base on An all-in-one LLMs Chat UI for Apple Silicon Mac using MLX Framework. <div align="center"> # Chat with MLX 🧑‍💻 [](https://badge.fury.io/py/chat-with-mlx) [](https://pypistats.org/packages/chat-with-mlx) [](https://github.com/qnguyen3/chat-with-mlx/blob/main/LICENSE.md) [](https://badge.fury.io/py/chat-with-mlx) </div> An all-in-one Chat Playground using Apple MLX on Apple Silicon Macs.  ## Features - **Privacy-enhanced AI**: Chat with your favourite models and data securely. - **MLX Playground**: Your all in one LLM Chat UI for Apple MLX - **Easy Integration**: Easy integrate any HuggingFace and MLX Compatible Open-Source Models. - **Default Models**: Llama-3, Phi-3, Yi, Qwen, Mistral, Codestral, Mixtral, StableLM (along with Dolphin and Hermes variants) ## Installation and Usage ### Easy Setup - Install Pip - Install: `pip install chat-with-mlx` ### Manual Pip Installation ```bash git clone https://github.com/qnguyen3/chat-with-mlx.git cd chat-with-mlx python -m venv .venv source .venv/bin/activate pip install -e . ``` #### Manual Conda Installation ```bash git clone https://github.com/qnguyen3/chat-with-mlx.git cd chat-with-mlx conda create -n mlx-chat python=3.11 conda activate mlx-chat pip install -e . ``` #### Usage - Start the app: `chat-with-mlx` ## Add Your Model Please checkout the guide [HERE](ADD_MODEL.MD) ## Known Issues - When the model is downloading by Solution 1, the only way to stop it is to hit `control + C` on your Terminal. - If you want to switch the file, you have to manually hit STOP INDEXING. Otherwise, the vector database would add the second document to the current database. - You have to choose a dataset mode (Document or YouTube) in order for it to work. - **Phi-3-small** can't do streaming in completions ## Why MLX? MLX is an array framework for machine learning research on Apple silicon, brought to you by Apple machine learning research. Some key features of MLX include: - **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror the Python API. MLX has higher-level packages like `mlx.nn` and `mlx.optimizers` with APIs that closely follow PyTorch to simplify building more complex models. - **Composable function transformations**: MLX supports composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization. - **Lazy computation**: Computations in MLX are lazy. Arrays are only materialized when needed. - **Dynamic graph construction**: Computation graphs in MLX are constructed dynamically. Changing the shapes of function arguments does not trigger slow compilations, and debugging is simple and intuitive. - **Multi-device**: Operations can run on any of the supported devices (currently the CPU and the GPU). - **Unified memory**: A notable difference from MLX and other frameworks is the *unified memory model*. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without transferring data. ## Acknowledgement I would like to send my many thanks to: - The Apple Machine Learning Research team for the amazing MLX library. - LangChain and ChromaDB for such easy RAG Implementation - All contributors ## Star History [](https://star-history.com/#qnguyen3/chat-with-mlx&Date) ", Assign "at most 3 tags" to the expected json: {"id":"8163","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"