Together AI AI technology page Top Builders

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

Together AI: Powering AI Innovation

Together AI is AI cloud platform which drives AI innovation. It contributes to open-source research, empowering developers to deploy AI models.

General
Author	Together AI
Type	AI platform

Key Features

Unmatched performance: Its research and developments introduce advanced efficiencies in training and inference processes which grows along with user's requirements. The Together Inference Engine has the swiftest inference stack currently available.
High scalability: It is a horizontally scalable platform which is delivering peak performance based on user's traffic demands.
Rapid integration: Integrates into existing applications with minimal setup with its easy-to-use API.
Topnotch support: Their expert team is there to assist users in the preparation and optimization of datasets to ensure accuracy and providing support in training personalized AI models.

Start building with Together AI's products

Dive into the possible solutions from Together AI to ensure flawless building of your app. Explore the apps created with Together AI technology showcased during hackathons and innovation challenges!

List of Together AI's products

Together Inference

Together Inference is the fastest inference stack available, delivering speeds up to 3 times faster than competitors like TGI, vLLM, or other inference APIs such as Perplexity, Anyscale, or Mosaic ML. Run leading open-source models like Llama-2 with lightning-fast performance, all at a cost 6 times lower than GPT 3.5 Turbo when using Llama2-13B.

Together Custom Models

Together Custom Models is designed to assist you in training your own advanced AI model. You can use state-of-the-art optimizations for the better performance in the Together Training stack, such as FlashAttention-2. Once completed, the model belongs to you. Also, you will be able to maintain the whole ownership of the model and deploy it wherever you want to.

Together GPU Clusters

Together AI provides top-performing computing clusters designed for training and refining purposes. Their clusters come equipped with the lightning-fast Together Training stack, ensuring seamless operation. Additionally, their AI experts team of is readily available to offer any kind of assistance. With a renewal rate higher than 95%, Together GPU Clusters ensure reliability and performance.

System Requirements

Together AI is compatible with major operating systems, including Windows, macOS, and Linux. A minimum of 4 GB of RAM is recommended for optimal performance. Complimentary is having access to a GPU can which significantly enhances performance of model training.

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Together AI AI technology page Hackathon projects

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

Alice Project

Alice Project revolutionizes small business management by providing a platform that seamlessly integrates multiple productivity tools. Through Synapse Copilot, Alice achieves seamless integration of external apps with its intuitive interface, enabling users to automate task management and optimize workflows with a fluid user experience. By harnessing the power of advanced AI, Alice offers real-time insights and automated solutions to common challenges faced by small businesses, such as missed deadlines and disorganized workflows. By integrating multiple APIs, Alice offers seamless management: Calendar: Schedule events and milestones. Trello: Manage tasks and workflows. Sheets: Analyze data effortlessly. Search: Instantly access vital information. QuickChart: Visualize your data with charts.

Spirit AGI

A cross-platform application for creating AI agents and AGI, by coding using node editor, and can support more than Python language by using bindings for C/C++ (Cython), Java (jnius), C# (DLLs), Golang bindings, and whatever bindings is available. For this event, I integrated Stability's SDXL, Cohere's Aya model, TogetherAI for the LLM, but can also be configured for other models and providers. To create a video subtitle generator using faster-whisper, translated by the Cohere Aya model. Hopefully, the node editor will be included to run code asynchronously like in Unreal Engine's node editor and ComfyUI.

Code Interpreter Powered RAG

Tentative Submission: Please go here: https://github.com/sprites20/Sprites-RAG-Project/tree/main The program integrates with the TogetherAI API for the Large Language Model (LLM) and utilizes LlamaIndex Cohere embeddings and Cohere rerank to find and process documents. It is capable of retrieving relevant documentation locally and generating a program sequence for execution. This sequence, when run, generates output text that the system uses for its responses. The key enhancement this program provides to RAG's capabilities is the addition of a full code interpreter. This interpreter significantly improves RAG's ability to process information and generate accurate results, particularly in the realm of numeric computations. It also allows RAG to execute functions as requested by the user, expanding its utility and versatility. By leveraging the TogetherAI API and Cohere technology, this program enhances RAG's functionality across a wide range of tasks. It enables RAG to provide more detailed and accurate information, as well as perform calculations and execute functions, making it a more powerful tool for users seeking information and computational assistance.

SYSTEMiX ChatBot

SUMMARY :: Imagine having a friendly and knowledgeable partner who can answer your questions in a conversational way, tailoring responses based on your specific interests and needs only - SYSTEMiX is your partner - a personal assistant for all things about systems innovation. Engage with complex topics through a user-friendly chatbot interface, drawing from the vast knowledge of the Vectara AI database. This responsive chatbot sparks short conversations on Systems Thinking, Systems Theory, Systemic Design, and Systems Innovation based on your specific data. Utilizing AI Retrieval Augmented Generation (RAG) technology, this app indexes and queries information from an uploaded corpus in the Vectara database.

Snowflake

Introducing a cutting-edge personal assistant designed to revolutionize the support and care for individuals living with diabetes. Leveraging state-of-the-art research and advanced technologies, this personal assistant is poised to enhance the quality of life for diabetic patients by providing accurate, relevant, and personalized information while minimizing the risks of misinformation. A personal assistant updated with state-of-the-art research surrounding Diabetes, to enhance Diabetic patients' quality of life by summarizing important information while minimizing misinformation. RAG chatbot combining semantic search with vector database, keyword databases, and summary index.

Edulance-AI

Edulance is an open-source project that utilizes advanced technologies such as Unstructured, machine learning models, and APIs to transform text documents and PDFs into interactive educational resources. The software accepts user-uploaded files, applies optical character recognition (OCR) for text documents, or extracts valuable content from PDFs. It then generates lessons, quizzes, and lesson plans based on the content using its Lesson Graph model and agents like LessonGenerator, LessonPlanner, OCRAgent, PdfAgent, QuizAgent, and TogetherLLM. Edulance provides an immersive learning experience, enabling effective teaching and interactive knowledge acquisition. Overall this project incorporates the following: TogetherAI's LLM Models Unstructured Partition pdf for making PDFs LLM Ready Agentic AI with state management. Features Feature Description ⚙️ Architecture Edulance is a Python-based project using FastAPI as the web framework and Uvicorn for runtime serving. The application leverages containers with Docker for deployment, installing required dependencies from requirements.txt. It utilizes libraries like LangChain, PikePDF, PyTesseract for OCR, and TogetherAI's LLM models. 🔩 Code Quality The codebase follows a modular structure with clearly defined agents and graph files, ensuring high cohesion and low coupling. Python style guides are followed consistently, including PEP8 and PEP534. There is adequate usage of comments throughout the codebase.🔌 Integrations Key integrations include Docker for deployment, LangChain libraries, TogetherAI's LLM models, Vectara for Chat. 🧩 Modularity 📦 Dependencies Main dependencies include FastAPI, Docker, Python 3.10, requirements.txt, LangChain package, PikePDF, PyTesseract, and related tools.