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
AuthorTogether AI
TypeAI 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.

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.

Edulance-AI

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 โš™๏ธ 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. ๐Ÿ”ฉ 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.๐Ÿ”Œ Integrations Key integrations include Docker for deployment, LangChain libraries, TogetherAI's LLM models, Vectara for Chat. ๐Ÿงฉ Modularity ๐Ÿ“ฆ Dependencies Main dependencies include FastAPI, Docker, Python 3.10, requirements.txt, LangChain package, PikePDF, PyTesseract, and related tools.