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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
An all-in-one LLMs Chat UI for Apple Silicon Mac using MLX Framework.
| Date | Stars |
|---|---|
| 2026-07-31 | 1597 |
| 2026-08-01 | 1597 |
| 2026-08-06 | 1597 |
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<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)
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4ec793969b315f08, desc:chat ui