In the rapidly evolving landscape of artificial intelligence, local multimodal language models (LLMs) have emerged as game-changers. These advanced models possess the remarkable ability to comprehend and generate text while incorporating visual information, paving the way for exciting applications across various domains.
In this article, we will explore the four best local multimodal LLMs that are revolutionizing the field. From their unique features to their intended uses, we will delve into the world of these cutting-edge models and uncover their potential to transform research, innovation, and user experiences. Whether you are a researcher, hobbyist, or enthusiast in the realms of computer vision, natural language processing, or machine learning, join us on this journey to unlock the power of these four exceptional local multimodal LLMs.
Best Local Multimodal LLM
IDEFICS 8 to 80b
IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS) is an innovative open-access visual language model that brings the power of multimodal understanding to the realm of language generation. This model is a derivation of Flamingo, an advanced visual language model developed by DeepMind that had previously been withheld from public release. Similar in design to GPT-4, IDEFICS is designed to seamlessly process combinations of image and text inputs, culminating in the production of coherent textual outputs.
What sets IDEFICS apart is its exclusive reliance on publicly accessible data and models, particularly LLaMA v1 and OpenCLIP. This approach ensures its accessibility and adaptability, further augmented by its two distinct versions: the base variant and the instructed variant. These variants come in sizes of either 9 billion or 80 billion parameters, respectively, offering varying degrees of complexity and accuracy.
The versatility of IDEFICS is worth emphasizing. It can excel in multiple domains, ranging from answering questions related to images and describing visual content, to fabricating narratives that are deeply rooted in multiple images. It even possesses the capacity to function as a pure language model without the inclusion of visual data.
Crucially, IDEFICS exhibits performance at par with its original closed-source predecessor on a range of image-text benchmarks. This includes tasks such as visual question answering, both open-ended and multiple choice, crafting descriptive captions for images, and even classifying images within the context of few-shot learning scenarios. With its two available parameter sizes, 9 billion and 80 billion, IDEFICS caters to diverse needs while maintaining its proficiency in bridging the gap between language and imagery.
Lynx
Lynx is a cutting-edge local multimodal language model designed for sophisticated comprehension and generation tasks. It introduces a diverse array of over 20 meticulously crafted variants, each exploring distinct aspects of multimodal capabilities.
In-depth Exploration: Lynx’s development focuses on network structures, model designs, training data impact, and prompt diversity. These controlled settings facilitate refined understanding and effective instruction-following.
Comprehensive Evaluation: Lynx’s innovation lies in its creation of an expansive evaluation set, covering image and video tasks via crowd-sourced input. This set benchmarks the model’s performance comprehensively.
Performance Excellence: Lynx emerges as a pinnacle of accuracy in understanding and generation, surpassing GPT-4 style models. Its prowess in processing vision and language concurrently sets new standards.
Vision-Language Synergy: Lynx harmonizes vision and instruction tokens through a dedicated vision encoder, followed by concatenated processing for task execution.
Elegant Architecture: Lynx’s hallmark is the “prefix-finetuning” (PT) structure, simplifying text generation aligned with input instructions through decoder-only LLMs.
Cheetor
Cheetor, an advanced local multimodal language model, showcases remarkable capabilities in processing intricate vision-language instructions. It excels in reasoning across complex scenarios where images and text intertwine seamlessly. In scenario (a), Cheetor’s adeptness shines as it discerns intricate connections within images, unveiling the underlying causes of unusual occurrences. Similarly, in (b) and (c), Cheetor displays its capacity to deduce relationships among images, adeptly grasping the metaphorical nuances they convey. Impressively, in (e) and (f), Cheetor engages in multi-modal conversations with humans, displaying an unparalleled aptitude for comprehending even the most unconventional visual elements.
Cheetor’s prowess can be attributed to three key attributes:
- Interleaved Vision-Language Context: Cheetor seamlessly integrates both images and text within its instructions. Whether it’s unraveling storyboards accompanied by scripts or deciphering diagrams in textbooks, Cheetor navigates diverse visual-textual sequences with ease.
