Proven open source AI technology and tools in 2023

Most companies aren’t fighting for billions of dollars in GPUs or trying to build the next ChatGPT, they’re working to optimize AI for their business. That means using lots of the same tools and approaches for systems integration and making big technology choices between open source AI tools and closed source options.

With the right ecosystem of open source technologies, you can build AI capabilities that cost less to run, are highly customizable, and integrate with your existing open source technology stacks.

High-level benefits of open source AI tools

• Similar features as closed source AI models (GPT-4)  without extreme costs for training or compute resources.
• Integration with your existing open source technology – e.g. Kubernetes and Docker.
• Customization for AI enterprise systems integration that’s not available out of the box.

We’ve been busy building AI applications for our partners in 2023. Here are some of the best open source AI tools and tech you can use to deliver your own enterprise AI capabilities.

Open source AI Large Language Models (LLMs)

LLMs are the driving force in consumer-facing AI technology. There are open source options from companies like Meta and Google that you can use to build your own enterprise chatbots and secure productivity tools.

• Llama 2 – Llama 2 is an open-source LLM developed by Meta. It’s available for free for research and commercial use. Llama 2 comes in various sizes: 7B, 13B, and 70B parameters. Llama 2 has a context length of 4096 tokens and is available through Azure, AWS, Hugging Face, and other platforms.
  
  
• Falcon 180B – Falcon is an LLM developed by the Technology Innovation Institute (TII) and hosted on the Hugging Face hub. It comes in two models, Falcon-40B and Falcon-7B. The flagship model, Falcon 180B, is a 180-billion-parameter LLM trained on 3.5 trillion tokens. 
  
  It has a context window size of 2048 tokens, and there are plans to extend it to 10k. Falcon is available on Azure and ranks as the highest-performing pre-trained LLM on the Hugging Face Open LLM Leaderboard.
  
  
• PaLM 2 – PaLM 2 is a transformer-based model released by Google with multilingual, reasoning, and coding capabilities. Google will make PaLM 2 available in four sizes from smallest to largest: Gecko, Otter, Bison, and Unicorn2. It has a context size of 32,000 tokens and is available through Google’s PaLM API. 
  
  
• Yi-34B – A new multilingual model trained by 01.AI and backed by Hai-Fu Lee (Chinese computer scientist and AI investor). Yi-34B is significantly smaller than other open source models in terms of parameter size, but currently outperforms Llama 2-70B and Falcon 180B in certain tasks on the Hugging Face leaderboard.

Open source AI coding models

While there are closed source coding models like ChatGPT and Github Copilot, Meta has designed an open source one just for writing code. It even has a specialized version for Python, which is a critical open source language for AI applications. 

• Code Llama – In benchmark testing, Code Llama outperformed other state-of-the-art publicly available LLMs on code tasks. It can make software development workflows faster and more efficient and lower the barrier to entry for people learning to code.
  
  Code Llama is available in three models:
  
  Code Llama: the foundational code model
  Code Llama Python: specialized for Python
  Code Llama Instruct: fine-tuned for understanding natural language instructions (e.g., code me a website in HTML with these features) 
  

Open source AI vision models

ChatGPT gets vision features from OpenAI’s GPT-4V model, a powerful vision model, but there’s at least one open source vision model that rivals it – LlaVA. 

You can also pair LlaVA with an open source vision library that’s widely used for computer vision tasks to add well-developed AI capabilities quickly.

• LlaVA 1.5 – Think GPT-4 vision but open-source. 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. It achieves similar and impressive chat capabilities that mimic the multimodal GPT-4 model from OpenAI.
  
  
• OpenCV – OpenCV (Open Source Computer Vision Library) isn’t a model like LlaVA, but a robust and versatile library tailored for computer vision tasks – including image recognition, 2D or 3D analysis, motion tracking, and facial recognition. Known for real-time computer vision capabilities, it's a favored choice for developers working on real-time vision applications.

Open source AI frameworks and libraries

AI frameworks provide the foundation and tools for developing, training, and deploying AI capabilities. While many are new and developing, some leaders are emerging in developing enterprise AI applications.

LLM frameworks

• LlamaIndex – LlamaIndex is a simple, flexible data framework connecting custom data sources to large language models (LLMs).
  
  
• Langchain – LangChain is a framework designed to simplify the creation of applications using large language models. As a language model integration framework, LangChain's use-cases largely overlap with language models in general – including document analysis and summarization, chatbots, and code analysis.

