MiniCPM Llama3 V 2 5
MiniCPM-Llama3-V 2.5 is a multimodal large language model that achieves GPT-4V level performance. With 8B parameters, it outperforms proprietary models like GPT-4V-1106, Gemini Pro, and Claude 3. It has strong OCR capabilities, processing images with up to 1.8 million pixels, and supports over 30 languages. The model is efficient, achieving a 150-fold acceleration in multimodal large model end-side image encoding and a 3-fold increase in language decoding speed. It's also easy to use, with support for various interfaces like llama.cpp, ollama, and GGUF format quantized models. What can you do with MiniCPM-Llama3-V 2.5? You can use it for text generation, image understanding, and even real-time video understanding on iPad. How does it compare to other models? It has a lower hallucination rate than GPT-4V-1106 and outperforms other Llama 3-based MLLMs. What makes it unique? Its ability to process multiple languages and its efficient deployment on edge devices make it a powerful tool for various applications.
Table of Contents
Model Overview
The MiniCPM-Llama3-V 2.5 model is a powerful multimodal large language model (LLM) that can process both images and text. It’s designed to be efficient, trustworthy, and easy to use.
Key Features
- Leading Performance: Achieved an average score of
65.1on OpenCompass, outperforming widely used proprietary models like ==GPT-4V-1106==, ==Gemini Pro==, ==Claude 3==, and ==Qwen-VL-Max==. - Strong OCR Capabilities: Can process images with any aspect ratio and up to
1.8 million pixels, achieving a700+score on OCRBench, surpassing proprietary models like ==GPT-4o==, ==GPT-4V-0409==, ==Qwen-VL-Max==, and ==Gemini Pro==. - Trustworthy Behavior: Exhibits more trustworthy behavior using the latest RLAIF-V method, achieving a
10.3%hallucination rate on Object HalBench, lower than ==GPT-4V-1106== (13.6%). - Multilingual Support: Extends bilingual (Chinese-English) multimodal capabilities to over
30 languages, including German, French, Spanish, Italian, Korean, Japanese, and more. - Efficient Deployment: Employs model quantization, CPU optimizations, NPU optimizations, and compilation optimizations for high-efficiency deployment on edge devices.
Capabilities
Leading Performance
- Achieved an average score of
65.1on OpenCompass, a comprehensive evaluation over 11 popular benchmarks. - Surpassed widely used proprietary models like ==GPT-4V-1106==, ==Gemini Pro==, ==Claude 3==, and ==Qwen-VL-Max==.
Strong OCR Capabilities
- Can process images with any aspect ratio and up to
1.8 million pixels(e.g.,1344x1344). - Achieved a
700+score on OCRBench, surpassing proprietary models like ==GPT-4o==, ==GPT-4V-0409==, ==Qwen-VL-Max==, and ==Gemini Pro==.
Trustworthy Behavior
- Exhibits more trustworthy behavior using the latest RLAIF-V method.
- Achieved a
10.3%hallucination rate on Object HalBench, lower than ==GPT-4V-1106== (13.6%).
Multilingual Support
- Extends bilingual (Chinese-English) multimodal capabilities to over
30 languages, including German, French, Spanish, Italian, Korean, Japanese, and more.
Efficient Deployment
- Employs model quantization, CPU optimizations, NPU optimizations, and compilation optimizations for high-efficiency deployment on edge devices.
- Achieved a
150-fold accelerationin multimodal large model end-side image encoding and a3-fold increasein language decoding speed.
What Can You Do with MiniCPM-Llama3-V 2.5?
- Run it on multiple low VRAM GPUs (12 GB or 16 GB) by distributing the model’s layers across multiple GPUs.
- Use it for streaming outputs and customized system prompts.
- Deploy it on end devices, such as mobile phones, with efficient CPU inference.
- Fine-tune it with only 2 V100 GPUs using LoRA fine-tuning.
Getting Started
- Check out the demo video to see MiniCPM-Llama3-V 2.5 in action.
- Try out the demo on HuggingFace Spaces.
- Explore the GitHub repository for more information on usage and deployment.
Limitations
While MiniCPM-Llama3-V 2.5 has achieved impressive results in various tasks, it’s essential to acknowledge its limitations.
1. Hallucinations
- Still has a hallucination rate of
10.3%on Object HalBench, which is lower than some proprietary models like ==GPT-4V-1106==, but not perfect.
2. Multimodal Understanding
- May struggle to understand the nuances of human language and visual cues, leading to errors in tasks like image captioning or visual question answering.
3. Language Limitations
- Supports over
30 languages, but its performance may vary across languages. - May not be as proficient in languages with limited training data or complex grammar rules.
4. Deployment Challenges
- Requires significant computational resources.
- May pose challenges for deployment on devices with limited resources, such as low-end smartphones or embedded systems.
5. Bias and Fairness
- May inherit biases and stereotypes present in the training data.
- Can result in unfair or discriminatory outputs, particularly in tasks that involve sensitive information like personal characteristics or demographics.
6. Lack of Common Sense
- Doesn’t possess common sense or real-world experience.
- May generate outputs that are technically correct but lack practicality or real-world applicability.
7. Overfitting
- May overfit to the training data, which can result in poor performance on unseen data or tasks.
By acknowledging these limitations, we can better understand the capabilities and constraints of MiniCPM-Llama3-V 2.5 and work towards improving its performance and applicability in various tasks.
