```python
from chat import MiniCPMVChat, img2base64
import torch
import json
torch.manual_seed(0)
chat_model = MiniCPMVChat('openbmb/MiniCPM-Llama3-V-2_5')
im_64 = img2base64('./assets/airplane.jpeg')
# First round chat
msgs = [{"role": "user", "content": "Tell me the model of this aircraft."}]
inputs = {"image": im_64, "question": json.dumps(msgs)}
answer = chat_model.chat(inputs)
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."})
inputs = {"image": im_64, "question": json.dumps(msgs)}
answer = chat_model.chat(inputs)
print(answer)
```
可以得到以下输出:
```
"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."
```
### Mac 推理
点击查看 MiniCPM-Llama3-V 2.5 / MiniCPM-V 2.0 基于Mac MPS运行 (Apple silicon 或 AMD GPUs)的示例。
```python
# test.py Need more than 16GB memory to run.
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, low_cpu_mem_usage=True)
model = model.to(device='mps')
tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True)
model.eval()
image = Image.open('./assets/hk_OCR.jpg').convert('RGB')
question = 'Where is this photo taken?'
msgs = [{'role': 'user', 'content': question}]
answer, context, _ = model.chat(
image=image,
msgs=msgs,
context=None,
tokenizer=tokenizer,
sampling=True
)
print(answer)
```
运行:
```shell
PYTORCH_ENABLE_MPS_FALLBACK=1 python test.py
```
### 手机端部署
MiniCPM-Llama3-V 2.5 和 MiniCPM-V 2.0 可运行在Android手机上，点击[MiniCPM-Llama3-V 2.5](http://minicpm.modelbest.cn/android/modelbest-release-20240528_182155.apk) / [MiniCPM-V 2.0](https://github.com/OpenBMB/mlc-MiniCPM)安装apk使用;
### 本地WebUI Demo部署
点击查看本地WebUI demo 在 NVIDIA GPU、Mac等不同设备部署方法
```shell
pip install -r requirements.txt
```
```shell
# For NVIDIA GPUs, run:
python web_demo_2.5.py --device cuda
# For Mac with MPS (Apple silicon or AMD GPUs), run:
PYTORCH_ENABLE_MPS_FALLBACK=1 python web_demo_2.5.py --device mps
```
### llama.cpp 部署
MiniCPM-Llama3-V 2.5 现在支持llama.cpp啦! 用法请参考我们的fork [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv)， 在手机上可以支持 6~8 token/s 的流畅推理（测试环境：Xiaomi 14 pro + Snapdragon 8 Gen 3）。
### vLLM 部署 点击查看 MiniCPM-V 2.0 利用vLLM 部署运行的方法（MiniCPM-Llama3-V 2.5 支持vLLM将在近期推出）
由于我们对 vLLM 提交的 PR 还在 review 中，因此目前我们 fork 了一个 vLLM 仓库以供测试使用。
1. 首先克隆我们 fork 的 vLLM 库:
```shell
git clone https://github.com/OpenBMB/vllm.git
```
2. 安装 vLLM 库:
```shell
cd vllm
pip install -e .