- Diverse Complex Instruction Formats: The model’s versatility shines through a wide spectrum of instructions. From predicting dialogue for comics to detecting disparities in surveillance images and tackling conversational embodied tasks, Cheetor effortlessly handles varied and intricate tasks.
- Wide Array of Instruction-Following Scenarios: Cheetor’s capabilities span across numerous real-world applications. Its expertise encompasses domains like cartoons, industrial images, and driving recordings, making it adaptable to an extensive range of scenarios.
LLaVA 13B & 7B
LLaVA 13B, 7B is an open-source chatbot and multimodal language model. Trained on GPT-generated multimodal data, it excels in simple visual reasoning tasks. While its performance may not be outstanding in complex visual reasoning, LLaVA 13B and 7B still surprises users in various ways. It is primarily designed for research purposes, catering to computer vision, natural language processing, and AI enthusiasts. For more information, visit https://llava-vl.github.io/. Questions and comments can be directed to the GitHub repository at https://github.com/haotian-liu/LLaVA/issues.
While LLaVA 7B may not excel in handling complex visual reasoning tasks, it still manages to surprise users with its ability to tackle certain challenging scenarios. Its performance in these complex tasks might exceed expectations, making it an intriguing choice for those looking to explore the boundaries of multimodal models.
MiniGPT-4 7B & 13B
MiniGPT-4 7B and 13B are two powerful multimodal language models designed to understand both text and visual information. With 7 billion and 13 billion parameters respectively, these models leverage the capabilities of MiniGPT-4, which combines a frozen visual encoder with a frozen LLM called Vicuna, using a single projection layer.
These models exhibit numerous capabilities similar to those of GPT-4, such as generating detailed descriptions for images and creating websites based on hand-written drafts. In addition to these features, MiniGPT-4 demonstrates emerging capabilities like crafting stories and poems inspired by given images, providing solutions to visual problems, and even offering cooking instructions based on food photos.
MiniGPT-4, when aligned with Vicuna-7B, offers a pretrained version that requires as low as 12GB of GPU memory for demonstration. While excelling in simple visual reasoning tasks, it’s important to note that these models may not perform as well on complex visual tasks. Nevertheless, they can still surprise users with their ability to tackle certain complex challenges.
OpenFlamingo v2
OpenFlamingo v2 is an advanced Local Multimodal (LLM) model that excels in processing combined sequences of images and text to generate meaningful textual output. By effectively handling interleaved examples, this model demonstrates its proficiency in various tasks such as captioning, visual question answering, and image classification.
Building upon the Flamingo modeling paradigm, OpenFlamingo v2 enhances the layers of a pre-trained language model, enabling them to leverage visual features during the decoding process. Following this approach, the vision encoder and language model are frozen, while the connecting modules are trained using image-text sequences obtained from web scraping. A combination of LAION-2B and Multimodal C4 serves as the basis for training.
OpenFlamingo v2 is a versatile LLM model capable of performing an array of functions. It can accurately count objects within an image, read and comprehend text, provide comprehensive explanations for images, and much more. With its multimodal capabilities, this model goes beyond traditional text-focused models, enabling it to understand and process visual information effectively.
Otter-9B
Otter-9B is an impressive Local Multimodal (LLM) model that takes the concept of understanding beyond text to a whole new level. With Otter v0.1, it introduces the groundbreaking support for multiple image inputs as in-context examples, making it the first multimodal instruction tuned model to organize inputs in this manner.
Building upon this achievement, Otter v0.2 expands its capabilities further by supporting video inputs, where frames are arranged similarly to Flamingo’s original implementation. Moreover, it continues to embrace multiple image inputs, leveraging them as in-context examples for each other. This enhanced flexibility and integration of visual elements empower Otter-9B to understand daily scenes, reason in context, spot differences in observations, and act as an egocentric assistant.
One of the standout features of Otter-9B is its multilingual support. In addition to English, it also caters to a global audience by offering compatibility with Chinese, Korean, Japanese, German, French, Spanish, and Arabic languages. This inclusive approach enables a larger user base to benefit from the convenience brought about by advancements in artificial intelligence, fostering engagement and accessibility across diverse cultures.
