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--- |
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datasets: |
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- openbmb/RLAIF-V-Dataset |
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language: |
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- multilingual |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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tags: |
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- minicpm-v |
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- vision |
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- ocr |
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- multi-image |
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- video |
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- custom_code |
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--- |
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# MiniCPM-V-2_6-RK3588-1.1.2 |
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This version of MiniCPM-V-2_6 has been converted to run on the RK3588 NPU using ['w8a8'] quantization. |
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This model has been optimized with the following LoRA: |
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Compatible with RKLLM version: 1.1.2 |
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## Useful links: |
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[Official RKLLM GitHub](https://github.com/airockchip/rknn-llm) |
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[RockhipNPU Reddit](https://reddit.com/r/RockchipNPU) |
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[EZRKNN-LLM](https://github.com/Pelochus/ezrknn-llm/) |
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Pretty much anything by these folks: [marty1885](https://github.com/marty1885) and [happyme531](https://huggingface.co/happyme531) |
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Converted using https://github.com/c0zaut/ez-er-rkllm-toolkit |
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# Original Model Card for base model, MiniCPM-V-2_6, below: |
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<h1>A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone</h1> |
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[GitHub](https://github.com/OpenBMB/MiniCPM-V) | [Demo](http://120.92.209.146:8887/)</a> |
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## MiniCPM-V 2.6 |
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**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: |
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- 🔥 **Leading Performance.** |
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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. |
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- 🖼️ **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. |
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- 🎬 **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. |
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- 💪 **Strong OCR Capability and Others.** |
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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**. |
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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. |
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- 🚀 **Superior Efficiency.** |
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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. |
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- 💫 **Easy Usage.** |
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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). |
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### Evaluation <!-- omit in toc --> |
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<div align="center"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/radar_final.png" width=66% /> |
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</div> |
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#### Single image results on OpenCompass, MME, MMVet, OCRBench, MMMU, MathVista, MMB, AI2D, TextVQA, DocVQA, HallusionBench, Object HalBench: |
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<div align="center"> |
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</div> |
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<sup>*</sup> We evaluate this benchmark using chain-of-thought prompting. |
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<sup>+</sup> Token Density: number of pixels encoded into each visual token at maximum resolution, i.e., # pixels at maximum resolution / # visual tokens. |
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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. |
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#### Multi-image results on Mantis Eval, BLINK Val, Mathverse mv, Sciverse mv, MIRB: |
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<div align="center"> |
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</div> |
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<sup>*</sup> We evaluate the officially released checkpoint by ourselves. |
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#### Video results on Video-MME and Video-ChatGPT: |
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<div align="center"> |
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<!--  --> |
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</div> |
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<details> |
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<summary>Click to view few-shot results on TextVQA, VizWiz, VQAv2, OK-VQA.</summary> |
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<div align="center"> |
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</div> |
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* denotes zero image shot and two additional text shots following Flamingo. |
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<sup>+</sup> We evaluate the pretraining ckpt without SFT. |
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</details> |
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### Examples <!-- omit in toc --> |
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<div style="display: flex; flex-direction: column; align-items: center;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-bike.png" alt="Bike" style="margin-bottom: -20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-menu.png" alt="Menu" style="margin-bottom: -20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multi_img-code.png" alt="Code" style="margin-bottom: -20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-Mem.png" alt="Mem" style="margin-bottom: -20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-medal.png" alt="medal" style="margin-bottom: 10px;"> |
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</div> |
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<details> |
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<summary>Click to view more cases.</summary> |
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<div style="display: flex; flex-direction: column; align-items: center;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/ICL-elec.png" alt="elec" style="margin-bottom: -20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/minicpmv2_6/multiling-olympic.png" alt="Menu" style="margin-bottom: 10px;"> |
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</div> |
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</details> |
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We deploy MiniCPM-V 2.6 on end devices. The demo video is the raw screen recording on a iPad Pro without edition. |
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<div style="display: flex; justify-content: center;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ai.gif" width="48%" style="margin: 0 10px;"/> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/beer.gif" width="48%" style="margin: 0 10px;"/> |
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</div> |
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<div style="display: flex; justify-content: center; margin-top: 20px;"> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/ticket.gif" width="48%" style="margin: 0 10px;"/> |
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<img src="https://github.com/OpenBMB/MiniCPM-V/raw/main/assets/gif_cases/wfh.gif" width="48%" style="margin: 0 10px;"/> |
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</div> |
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<div style="text-align: center;"> |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/mXAEFQFqNd4nnvPk7r5eX.mp4"></video> |
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<!-- <video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/64abc4aa6cadc7aca585dddf/fEWzfHUdKnpkM7sdmnBQa.mp4"></video> --> |
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</div> |
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## Demo |
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Click here to try the Demo of [MiniCPM-V 2.6](http://120.92.209.146:8887/). |
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## Usage |
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Inference using Huggingface transformers on NVIDIA GPUs. Requirements tested on python 3.10： |
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``` |
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Pillow==10.1.0 |
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torch==2.1.2 |
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torchvision==0.16.2 |
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transformers==4.40.0 |
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sentencepiece==0.1.99 |
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decord |
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``` |
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```python |
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# test.py |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, |
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attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager |
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model = model.eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) |
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image = Image.open('xx.jpg').convert('RGB') |
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question = 'What is in the image?' |
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msgs = [{'role': 'user', 'content': [image, question]}] |
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res = model.chat( |
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image=None, |
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msgs=msgs, |
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tokenizer=tokenizer |
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) |
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print(res) |
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## if you want to use streaming, please make sure sampling=True and stream=True |
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## the model.chat will return a generator |