```
3. 安装 timm 库:
```shell
pip install timm=0.9.10
```
4. 测试运行示例程序:
```shell
python examples/minicpmv_example.py
```
## 微调
### 简易微调
我们支持使用 Huggingface Transformers 库简易地微调 MiniCPM-V 2.0 和 MiniCPM-Llama3-V 2.5 模型。
[参考文档](./finetune/readme.md)
### 使用 SWIFT 框架
我们支持使用 SWIFT 框架微调 MiniCPM-V 系列模型。SWIFT 支持近 200 种大语言模型和多模态大模型的训练、推理、评测和部署。支持 PEFT 提供的轻量训练方案和完整的 Adapters 库支持的最新训练技术如 NEFTune、LoRA+、LLaMA-PRO 等。
参考文档：[MiniCPM-V 1.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v最佳实践.md)，[MiniCPM-V 2.0](https://github.com/modelscope/swift/blob/main/docs/source/Multi-Modal/minicpm-v-2最佳实践.md)
## 未来计划
- [x] 支持 MiniCPM-V 系列模型微调
- [ ] 实时多模态交互代码开源
## 模型协议
* 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源
* MiniCPM-V 模型权重的使用则需要遵循 [“MiniCPM模型商用许可协议.md”](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%E6%A8%A1%E5%9E%8B%E5%95%86%E7%94%A8%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE.md)。
* MiniCPM 模型权重对学术研究完全开放，在填写[“问卷”](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)进行登记后亦允许免费商业使用。
## 声明
作为多模态大模型，MiniCPM-V 系列模型（包括 OmniLMM）通过学习大量的多模态数据来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。
因此用户在使用本项目的系列模型生成的内容时，应自行负责对其进行评估和验证。如果由于使用本项目的系列开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。
## 机构
本项目由以下机构共同开发：
- [清华大学自然语言处理实验室](https://nlp.csai.tsinghua.edu.cn/)
- [面壁智能](https://modelbest.cn/)
- [知乎](https://www.zhihu.com/ )
## 其他多模态项目
👏 欢迎了解我们更多的多模态项目：
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
## 🌟 Star History
## 引用
如果您觉得我们模型/代码/论文有帮助，请给我们 ⭐ 和 引用 📝，感谢！
```bib
@article{yu2023rlhf,
title={Rlhf-v: Towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback},
author={Yu, Tianyu and Yao, Yuan and Zhang, Haoye and He, Taiwen and Han, Yifeng and Cui, Ganqu and Hu, Jinyi and Liu, Zhiyuan and Zheng, Hai-Tao and Sun, Maosong and others},
journal={arXiv preprint arXiv:2312.00849},
year={2023}
}
@article{viscpm,
title={Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages},
author={Jinyi Hu and Yuan Yao and Chongyi Wang and Shan Wang and Yinxu Pan and Qianyu Chen and Tianyu Yu and Hanghao Wu and Yue Zhao and Haoye Zhang and Xu Han and Yankai Lin and Jiao Xue and Dahai Li and Zhiyuan Liu and Maosong Sun},
journal={arXiv preprint arXiv:2308.12038},
year={2023}
}
@article{xu2024llava-uhd,
title={{LLaVA-UHD}: an LMM Perceiving Any Aspect Ratio and High-Resolution Images},
author={Xu, Ruyi and Yao, Yuan and Guo, Zonghao and Cui, Junbo and Ni, Zanlin and Ge, Chunjiang and Chua, Tat-Seng and Liu, Zhiyuan and Huang, Gao},
journal={arXiv preprint arXiv:2403.11703},
year={2024}
}
@article{yu2024rlaifv,
title={RLAIF-V: Aligning MLLMs through Open-Source AI Feedback for Super GPT-4V Trustworthiness},
author={Yu, Tianyu and Zhang, Haoye and Yao, Yuan and Dang, Yunkai and Chen, Da and Lu, Xiaoman and Cui, Ganqu and He, Taiwen and Liu, Zhiyuan and Chua, Tat-Seng and Sun, Maosong},
journal={arXiv preprint arXiv:2405.17220},
year={2024}
}
```
**端侧可用的 GPT-4V 级多模态大模型**
中文 |
[English](./README_en.md)
加入我们的 💬 微信社区
](https://modelscope.cn/models/OpenBMB/MiniCPM-Llama3-V-2_5) |
| MiniCPM-Llama3-V 2.5 gguf| CPU | 5 GB | gguf 版本，更低的内存占用和更高的推理效率。 | [🤗](https://huggingface.co/openbmb/MiniCPM-Llama3-V-2_5-gguf) [
[清华大学自然语言处理实验室](https://nlp.csai.tsinghua.edu.cn/)
- 
[面壁智能](https://modelbest.cn/)
- 
[知乎](https://www.zhihu.com/ )
## 其他多模态项目
👏 欢迎了解我们更多的多模态项目：
[VisCPM](https://github.com/OpenBMB/VisCPM/tree/main) | [RLHF-V](https://github.com/RLHF-V/RLHF-V) | [LLaVA-UHD](https://github.com/thunlp/LLaVA-UHD) | [RLAIF-V](https://github.com/RLHF-V/RLAIF-V)
## 🌟 Star History
