Discover 4 Open Source Alternatives to GPT-4 Vision
Exploring Cost-Free Open Source Alternatives: A Guide to GPT-4 Vision Substitutes
GPT-4 Vision has undeniably emerged as a prominent player, showcasing remarkable capabilities in language understanding and visual processing. However, for those seeking cost-effective alternatives without compromising on performance, the realm of open-source solutions holds a treasure trove of possibilities.
In this introductory guide, we unveil four compelling alternatives to GPT-4 Vision that operate on open-source principles, ensuring accessibility and adaptability.
We will cover four open-source vision language models which are LLaVa (Large Language and Vision Assistant), CogAgent, Qwen Large Vision Language Model (Qwen-VL), and BakLLaVA, and explore their unique features and potential to redefine the landscape of language and vision processing.
Table of Contents:
LLaVa (Large Language and Vision Assistant)
CogAgent
Qwen Large Vision Language Model (Qwen-VL)
BakLLaVA
My E-book: Data Science Portfolio for Success Is Out!
I recently published my first e-book Data Science Portfolio for Success which is a practical guide on how to build your data science portfolio. The book covers the following topics: The Importance of Having a Portfolio as a Data Scientist How to Build a Data Science Portfolio That Will Land You a Job?
1. LLaVa (Large Language and Vision Assistant)
LLaVA represents a novel end-to-end trained large multimodal model that combines a vision encoder and Vicuna for general-purpose visual and language understanding, achieving impressive chat capabilities mimicking spirits of the multimodal GPT-4 and setting a new state-of-the-art accuracy on Science QA.
LLaVA is a research preview intended for non-commercial use only, subject to the model License of LLaMA, Terms of Use of the data generated by OpenAI, and Privacy Practices of ShareGPT. Users must agree to the following terms by using this service: The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Let's see some examples of visual instruction:
Visual Reasoning
Optical character recognition (OCR)
2. CogAgent
CogAgent is an open-source visual language model improved based on CogVLM. CogAgent-18B has 11 billion visual parameters and 7 billion language parameters
CogAgent-18B achieves state-of-the-art generalist performance on 9 classic cross-modal benchmarks, including VQAv2, OK-VQ, TextVQA, ST-VQA, ChartQA, infoVQA, DocVQA, MM-Vet, and POPE. It significantly surpasses existing models on GUI operation datasets such as AITW and Mind2Web.
In addition to all the features already present in CogVLM (visual multi-round dialogue, visual grounding), CogAgent:
Supports higher resolution visual input and dialogue question-answering. It supports ultra-high-resolution image inputs of 1120x1120.
Possesses the capabilities of a visual Agent, being able to return a plan, next action, and specific operations with coordinates for any given task on any GUI screenshot.
Enhanced GUI-related question-answering capabilities, allowing it to handle questions about any GUI screenshot, such as web pages, PC apps, mobile applications, etc.
Enhanced capabilities in OCR-related tasks through improved pre-training and fine-tuning.
GUI Agent Examples
3. Qwen Large Vision Language Model (Qwen-VL)
Qwen-VL (Qwen Large Vision Language Model) is the multimodal version of the large model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-VL accepts images, text, and bounding box as inputs, outputs text, and bounding box. The features of Qwen-VL include:
Strong performance: It significantly surpasses existing open-sourced Large Vision Language Models (LVLM) under a similar model scale on multiple English evaluation benchmarks (including Zero-shot Captioning, VQA, DocVQA, and Grounding).
Multi-lingual LVLM supporting text recognition: Qwen-VL naturally supports English, Chinese, and multi-lingual conversation, and it promotes end-to-end recognition of Chinese and English bi-lingual text in images.
Multi-image interleaved conversations: This feature allows for the input and comparison of multiple images, as well as the ability to specify questions related to the images and engage in multi-image storytelling.
First generalist model supporting grounding in Chinese: Detecting bounding boxes through open-domain language expression in both Chinese and English.
Fine-grained recognition and understanding: Compared to the 224*224 resolution currently used by other open-sourced LVLM, the 448*448 resolution promotes fine-grained text recognition, document QA, and bounding box annotation.
4. BakLLaVA
BakLLaVA 1 is a Mistral 7B base augmented with the LLaVA 1.5 architecture. In this first version, the authors showcase that a Mistral 7B base outperforms Llama 2 13B on several benchmarks. You can run BakLLaVA-1 on their repo. They are currently updating it to make it easier for you to finetune and inference. (https://github.com/SkunkworksAI/BakLLaVA).
BakLLaVA-1 is fully open-source but was trained on certain data that includes LLaVA’s corpus which is not commercially permissive. BakLLaVA 2 is cooking with a significantly larger (commercially viable) dataset and a novel architecture that expands beyond the current LLaVA method. BakLLaVA-2 will do away with the restrictions of BakLLaVA-1.
Are you looking to start a career in data science and AI and do not know how? I offer data science mentoring sessions and long-term career mentoring:
Mentoring sessions: https://lnkd.in/dXeg3KPW
Long-term mentoring: https://lnkd.in/dtdUYBrM
