Today's Open Source (2024-09-04): Mini-Omni - First Open-Source Real-Time Voice Interaction Model
Discover the latest in AI open-source models like Mini-Omni, OLMoE, and RAG Techniques. Real-time voice interaction, MOE, and cutting-edge retrieval systems explored.
Here are some interesting AI open-source models and frameworks I wanted to share today:
Project: Mini-Omni
Mini-Omni is an open-source multimodal language model that supports real-time, end-to-end voice input and streaming audio output. It can "think and speak" at the same time, generating text and audio simultaneously.
To achieve this, the authors introduced a text-guided speech generation method and a batch parallel strategy during inference to improve performance.
Mini-Omni is the first fully open-source real-time voice interaction model, offering great potential for future research.
Links:
https://huggingface.co/gpt-omni/mini-omni
https://github.com/gpt-omni/mini-omni
https://arxiv.org/abs/2408.16725
Project: OLMoE
OLMoE-1B-7B is an open-source MOE (Mixture of Experts) model developed by the Allen Institute for AI, featuring 1 billion active and 7 billion total parameters.
It performs well among models with 1B parameters and is competitive with larger models like Llama2-13B and DeepSeekMoE-16B.
OLMoE is fully open-source, including model weights, code, and datasets.
Links:
https://github.com/allenai/OLMoE
https://arxiv.org/abs/2409.02060v1
https://huggingface.co/allenai/OLMoE-1B-7B-0924
Project: RAG_Techniques
RAG techniques are transforming how information retrieval is combined with AI generation.
This project presents a set of advanced RAG methods to improve accuracy, efficiency, and context richness in RAG systems.
It offers comprehensive tutorials and practical guides, making it a valuable resource for researchers and practitioners pushing RAG innovation.
Link:
https://github.com/NirDiamant/RAG_Techniques
Project: Anthropic Quickstarts
Anthropic Quickstarts is a collection of projects designed to help developers quickly start building deployable applications with the Anthropic API.
Each project provides a solid foundation that developers can easily customize to meet specific needs.
Link:
https://github.com/anthropics/anthropic-quickstarts
Project: HuixiangDou
HuixiangDou is a large language model (LLM) assistant system developed by the Shanghai AI Lab.
It helps algorithm developers by offering in-depth answers to questions about open-source algorithm projects. The system works well in group chats on messaging platforms like WeChat and Lark.
Text vectorization identifies which questions to answer, avoiding irrelevant or non-technical content.
Links:
https://github.com/InternLM/HuixiangDou
https://arxiv.org/abs/2401.08772
Project: ReMind
Remind is a local AI agent that captures and indexes local activities, such as screenshots and audio. It transcribes and summarizes these actions for easy recall.
Using advanced AI models, ReMind provides detailed summaries of daily activities and answers questions based on personal operation history.
Link:
