LLavA AI technology page Top Builders

Explore the top contributors showcasing the highest number of LLavA AI technology page app submissions within our community.

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.

General
Relese dateNovember 20, 2023
Repositoryhttps://github.com/haotian-liu/LLaVA
TypeMultimodal Language and Vision Model

What is LLaVA?

Visual Instruction Tuning: LLaVA, short for Large Language-and-Vision Assistant, represents a significant leap in multimodal AI models.

With a focus on visual instruction tuning, LLaVA has been engineered to rival the capabilities of GPT-4V, demonstrating its exceptional prowess in understanding both language and vision. This state-of-the-art model excels in tasks ranging from impressive chatbot interactions to setting a new standard in science question-answering accuracy, achieving a remarkable 92.53%. With LLaVA's innovative approach to instruction-following data and the effective combination of vision and language models, it promises a versatile solution for diverse applications, marking a significant milestone in the field of multimodal AI.

LLaVA Tutorials


LLaVA Libraries

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LLavA AI technology page Hackathon projects

Discover innovative solutions crafted with LLavA AI technology page, developed by our community members during our engaging hackathons.

InsurCap

InsurCap

Insur.Cap revolutionizes risk management with algorithmically driven augmented underwriting, leveraging computer vision AI & LAM for image-caption fusion. The orchestration processes proactively predict risks and facilitate accessible comprehensive coverage, overcoming traditional insurance limitations. Insur.Cap optimizes “Assistant-LAM” communication via a chatbot-based UI conversation flow interface. Looking from the perspective of Knowledge augmentation, we have a “data point issue” while the PROBLEM is that the incumbent does not employ DATA {as a tool, knowledge…} driven decision making, (to help processes make better-agile decisions by bringing in {data} more {usable} information to the risk underwriting, as a new data set - data points.) -Traditional insurance is too complex. -Definitely there is still a gap between the needs (mainly on-demand or custom-target needs). -Last but not least, proactive prevention might play a crucial role - if we emphasize prevention as a service proposition. Multimodal orchestration is our magic weapon! We develop a seamless-simple customer UuserInterface that delivers more/new datasets and data points for augmenting underwriting. Through the {Large Action/Agentic Model} we empower algorithmically driven architecture and orchestrate the process flow decision tree. Let me show you how we do that! First, with a simple User Interface we ingest the image and from the AI receive the CAPTION - this means the context from the image. That is the first pillar of the AI_assitant Then the core pillar of AI_ “Agent - Action” capability is to compute the: proper insurance product line based on the item from the caption execute premium calculation logic offer personalized coverage and finally issue the insurance policy All of that is our AI assistant Chatbot-based user interface; a SaaS (IaaS) API-driven technology stack.

Data Tonic

Data Tonic

Enterprise Autonomation Agent Do not wait for accounting, legal or business intelligence reporting with uncertain quality and long review cycles. DataTonic accelerates the slowest part of analysis : data processing and project planning execution. Main Benefits DataTonic is unique for many reasons : local and secure application threads. compatible with microsoft enterprise environments. based on a rigorous and reproducible evaluation method. developper friendly : easily plug in new functionality and integrations. How we use it : Multi-Consult Technology You can use datatonic however you want, here's how we're using it : add case books to your folder for embedding : now DataTonic always presents its results in a case study! add medical textbooks to your folder for embedding : now DataTonic helps you through med-school ! add entire company business information : Data tonic is now your strategic advisor ! ask data tonic to create targetted sales strategies : now DataTonic is your sales assistant ! Data Tonic is the first multi-nested agent-builder-of-agents! Data Tonic uses a novel combination of three orchestration libraries. Each library creates it's own multi-agent environment. Each of these environments includes a code execution and code generation capability. Each of these stores data and embeddings on it's own datalake. Autogen is at the interface with the user and orchestrates the semantic kernel hub as well as using Taskweaver for data processing tasks. Semantic-kernel is a hub that includes internet browsing capabilities and is specifically designed to use taskweaver for data storage and retrieval and produce fixed intelligence assets also specifically designed for Autogen. Taskweaver is used as a plugin in semantic kernel for data storage and retrieval and also in autogen, but remains an autonomous task that can execute complex tasks in its multi-environment execution system.

