MiniCPM-Llama3-V-2_5-gguf
Maintainer: openbmb
172
👁️
| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model overview
MiniCPM-Llama3-V-2_5-gguf is the latest model in the MiniCPM-V series developed by openbmb. It is built on the SigLip-400M and Llama3-8B-Instruct models, resulting in a total of 8B parameters. Compared to the previous MiniCPM-V 2.0 model, MiniCPM-Llama3-V-2_5-gguf has achieved significant performance improvements across a range of benchmarks, surpassing several widely used proprietary models.
The model exhibits strong capabilities in areas like OCR, language understanding, and trustworthy behavior. It also supports over 30 languages through minimal instruction-tuning, and has been optimized for efficient deployment on edge devices. This model builds upon the work of the VisCPM, RLHF-V, LLaVA-UHD, and RLAIF-V projects from the openbmb team.
Model inputs and outputs
Inputs
- Images:
MiniCPM-Llama3-V-2_5-ggufcan process images with any aspect ratio up to 1.8 million pixels. - Text: The model can engage in interactive conversations, processing user messages as input.
Outputs
- Text: The model generates relevant and coherent text responses to user inputs.
- Multimodal understanding: The model can combine its understanding of the input image and text to provide comprehensive, multimodal outputs.
Capabilities
MiniCPM-Llama3-V-2_5-gguf has demonstrated leading performance on a range of benchmarks, including TextVQA, DocVQA, OCRBench, OpenCompass, MME, MMBench, MMMU, MathVista, LLaVA Bench, RealWorld QA, and Object HalBench. It surpasses widely used proprietary models like GPT-4V-1106, Gemini Pro, Qwen-VL-Max, and Claude 3 with 8B parameters.
The model has also shown strong OCR capabilities, achieving a score of over 700 on OCRBench, outperforming proprietary models such as GPT-4o, GPT-4V-0409, Qwen-VL-Max, and Gemini Pro. Additionally, MiniCPM-Llama3-V-2_5-gguf exhibits trustworthy behavior, with a hallucination rate of 10.3% on Object HalBench, lower than GPT-4V-1106 (13.6%).
What can I use it for?
MiniCPM-Llama3-V-2_5-gguf can be used for a variety of multimodal tasks, such as visual question answering, document understanding, and interactive language-image applications. Its strong OCR capabilities make it well-suited for tasks like text extraction from images, document processing, and table-to-markdown conversion.
The model's multilingual support and efficient deployment on edge devices also open up opportunities for developing language-agnostic applications and integrating the model into mobile and IoT solutions.
Things to try
One exciting aspect of MiniCPM-Llama3-V-2_5-gguf is its ability to engage in interactive, multimodal conversations. You can try providing the model with a series of messages and images, and observe how it leverages its understanding of both modalities to generate coherent and informative responses.
Additionally, the model's versatile OCR capabilities allow you to experiment with tasks like extracting text from images of varying complexity, such as documents, receipts, or handwritten notes. You can also explore its ability to understand and reason about the contents of these images in a multimodal context.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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