openbmb/MiniCPM-Llama3-V-2_5
huggingface.co/openbmb/MiniCPM-Llama3-V-2_5
Model card
A GPT-4V Level Multimodal LLM on Your Phone
News
π Pinned
- [2025.01.14] π₯π₯ π₯ We open source MiniCPM-o 2.6, with significant performance improvement over MiniCPM-V 2.6, and support real-time speech-to-speech conversation and multimodal live streaming. Try it now.
- [2024.08.10] πππ MiniCPM-Llama3-V 2.5 is now fully supported by official llama.cpp! GGUF models of various sizes are available here.
- [2024.08.06] π₯π₯π₯ 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] π« We now support LoRA fine-tuning for MiniCPM-Llama3-V 2.5, using only 2 V100 GPUs! See more statistics here.
- [2024.05.23] π₯π₯π₯ MiniCPM-V tops GitHub Trending and HuggingFace Trending! Our demo, recommended by Hugging Face Gradioβs official account, is available here. Come and try it out!
- [2024.05.20] We open-soure MiniCPM-Llama3-V 2.5, it has improved OCR capability and supports 30+ languages, representing the first end-side MLLM achieving GPT-4V level performance! We provide efficient inference and simple fine-tuning. Try it now!
Model Summary
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:
- π₯ 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.
- πͺ 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 an 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.
- π Trustworthy Behavior. Leveraging the latest RLAIF-V method (the newest technology in the RLHF-V [CVPR'24] series), MiniCPM-Llama3-V 2.5 exhibits more trustworthy behavior. It achieves 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.
- π Multilingual Support. Thanks to the strong multilingual capabilities of Llama 3 and the cross-lingual generalization technique from VisCPM, MiniCPM-Llama3-V 2.5 extends its bilingual (Chinese-English) multimodal capabilities to over 30 languages including German, French, Spanish, Italian, Korean, Japanese etc. All Supported Languages.
- π Efficient Deployment. MiniCPM-Llama3-V 2.5 systematically employs model quantization, CPU optimizations, NPU optimizations and compilation optimizations, achieving high-efficiency deployment on edge 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 150-fold acceleration in multimodal large model end-side image encoding and a 3-fold increase in language decoding speed.
- π« Easy Usage. MiniCPM-Llama3-V 2.5 can be easily used in various ways: (1) llama.cpp and ollama support for efficient CPU inference on local devices, (2) GGUF format quantized models in 16 sizes, (3) efficient LoRA fine-tuning with only 2 V100 GPUs, (4) streaming output, (5) quick local WebUI demo setup with Gradio and Streamlit, and (6) interactive demos on HuggingFace Spaces.
Evaluation
Results on TextVQA, DocVQA, OCRBench, OpenCompass MultiModal Avg , MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, Object HalBench.
Evaluation results of multilingual LLaVA Bench
Examples
We deploy MiniCPM-Llama3-V 2.5 on end devices. The demo video is the raw screen recording on a Xiaomi 14 Pro without edition.
Demo
Click here to try out the Demo of MiniCPM-Llama3-V 2.5.
Deployment on Mobile Phone
Coming soon.
Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10οΌ
``
Pillow==10.1.0
torch==2.1.2
torchvision==0.16.2
transformers==4.40.0
sentencepiece==0.1.99
``
```python # test.py import torch from PIL import Image from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16) model = model.to(device='cuda')
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True) model.eval()
image = Image.open('xx.jpg').convert('RGB') question = 'What is in the image?' msgs = [{'role': 'user', 'content': question}]
res = model.chat( image=image, msgs=msgs, tokenizer=tokenizer, sampling=True, # if sampling=False, beam_search will be used by default temperature=0.7, # system_prompt='' # pass system_prompt if needed ) print(res)
if you want to use streaming, please make sure sampling=True and stream=True
## the model.chat will return a generator res = model.chat( image=image, msgs=msgs, tokenizer=tokenizer, sampling=True, temperature=0.7, stream=True )
generated_text = "" for new_text in res: generated_text += new_text print(new_text, flush=True, end='') ```
Please look at GitHub for more detail about usage.
Inference with llama.cpp
MiniCPM-Llama3-V 2.5 can run with llama.cpp now! See our fork of llama.cpp for more detail.
Int4 quantized version
Download the int4 quantized version for lower GPU memory (8GB) usage: MiniCPM-Llama3-V-2_5-int4.
MiniCPM-V 2.0
Please see the info about MiniCPM-V 2.0 here.
License
#### Model License * The code in this repo is released under the Apache-2.0 License. * The usage of MiniCPM-V series 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 an LLM, MiniCPM-Llama3-V 2.5 generates contents by learning a large mount of texts, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-Llama3-V 2.5 does not represent the views and positions of the model developers
- We will not be liable for any problems arising from the use of the MinCPM-V open Source model, 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.
Key Techniques and Other Multimodal Projects
π Welcome to explore key techniques of MiniCPM-V 2.6 and other multimodal projects of our team:
VisCPM | RLHF-V | LLaVA-UHD | RLAIF-V
Citation
If you find our work helpful, please consider citing our papers π and liking this project β€οΈοΌ
``bib
@article{yao2024minicpmv,
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 Chen, Qianyu and Zhou, Huarong and Zou, Zhensheng and Zhang, Haoye and Hu, Shengding and Zheng, Zhi and Zhou, Jie and Cai, Jie and Han, Xu and Zeng, Guoyang and Li, Dahai and Liu, Zhiyuan and Sun, Maosong},
journal={arXiv preprint 2408.01800},
year={2024},
}
``