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res = model.chat( |
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image=None, |
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msgs=msgs, |
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tokenizer=tokenizer, |
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sampling=True, |
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stream=True |
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) |
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generated_text = "" |
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for new_text in res: |
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generated_text += new_text |
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print(new_text, flush=True, end='') |
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``` |
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### Chat with multiple images |
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<details> |
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<summary> Click to show Python code running MiniCPM-V 2.6 with multiple images input. </summary> |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, |
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attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager |
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model = model.eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) |
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image1 = Image.open('image1.jpg').convert('RGB') |
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image2 = Image.open('image2.jpg').convert('RGB') |
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question = 'Compare image 1 and image 2, tell me about the differences between image 1 and image 2.' |
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msgs = [{'role': 'user', 'content': [image1, image2, question]}] |
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answer = model.chat( |
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image=None, |
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msgs=msgs, |
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tokenizer=tokenizer |
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) |
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print(answer) |
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``` |
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</details> |
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### In-context few-shot learning |
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<details> |
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<summary> Click to view Python code running MiniCPM-V 2.6 with few-shot input. </summary> |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, AutoTokenizer |
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model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, |
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attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager |
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model = model.eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) |
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question = "production date" |
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image1 = Image.open('example1.jpg').convert('RGB') |
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answer1 = "2023.08.04" |
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image2 = Image.open('example2.jpg').convert('RGB') |
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answer2 = "2007.04.24" |
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image_test = Image.open('test.jpg').convert('RGB') |
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msgs = [ |
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{'role': 'user', 'content': [image1, question]}, {'role': 'assistant', 'content': [answer1]}, |
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{'role': 'user', 'content': [image2, question]}, {'role': 'assistant', 'content': [answer2]}, |
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{'role': 'user', 'content': [image_test, question]} |
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] |
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answer = model.chat( |
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image=None, |
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msgs=msgs, |
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tokenizer=tokenizer |
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) |
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print(answer) |
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``` |
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</details> |
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### Chat with video |
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<details> |
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<summary> Click to view Python code running MiniCPM-V 2.6 with video input. </summary> |
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```python |
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import torch |
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from PIL import Image |
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from transformers import AutoModel, AutoTokenizer |
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from decord import VideoReader, cpu # pip install decord |
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model = AutoModel.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True, |
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attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eager |
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model = model.eval().cuda() |
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tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-V-2_6', trust_remote_code=True) |
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MAX_NUM_FRAMES=64 # if cuda OOM set a smaller number |
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def encode_video(video_path): |
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def uniform_sample(l, n): |
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gap = len(l) / n |
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idxs = [int(i * gap + gap / 2) for i in range(n)] |
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return [l[i] for i in idxs] |
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vr = VideoReader(video_path, ctx=cpu(0)) |
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sample_fps = round(vr.get_avg_fps() / 1) # FPS |
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frame_idx = [i for i in range(0, len(vr), sample_fps)] |
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if len(frame_idx) > MAX_NUM_FRAMES: |
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frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES) |
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frames = vr.get_batch(frame_idx).asnumpy() |
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frames = [Image.fromarray(v.astype('uint8')) for v in frames] |
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print('num frames:', len(frames)) |
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return frames |
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video_path ="video_test.mp4" |
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frames = encode_video(video_path) |
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question = "Describe the video" |
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msgs = [ |
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{'role': 'user', 'content': frames + [question]}, |
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] |
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# Set decode params for video |
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params={} |
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params["use_image_id"] = False |
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params["max_slice_nums"] = 2 # use 1 if cuda OOM and video resolution > 448*448 |
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answer = model.chat( |
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image=None, |
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msgs=msgs, |
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tokenizer=tokenizer, |
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**params |
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) |
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print(answer) |
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``` |
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</details> |
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Please look at [GitHub](https://github.com/OpenBMB/MiniCPM-V) for more detail about usage. |
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## Inference with llama.cpp<a id="llamacpp"></a> |
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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. |
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## Int4 quantized version |
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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). |
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## License |
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#### Model License |
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* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. |
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* 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). |
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* 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. |
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#### Statement |
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* 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 |
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* 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. |
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## Key Techniques and Other Multimodal Projects |
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👏 Welcome to explore key techniques of MiniCPM-V 2.6 and other multimodal projects of our team: |
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[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) |
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## Citation |
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If you find our work helpful, please consider citing our papers 📝 and liking this project ❤️！ |
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```bib |
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@article{yao2024minicpm, |
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title={MiniCPM-V: A GPT-4V Level MLLM on Your Phone}, |
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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}, |
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journal={arXiv preprint arXiv:2408.01800}, |
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year={2024} |
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} |
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``` |
