List of Gen AI Frameworks

List of Generative AI (GenAI) or Large Language Model (LLM) related frameworks that are specifically designed or used to develop applications involving language models, text generation, and natural language processing tasks:

1. Langchain

• Description: A framework designed for building applications with large language models (LLMs). It facilitates prompt engineering, chaining, and connecting LLMs with external APIs and tools, making it easier to develop conversational AI and other LLM-based applications.

2. Hugging Face Transformers

• Description: Hugging Face provides one of the most popular libraries for working with transformer-based models, such as GPT, BERT, T5, and more. It offers pre-trained models and tools for tasks like text generation, summarization, translation, and question answering.

3. GPT-3 (OpenAI API)

• Description: OpenAI’s GPT-3 is a state-of-the-art language model known for its ability to generate human-like text. While not a framework in itself, the GPT-3 API can be used as part of a framework to build GenAI applications for conversational agents, content generation, and more.

4. DeepPavlov

• Description: DeepPavlov is an open-source framework focused on natural language processing (NLP) and dialogue systems. It includes various models for chatbots, question answering, and other GenAI applications and provides easy integration for LLM-based systems.

5. Rasa

• Description: Rasa is an open-source conversational AI platform. It’s used for building contextual assistants and chatbots, using machine learning-based language models for understanding and generating natural language responses.

6. Microsoft DeepSpeed

• Description: DeepSpeed is a deep learning optimization library developed by Microsoft. It’s designed for training large models (including LLMs) at scale, and it helps speed up the training and inference of Generative AI models.

7. OpenNLP

• Description: OpenNLP is an Apache project that provides machine learning-based libraries for processing text, including language modeling, tokenization, and parsing. Though not primarily focused on GenAI, it can be integrated into larger NLP workflows for language generation tasks.

8. Fairseq

• Description: Developed by Facebook AI Research, Fairseq is a framework for sequence-to-sequence models that is highly optimized for tasks involving text generation. It supports a wide range of architectures like transformers and BART, which are useful for training and deploying LLMs.

9. T5 (Text-to-Text Transfer Transformer)

• Description: T5, developed by Google Research, is a framework based on a unified text-to-text approach for various NLP tasks, including text generation, translation, and summarization. It’s based on the transformer architecture and has been adapted for many GenAI applications.

10. OpenAI Codex

• Description: Codex is a descendant of GPT-3, specifically fine-tuned to understand and generate programming code. It powers applications like GitHub Copilot, which assists in code generation and completion. While not a full-fledged framework, it is widely used in software development GenAI use cases.

11. EleutherAI GPT-Neo and GPT-J

• Description: EleutherAI is an open-source community focused on developing large language models. GPT-Neo and GPT-J are open-source alternatives to GPT-3, trained for general-purpose text generation tasks and available for use in building GenAI applications.

12. LLama (Meta AI)

• Description: LLaMA (Large Language Model Meta AI) is Meta’s open-source LLM designed to be efficient and effective across a wide range of NLP tasks, including text generation, summarization, and translation. It's being used for research and GenAI purposes.

13. Cohere

• Description: Cohere provides large language models that are optimized for tasks like text generation, summarization, and classification. Their API can be used to integrate GenAI capabilities into custom applications.

14. Anthropic Claude

• Description: Claude is a family of language models developed by Anthropic, designed for conversational AI and safety-critical applications. It focuses on natural language understanding and generation, offering an API for integrating GenAI capabilities.

These frameworks and models are designed to work with or are based on LLMs, enabling developers to build generative AI applications that understand and generate human-like text across a wide range of use cases. If you're looking to build applications that involve large language models or text generation, these frameworks are the most relevant.