README.md

---
datasets:
- openbmb/RLAIF-V-Dataset
language:
- multilingual
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- minicpm-v
- vision
- ocr
- multi-image
- video
- custom_code
---
# MiniCPM-V-2_6-RK3588-1.1.1

This version of MiniCPM-V-2_6 has been converted to run on the RK3588 NPU using {'w8a8'} quantization.

This model has been optimized with the following LoRA: 

Compatible with RKLLM version: 1.1.1

###Useful links:
[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) 

[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) 

[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) 

Pretty much anything by these folks: (marty1885)[https://github.com/marty1885] and [happyme531](https://huggingface.co/happyme531) 

# Original Model Card for base model, MiniCPM-V-2_6, below:


<h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1>

[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](http://120.92.209.146:8887/)</a> 


## 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](https://github.com/RLHF-V/RLAIF-V/) and [VisCPM](https://github.com/OpenBMB/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](https://github.com/OpenBMB/llama.cpp/blob/minicpmv-main/examples/llava/README-minicpmv2.6.md) and [ollama](https://github.com/OpenBMB/ollama/tree/minicpm-v2.6) support for efficient CPU inference on local devices, (2) [int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4) and [GGUF](https://huggingface.co/openbmb/MiniCPM-V-2_6-gguf) format quantized models in 16 sizes, (3) [vLLM](https://github.com/OpenBMB/MiniCPM-V/tree/main?tab=readme-ov-file#inference-with-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](https://github.com/OpenBMB/MiniCPM-V/tree/main?tab=readme-ov-file#chat-with-our-demo-on-gradio) and (6) online web [demo](http://120.92.209.146:8887).

### Evaluation  <!-- omit in toc -->
<div align="center">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/radar_final.png" width=66% />
</div>

Single image results on OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench:
<div align="center">

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/QVl0iPtT5aUhlvViyEpgs.png)

</div>

<sup>*</sup> We evaluate this benchmark using chain-of-thought prompting.

<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens.

Note: For proprietary models, we calculate token density based on the image encoding charging strategy defined in the official API documentation, which provides an upper-bound estimation.


<details>
<summary>Click to view multi-image results on Mantis Eval, BLINK Val, Mathverse mv, Sciverse mv, MIRB.</summary>
<div align="center">

![image/png](https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/o6FGHytRhzeatmhxq0Dbi.png)

</div>
<sup>*</sup> We evaluate the officially released checkpoint by ourselves.
</details>

<details>
<summary>Click to view video results on Video-MME and Video-ChatGPT.</summary>
<div align="center">

<!-- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/_T1mw5yhqNCqVdYRTQOGu.png) -->
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/jmrjoRr8SFLkrstjDmpaV.png)

</div>

</details>


<details>
<summary>Click to view few-shot results on TextVQA, VizWiz, VQAv2, OK-VQA.</summary>
<div align="center">


![image/png](https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/zXIuiCTTe-POqKGHszdn0.png)

</div>
* denotes zero image shot and two additional text shots following Flamingo.

<sup>+</sup> We evaluate the pretraining ckpt without SFT.
</details>

### Examples <!-- omit in toc -->

<div style="display: flex; flex-direction: column; align-items: center;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-bike.png" alt="Bike" style="margin-bottom: -20px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-menu.png" alt="Menu" style="margin-bottom: -20px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-code.png" alt="Code" style="margin-bottom: -20px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-Mem.png" alt="Mem" style="margin-bottom: -20px;">
  <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-medal.png" alt="medal" style="margin-bottom: 10px;">
</div>
<details>
  <summary>Click to view more cases.</summary>
  <div style="display: flex; flex-direction: column; align-items: center;">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-elec.png" alt="elec" style="margin-bottom: -20px;">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-olympic.png" alt="Menu" style="margin-bottom: 10px;">
  </div>
</details>

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

<div style="display: flex; justify-content: center;">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ai.gif" width="48%" style="margin: 0 10px;"/>
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/beer.gif" width="48%" style="margin: 0 10px;"/>
</div>
<div style="display: flex; justify-content: center; margin-top: 20px;">
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ticket.gif" width="48%" style="margin: 0 10px;"/>
    <img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/wfh.gif" width="48%" style="margin: 0 10px;"/>
</div>

