| Name | chat-with-mlx JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | None |
| Summary | A Retrieval-augmented Generation (RAG) chat interface with support for multiple open-source models, designed to run natively on MacOS and Apple Silicon with MLX. |
| upload_time | 2024-06-13 04:07:04 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License |
| keywords |
mlx
chat
chatbot
chat_with_mlx
chat_with_mlx
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<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)
Raw data
{
"_id": null,
"home_page": null,
"name": "chat-with-mlx",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "mlx, chat, chatbot, chat_with_mlx, chat_with_mlx",
"author": null,
"author_email": "Quan Nguyen <quanjenkey@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/ad/09/91f59103795fd3335a76d025d53ce2f58c59f6f2da7bb076fb99f8d1ce07/chat_with_mlx-0.2.2.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\n# Chat with MLX \ud83e\uddd1\u200d\ud83d\udcbb\n\n[](https://badge.fury.io/py/chat-with-mlx)\n[](https://pypistats.org/packages/chat-with-mlx)\n[](https://github.com/qnguyen3/chat-with-mlx/blob/main/LICENSE.md)\n[](https://badge.fury.io/py/chat-with-mlx)\n</div>\n\nAn all-in-one Chat Playground using Apple MLX on Apple Silicon Macs.\n\n\n\n## Features\n\n- **Privacy-enhanced AI**: Chat with your favourite models and data securely.\n- **MLX Playground**: Your all in one LLM Chat UI for Apple MLX\n- **Easy Integration**: Easy integrate any HuggingFace and MLX Compatible Open-Source Models.\n- **Default Models**: Llama-3, Phi-3, Yi, Qwen, Mistral, Codestral, Mixtral, StableLM (along with Dolphin and Hermes variants)\n\n## Installation and Usage\n\n### Easy Setup\n\n- Install Pip\n- Install: `pip install chat-with-mlx`\n\n### Manual Pip Installation\n\n```bash\ngit clone https://github.com/qnguyen3/chat-with-mlx.git\ncd chat-with-mlx\npython -m venv .venv\nsource .venv/bin/activate\npip install -e .\n```\n\n#### Manual Conda Installation\n\n```bash\ngit clone https://github.com/qnguyen3/chat-with-mlx.git\ncd chat-with-mlx\nconda create -n mlx-chat python=3.11\nconda activate mlx-chat\npip install -e .\n```\n\n#### Usage\n\n- Start the app: `chat-with-mlx`\n\n## Add Your Model\n\nPlease checkout the guide [HERE](ADD_MODEL.MD)\n\n## Known Issues\n\n- When the model is downloading by Solution 1, the only way to stop it is to hit `control + C` on your Terminal.\n- 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.\n- You have to choose a dataset mode (Document or YouTube) in order for it to work.\n- **Phi-3-small** can't do streaming in completions\n\n## Why MLX?\n\nMLX is an array framework for machine learning research on Apple silicon,\nbrought to you by Apple machine learning research.\n\nSome key features of MLX include:\n\n- **Familiar APIs**: MLX has a Python API that closely follows NumPy. MLX\n also has fully featured C++, [C](https://github.com/ml-explore/mlx-c), and\n [Swift](https://github.com/ml-explore/mlx-swift/) APIs, which closely mirror\n the Python API. MLX has higher-level packages like `mlx.nn` and\n `mlx.optimizers` with APIs that closely follow PyTorch to simplify building\n more complex models.\n\n- **Composable function transformations**: MLX supports composable function\n transformations for automatic differentiation, automatic vectorization,\n and computation graph optimization.\n\n- **Lazy computation**: Computations in MLX are lazy. Arrays are only\n materialized when needed.\n\n- **Dynamic graph construction**: Computation graphs in MLX are constructed\n dynamically. Changing the shapes of function arguments does not trigger\n slow compilations, and debugging is simple and intuitive.\n\n- **Multi-device**: Operations can run on any of the supported devices\n (currently the CPU and the GPU).\n\n- **Unified memory**: A notable difference from MLX and other frameworks\n is the *unified memory model*. Arrays in MLX live in shared memory.\n Operations on MLX arrays can be performed on any of the supported\n device types without transferring data.\n\n## Acknowledgement\n\nI would like to send my many thanks to:\n\n- The Apple Machine Learning Research team for the amazing MLX library.\n- LangChain and ChromaDB for such easy RAG Implementation\n- All contributors\n\n## Star History\n\n[](https://star-history.com/#qnguyen3/chat-with-mlx&Date)\n",
"bugtrack_url": null,
"license": "MIT License",
"summary": "A Retrieval-augmented Generation (RAG) chat interface with support for multiple open-source models, designed to run natively on MacOS and Apple Silicon with MLX.",
"version": "0.2.2",
"project_urls": null,
"split_keywords": [
"mlx",
" chat",
" chatbot",
" chat_with_mlx",
" chat_with_mlx"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a5a59b6d014a29ae9f0fb7304c4536da9996358595acf04f9e61bec5fb135d36",
"md5": "0eccd5f51808dfaf0e2e200ffacc7a14",
"sha256": "2c7404cef8f3d887c008c3d3428c711f76926d21d2618ee6f3c337162c64bba6"
},
"downloads": -1,
"filename": "chat_with_mlx-0.2.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "0eccd5f51808dfaf0e2e200ffacc7a14",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 24333,
"upload_time": "2024-06-13T04:07:02",
"upload_time_iso_8601": "2024-06-13T04:07:02.135923Z",
"url": "https://files.pythonhosted.org/packages/a5/a5/9b6d014a29ae9f0fb7304c4536da9996358595acf04f9e61bec5fb135d36/chat_with_mlx-0.2.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ad0991f59103795fd3335a76d025d53ce2f58c59f6f2da7bb076fb99f8d1ce07",
"md5": "887f9b82b5f4b938cf03d71841a2dada",
"sha256": "1718bceb73bddb3c8fcfb315d6f31cb532e06f9cecea0174a39baea75aaee4d3"
},
"downloads": -1,
"filename": "chat_with_mlx-0.2.2.tar.gz",
"has_sig": false,
"md5_digest": "887f9b82b5f4b938cf03d71841a2dada",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 97995,
"upload_time": "2024-06-13T04:07:04",
"upload_time_iso_8601": "2024-06-13T04:07:04.002778Z",
"url": "https://files.pythonhosted.org/packages/ad/09/91f59103795fd3335a76d025d53ce2f58c59f6f2da7bb076fb99f8d1ce07/chat_with_mlx-0.2.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-06-13 04:07:04",
"github": false,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"lcname": "chat-with-mlx"
}
