

You can run MiniCPM-Llama3-V 2.5 on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model's layers across multiple GPUs. Please refer to this tutorial for detailed instructions on how to load the model and inference using multiple low VRAM GPUs.

Inference on Mac

Deployment on Mobile Phone

MiniCPM-V 2.0 can be deployed on mobile phones with Android operating systems. 🚀 Click MiniCPM-V 2.0 to install apk.

Inference with llama.cpp

MiniCPM-V 2.6 can run with llama.cpp now! See our fork of llama.cpp for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4).

Inference with ollama

MiniCPM-V 2.6 can run with ollama now! See our fork of ollama for more detail. This implementation supports smooth inference of 16~18 token/s on iPad (test environment：iPad Pro + M4).

Inference with vLLM

vLLM now officially supports MiniCPM-V 2.6, MiniCPM-Llama3-V 2.5 and MiniCPM-V 2.0, Click to see.

1. Install vLLM(>=0.5.4): ```shell pip install vllm ``` 2. Install timm: (optional, MiniCPM-V 2.0 need timm) ```shell pip install timm==0.9.10 ``` 3. Run the example(for image): ```python from transformers import AutoTokenizer from PIL import Image from vllm import LLM, SamplingParams MODEL_NAME = "openbmb/MiniCPM-V-2_6" # Also available for previous models # MODEL_NAME = "openbmb/MiniCPM-Llama3-V-2_5" # MODEL_NAME = "HwwwH/MiniCPM-V-2" image = Image.open("xxx.png").convert("RGB") tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True) llm = LLM( model=MODEL_NAME, trust_remote_code=True, gpu_memory_utilization=1, max_model_len=2048 ) messages = [{ "role": "user", "content": # Number of images "(./)" + \ "\nWhat is the content of this image?" }] prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) # Single Inference inputs = { "prompt": prompt, "multi_modal_data": { "image": image # Multi images, the number of images should be equal to that of `(./)` # "image": [image, image] }, } # Batch Inference # inputs = [{ # "prompt": prompt, # "multi_modal_data": { # "image": image # }, # } for _ in 2] # 2.6 stop_tokens = ['<|im_end|>', '<|endoftext|>'] stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens] # 2.0 # stop_token_ids = [tokenizer.eos_id] # 2.5 # stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id] sampling_params = SamplingParams( stop_token_ids=stop_token_ids, use_beam_search=True, temperature=0, best_of=3, max_tokens=1024 ) outputs = llm.generate(inputs, sampling_params=sampling_params) print(outputs[0].outputs[0].text) ``` 4. click [here](https://modelbest.feishu.cn/wiki/C2BWw4ZP0iCDy7kkCPCcX2BHnOf?from=from_copylink) if you want to use it with *video*, or get more details about `vLLM`.

Fine-tuning

Simple Fine-tuning

We support simple fine-tuning with Hugging Face for MiniCPM-V 2.0 and MiniCPM-Llama3-V 2.5.

Reference Document

With the SWIFT Framework

We now support MiniCPM-V series fine-tuning with the SWIFT framework. SWIFT supports training, inference, evaluation and deployment of nearly 200 LLMs and MLLMs . It supports the lightweight training solutions provided by PEFT and a complete Adapters Library including techniques such as NEFTune, LoRA+ and LLaMA-PRO.

Best Practices：MiniCPM-V 1.0, MiniCPM-V 2.0, MiniCPM-V 2.6.

FAQs

Click here to view the FAQs

Model License

• This repository is released under the Apache-2.0 License.

• The usage of MiniCPM-V model weights must strictly follow MiniCPM Model License.md.

• The models and weights of MiniCPM are completely free for academic research. after filling out a "questionnaire" for registration, are also available for free commercial use.

Statement

As LMMs, MiniCPM-V models (including OmniLMM) generate contents by learning a large amount of multimodal corpora, but they cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V models does not represent the views and positions of the model developers

We will not be liable for any problems arising from the use of MiniCPM-V models, including but not limited to data security issues, risk of public opinion, or any risks and problems arising from the misdirection, misuse, dissemination or misuse of the model.

Institutions

This project is developed by the following institutions:

• THUNLP
• ModelBest
• Zhihu

🌟 Star History

Key Techniques and Other Multimodal Projects

👏 Welcome to explore key techniques of MiniCPM-V and other multimodal projects of our team:

VisCPM | RLHF-V | LLaVA-UHD | RLAIF-V

Citation

If you find our model/code/paper helpful, please consider cite our papers 📝 and star us ⭐️！

@article{yao2024minicpm,
  title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone},
  author={Yao, Yuan and Yu, Tianyu and Zhang, Ao and Wang, Chongyi and Cui, Junbo and Zhu, Hongji and Cai, Tianchi and Li, Haoyu and Zhao, Weilin and He, Zhihui and others},
  journal={arXiv preprint arXiv:2408.01800},
  year={2024}
}