<div style="text-align: center;">
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/mXAEFQFqNd4nnvPk7r5eX.mp4"></video>
<!-- <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/fEWzfHUdKnpkM7sdmnBQa.mp4"></video> -->

</div>



## Demo
Click here to try the Demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/).


## Usage
Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10：
```
Pillow==10.1.0
torch==2.1.2
torchvision==0.16.2
transformers==4.40.0
sentencepiece==0.1.99
decord
```

```python
# test.py
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

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('xx.jpg').convert('RGB')
question = 'What is in the image?'
msgs = [{'role': 'user', 'content': [image, question]}]

res = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
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=None,
    msgs=msgs,
    tokenizer=tokenizer,
    sampling=True,
    stream=True
)

generated_text = ""
for new_text in res:
    generated_text += new_text
    print(new_text, flush=True, end='')
```

### Chat with multiple images
<details>
<summary> Click to show Python code running MiniCPM-V 2.6 with multiple images input. </summary>
  
```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer

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)

image1 = Image.open('image1.jpg').convert('RGB')
image2 = Image.open('image2.jpg').convert('RGB')
question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.'

msgs = [{'role': 'user', 'content': [image1, image2, question]}]

answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)
```
</details>

### In-context few-shot learning
<details>
<summary> Click to view Python code running MiniCPM-V 2.6 with few-shot input. </summary>

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

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)

question = "production date" 
image1 = Image.open('example1.jpg').convert('RGB')
answer1 = "2023.08.04"
image2 = Image.open('example2.jpg').convert('RGB')
answer2 = "2007.04.24"
image_test = Image.open('test.jpg').convert('RGB')

msgs = [
    {'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]},
    {'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]},
    {'role': 'user', 'content': [image_test, question]}
]

answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer
)
print(answer)
```
</details>

### Chat with video
<details>
<summary> Click to view Python code running MiniCPM-V 2.6 with video input. </summary>

```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer
from decord import VideoReader, cpu    # pip install decord

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)

MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number

def encode_video(video_path):
    def uniform_sample(l, n):
        gap = len(l) / n
        idxs = [int(i * gap + gap / 2) for i in range(n)]
        return [l[i] for i in idxs]

    vr = VideoReader(video_path, ctx=cpu(0))
    sample_fps = round(vr.get_avg_fps() / 1)  # FPS
    frame_idx = [i for i in range(0, len(vr), sample_fps)]
    if len(frame_idx) > MAX_NUM_FRAMES:
        frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
    frames = vr.get_batch(frame_idx).asnumpy()
    frames = [Image.fromarray(v.astype('uint8')) for v in frames]
    print('num frames:', len(frames))
    return frames

video_path ="video_test.mp4"
frames = encode_video(video_path)
question = "Describe the video"
msgs = [
    {'role': 'user', 'content': frames + [question]}, 
]

# Set decode params for video
params={}
params["use_image_id"] = False
params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution >  448*448

answer = model.chat(
    image=None,
    msgs=msgs,
    tokenizer=tokenizer,
    **params
)
print(answer)
```
</details>


Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage.


## Inference with llama.cpp<a id="llamacpp"></a>
MiniCPM-V 2.6 can run with llama.cpp. See our fork of [llama.cpp](https://github.com/OpenBMB/llama.cpp/tree/minicpm-v2.5/examples/minicpmv) for more detail.


## Int4 quantized version
Download the int4 quantized version for lower GPU memory (7GB) usage:  [MiniCPM-V-2_6-int4](https://huggingface.co/openbmb/MiniCPM-V-2_6-int4).


## License
#### Model License
* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
* The usage of MiniCPM-V series model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-V 2.6 weights are also available for free commercial use.


#### Statement
* As an LMM, MiniCPM-V 2.6 generates contents by learning a large mount of multimodal corpora, but it cannot comprehend, express personal opinions or make value judgement. Anything generated by MiniCPM-V 2.6 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 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.

## Key Techniques and Other Multimodal Projects

👏 Welcome to explore key techniques of MiniCPM-V 2.6 and other multimodal projects of our team:

[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)

## Citation

If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！

```bib
@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}
}
```