SPROUT

SPROUT

In the realm of agriculture, timely and accurate information can be the difference between a bountiful harvest and a failed crop. SPROUT addresses this critical need by offering an innovative platform that combines the power of AI with cutting-edge technologies like NDVI image analysis, multimodal data synthesis, and retrieval-augmented generation (RAG) to deliver real-time insights into crop health and disease diagnostics. Our target audience includes farmers, agronomists, and agricultural enterprises seeking to leverage technology for enhanced decision-making. By utilizing tools such as Vertex AI for disease classification and vector search, LangChain with LlamaIndex for nuanced query responses, and Multimodal RAG for image analysis, SPROUT offers a comprehensive solution that goes beyond traditional farming applications. One of SPROUT's unique features is the incorporation of CLIP, Hugging face Embeddings and Fuyu-8b models, which empower the platform with exceptional understanding and analysis of both textual and visual data. Our evaluation with TrueLlama and TrueChain ensures that the responses and solutions provided are not only accurate but also constantly improving. In an industry where precision and efficiency are paramount, SPROUT stands out by offering a seamless and intuitive interface through Streamlit, ensuring that our sophisticated technology translates into tangible benefits for users across the globe. With SPROUT, farmers can optimize their practices, reduce environmental impact, and secure their crops' health and productivity, ushering in a new era of sustainable and informed agriculture.

Tru Era Applied

Tru Era Applied

Hackathon Submission: Enhanced Multimodal AI Performance Project Title: Optimizing Multimodal AI for Real-World Applications Overview: Our project focused on optimizing multimodal AI performance using the TruEra Machine Learning Ops platform. We evaluated 18 models across vision, audio, and text domains, employing innovative prompting strategies, performance metrics, and sequential configurations. Methodology: Prompting Strategies: Tailored prompts to maximize model response accuracy. Performance Metrics: Assessed models on accuracy, speed, and error rate. Sequential Configurations: Tested various model combinations for task-specific effectiveness. Key Models Evaluated: Vision: GPT4V, LLava-1.5, Qwen-VL, Clip (Google/Vertex), Fuyu-8B. Audio: Seamless 1.0 & 2.0, Qwen Audio, Whisper2 & Whisper3, Seamless on device, GoogleAUDIOMODEL. Text: StableMed, MistralMed, Qwen On Device, GPT, Mistral Endpoint, Intel Neural Chat, BERT (Google/Vertex). Results: Top Performers: Qwen-VL in vision, Seamless 2.0 in audio, and MistralMed in text. Insights: Balance between performance and cost is crucial. Some models like GPT and Intel Neural Chat underperformed or were cost-prohibitive. Future Directions: Focus on fine-tuning models like BERT using Vertex. Develop more connectors for TruLens for diverse endpoints. Submission Contents: GitHub Repository: [Link] Demo: [Link] Presentation: [Link] Our submission showcases the potential of multimodal AI evaluation using TruEra / TruLens in enhancing real-world application performance, marking a step forward in human-centered AI solutions.

Multi-Med

Multi-Med

Mission: To democratize access to reliable medical information and public health education through advanced, multilingual, and multimodal technology. Vision: To become a global leader in providing accessible, accurate, and immediate medical guidance and health education, bridging language and accessibility barriers. Overview: MultiMed is an innovative company operating at the intersection of health technology and educational technology. It specializes in developing advanced software solutions focused on medical Q&A, public health information, and sanitation education. The company's flagship product is the MultiMed app, a highly accessible and multilingual platform designed to provide accurate medical information and public health education to a diverse global audience. Target Audience: Individuals seeking welness in life. Non-native English speakers requiring welness information in their native language. People with disabilities who benefit from multimodal input and output options. Educational institutions and public health organizations looking for a tool to aid in health education. Healthcare professionals seeking a tool for patient education and engagement. Impact and Social Responsibility: MultiMed is committed to social responsibility, focusing on reaching underserved communities and contributing to global health education. The company collaborates with health organizations and NGOs to ensure that accurate and vital health information is accessible to all, regardless of their location, language, or socio-economic status. Future Developments: MultiMed plans to integrate more languages and dialects, expand its database to cover more specialized medical fields, and collaborate with global health experts to enhance the accuracy and relevance of its content. Additionally, the company is exploring the integration of augmented reality (AR) for more interactive health education.