Mac Exclusive Large Model Standalone Application Chat-with-MLX, Supporting RAG！

Hello everyone, today I want to introduce a revolutionary product designed specifically for Apple Silicon Macs—[Chat with MLX]:https://github.com/qnguyen3/chat-with-mlx!

This is not just a chat tool but a full-featured large language model chat interface. It leverages Apple’s MLX framework to provide an unprecedented level of privacy protection and chat experience. Chat with MLX is based on Retrieval-Augmented Generation (RAG) technology, capable of interacting with various open-source models, supporting multiple file formats, and adding YouTube videos via URL. This application supports multilingual interaction, easily integrates any HuggingFace and MLX-compatible open-source models, and is particularly suitable for use on macOS and Apple Silicon.

Highlights of Chat-with-MLX

Chat-with-MLX is a chat interface based on “Retrieval-Augmented Generation (RAG)” that can utilize multiple open-source models to enhance the chat experience. With this tool, users can interact with their data, supporting various file formats (such as doc(x), pdf, txt) and adding YouTube videos via URL. It supports multilingual interaction, including English, Spanish, Chinese, Vietnamese, and Turkish. Additionally, Chat-with-MLX can easily integrate any HuggingFace and MLX-compatible open-source models, providing a native RAG example for macOS and Apple Silicon.

Use Cases

1. Multilingual Data Query: When you need to query specific data in different languages, whether it’s text, PDF files, or online videos.
2. Enhance Chat Experience with Powerful Models: Utilize advanced open-source models (such as Google Gemma, Mistral, StableLM, etc.) to improve the responsiveness and accuracy of chat applications.
3. Research and Development: Ideal for users with research and development needs in AI and machine learning, especially in an Apple Silicon (e.g., M1 chip) environment, leveraging the MLX framework for efficient development and testing.
4. Document Management and Retrieval: When you need to retrieve information from a large amount of text or multimedia content, providing a more intuitive and faster access method.

Key Features

• Supports chatting with data, including various file formats and YouTube videos.
• Multilingual support.
• Easily integrates various HuggingFace and MLX-compatible open-source models.
• Native RAG support on macOS and Apple Silicon.

Installation Guide

To simplify the installation process, Chat-with-MLX is packaged into a standalone launcher. Users can run it with a simple click, without the need to configure a complex Python environment.

Chat with MLX standalone application download URL: https://www.patreon.com/user/shop/chat-with-mlx-standalone-application-for-243328?u=122446863&utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=productshare_creator&utm_content=join_link

Note: The Chat-with-MLX App only supports devices equipped with the Mac M1/2/3 series chips.

Installation Steps

1. Download the DMG image file from the above link, and drag the app file into the Applications folder.
2. After the installation is complete, do not open it from the Launchpad for the first time; instead, right-click to open it in the Applications folder, as shown below. For more details, refer to Common Issues with Installing Software on Mac.
3. The software will automatically open the operation interface in the default browser, with the address http://127.0.0.1:7860/. You can start using it in the browser at this point.

• After selecting a model, click to load the model to start chatting. Click on RAG settings to load local data.

• In addition to the integrated Tongyi Qianwen model, you can also add other MLX models in the model management section.

Model and Memory Configuration

The table below shows the availability of different model sizes under various memory configurations. The left value in each cell indicates the availability of 4-bit quantization, while the right value indicates the availability of 8-bit quantization for the corresponding model size and memory combination.

For example, if your model size is between 14B and 34B parameters and you have 48GB of memory, you can use 4-bit quantization (✅), but 8-bit quantization is not available (❌).

Model Size/Memory	0.5B ~ 4B	6B ~ 13B	14B ~ 34B	40B ~ 56B	65B ~ 72B	100B ~ 180B
8GB	✅/✅	✅/❌	❌/❌	❌/❌	❌/❌	❌/❌
18GB	✅/✅	✅/✅	✅/❌	❌/❌	❌/❌	❌/❌
36GB	✅/✅	✅/✅	✅/❌	✅/❌	❌/❌	❌/❌
48GB	✅/✅	✅/✅	✅/✅	✅/❌	❌/❌	❌/❌
64GB	✅/✅	✅/✅	✅/✅	✅/✅	✅/❌	❌/❌
96GB	✅/✅	✅/✅	✅/✅	✅/✅	✅/❌	✅/❌
192GB	✅/✅	✅/✅	✅/✅	✅/✅	✅/✅	✅/❌

Dear friends, if you find this article helpful, please give it a thumbs up 👍 ! Let’s enjoy the new AI chat experience on Mac together!