Ai2 OLMoE vs VecML

Our app is intended to help researchers better explore how to make on-device intelligence better and to enable developers to quickly prototype new AI experiences – all with no cloud connectivity required. The Ai2 OLMoE app can be used to: * Experience which real-world tasks state-of-the-art local models are capable of * Research how to improve small AI models * Test your own models locally using our open-source codebase * Integrate OLMoE into other iOS applications Best of all, interactions with OLMoE are fully private. Unless you decide to share a conversation, your conversations never leave your device. Features: * Efficiency: OLMoE is a highly efficient mixture-of-experts version of the Ai2 OLMo family of models * Performance: OLMoE is trained with the fully open, state-of-the-art Tülu 3 recipe * Privacy: The Ai2 OLMoE app provides privacy and security by operating completely on-device * Shareable: Easily share the output of your conversations with friends or colleagues * Truly open: The OLMoE model and the application code are fully open-source OLMoE is proudly offered by Ai2 as a powerful research tool to help the community better understand how LLMs are created and how they operate. When interacting with OMLoE, always use your best judgment, fact-check important information, and consider the context when interpreting content generated in this app. Open source code repository available at: https://github.com/allenai/OLMoE.swift Terms of service: https://allenai.org/terms/2024-09-25 Privacy policy: https://allenai.org/privacy-policy/2022-07-21

VecML APP offers tools for intelligent photo search and large language models (LLMs) fully on device. Users can select from a range of small LLMs and engage in conversations with documents stored locally. The app can process files (PDFs, DOC documents, images, etc.) to generate embedding vectors, which are stored locally on the device. These embedding vectors are crucial for RAG (retrieval-augmented generation) capabilities, enhancing the LLM's ability to provide more accurate and relevant responses when queries are related to the local files.