Deep learning frameworks and libraries

• TensorFlow – Extensive library for high-performance numerical computations.
  
  
• PyTorch – Created by Facebook's AI Research lab, it’s known for dynamic computation graphing – making it particularly suitable for research.
  
  
• Caffe – Developed by the Berkeley Vision and Learning Center, Caffe is known for its deep learning applications speed and being modular.
  
  
• Theano – A foundational Python library for efficient definition, optimization, and evaluation of mathematical expressions.

Machine learning frameworks and libraries

• Scikit-learn – Simplifies implementing machine learning algorithms – especially for predictive data analysis.
  
  
• NumPy – NumPy is a library for the Python programming language – adding support for large, multi-dimensional arrays and matrices required in ML/AI applications.

Open source AI environments and notebooks

Development environments create a virtual space for coding, testing, and visualizing data – allowing for interactive and collaborative AI development.

Interactive notebooks

• Jupyter Notebook – An open-source web application that allows you to create and share documents containing live code, equations, visualizations, and explanatory text.
  
  
• Apache Zeppelin – Web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala, Python, R, and more.
  
  
• Keras – A high-level neural networks API, it can run on top of TensorFlow, CNTK, or Theano. Keras also has an open-source library that provides a Python interface for artificial neural networks.

Open source AI vector databases

Vector databases are a critical part of AI applications – especially production LLM capabilities. 

• Qdrant on Docker – From the proven open source expertise of Docker, Quadrant is a production-ready vector database. It enables JSON payloads to be associated with vectors, providing storage and filtering.
  
  
• Milvus – Created in 2019, Milvus stores, indexes, and manages massive embedding vectors from deep neural networks and machine learning (ML) models.
  
  
• Weaviate – Weaviate is an AI native vector database that lets you store data objects and vector embeddings from ML models and scales into billions of data objects.
  
  
• Atlas Vector Search from MongoDB – MongoDB is a trusted open source database solution and its vector database tool lets you combine operational data and vector data into a single platform. 

Open source AI deployment and scaling

Post-development, your AI models must be deployed and scaled efficiently to handle real-world demands. Many companies already use open source technologies like Kubernetes for containerization and microservices that can be integrated to scale new AI applications. 

Container orchestration

• Kubernetes – Kubernetes is an open-source platform that automates the deployment, scaling, and management of containerized applications. It orchestrates containers across clusters of machines, ensuring optimal resource utilization and high availability. 
  
  Its extensible architecture and strong community support make it a versatile choice for managing microservices and cloud-native applications that can also deploy and scale new AI applications.
  
  
• Docker – Docker is an open-source platform that simplifies the process of creating, deploying, and running applications by using containers. These containers allow developers to package an application with all the parts it needs, such as libraries and other dependencies, ensuring it will run seamlessly in any environment. 
  
  The lightweight nature of containers, coupled with Docker's easy-to-use interface, makes it a preferred choice for developers aiming for efficient and consistent AI application delivery.

Open source AI programming languages

Programming languages are the bedrock of AI development. If you focus on one language for AI, it should be Python. It already has extensive libraries and community support for AI and machine learning projects.

• Python –  Python is platform-agnostic and facilitates seamless development across different operating systems. Senior software engineers find it invaluable when working in the diverse environments required to build AI applications. 
  
  Python’s ability to integrate well with other languages and tools is another standout feature, simplifying the creation and deployment of AI solutions. The open-source nature of Python encourages a culture of collaboration and innovation, allowing for the broader dissemination of knowledge and tools within the AI space. 
  
  This blend of features underscores Python's prime position as a linchpin in the open-source AI development realm – making it a preferred choice for many in the community.

How do you put all these open source technologies together? That’s the most challenging part of building an open source ecosystem for enterprise applications, and it takes a team of AI experts to do it.

You can either make heavy investments to hire internally or find an external partner like Codingscape.

How do I hire open source AI experts?

Instead of waiting 6-18 months to recruit for expensive open source AI teams, you could engage Codingscape and start on your AI roadmap next quarter. We can assemble a senior AI development team for you in 4-6 weeks. 

It’ll be faster to get started, more cost-efficient than internal hiring, and we’ll deliver high-quality results quickly. We’re already building AI applications for our partners using open source technology and helping them plan their investments for 2024. 

Zappos, Twilio, and Veho are just a few companies that trust us with their cloud-native initiatives.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.