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LLM App Frameworks

by Stephen M. Walker II, Co-Founder / CEO

LLM App Frameworks

Here is a glossary of top Large Language Model (LLM) application programming frameworks:

  1. OpenAI: OpenAI is an AI research lab that has developed several LLMs, including GPT-3 and ChatGPT. These models are capable of generating text, translating languages, and answering questions in an informative way.

  2. ChatGPT: A specific LLM developed by OpenAI, designed for use in chatbots. It's trained on a massive dataset of text and code, enabling it to learn the patterns of human conversation and generate natural and engaging responses.

  3. Bard: Bard is a family of LLMs developed by Google AI. They are designed to be used as a starting point for developing other AI models and are trained on massive datasets of text and code.

  4. LangChain: An open-source orchestration framework designed to be easy to use and scalable. It provides a simple API, a distributed architecture, and features for managing LLMs such as load balancing, fault tolerance, and security.

  5. LlamaIndex: A data framework for LLMs that provides tools to ingest, structure, and access private or domain-specific data. It can be used to connect LLMs to a variety of data sources, including APIs, PDFs, documents, and SQL databases.

  6. vLLM: A framework for LLM inference and serving. It's one of the open-source libraries for LLM inference and serving.

  7. Ray Serve: A framework considered for a stable pipeline and flexible deployment. It is best suited for more mature projects.

  8. **MLC LLM: A universal deployment solution that enables LLMs to run efficiently on consumer devices, leveraging native hardware acceleration.

  9. DeepSpeed-MII: A framework to use if you already have experience with the DeepSpeed library and wish to continue using it for deploying LLMs.

  10. OpenLLM: A framework that supports connecting multiple adapters to only one deployed LLM. It allows the use of different implementations: Pytorch, Tensorflow, or Flax.

  11. FlexGen: A tool for running large language models on a single GPU for throughput-oriented scenarios.

  12. Flowise: A drag & drop UI to build your customized LLM flow using LangchainJS.

  13. lanarky: A FastAPI framework to build production-grade LLM applications.

  14. Xinference: A framework that gives you the freedom to use any LLM you need. It empowers you to run inference with any open-source language models, speech recognition models, and multimodal models.

  15. Giskard: A testing framework dedicated to ML models, from tabular to LLMs.

Remember, the best framework for a particular application will depend on the specific requirements of that application.

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