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base on MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX. [](https://github.com/Blaizzy/mlx-vlm/actions/workflows/python-publish.yml)
# MLX-VLM
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Command Line Interface (CLI)](#command-line-interface-cli)
- [Chat UI with Gradio](#chat-ui-with-gradio)
- [Python Script](#python-script)
- [Multi-Image Chat Support](#multi-image-chat-support)
- [Supported Models](#supported-models)
- [Usage Examples](#usage-examples)
- [Fine-tuning](#fine-tuning)
## Installation
The easiest way to get started is to install the `mlx-vlm` package using pip:
```sh
pip install mlx-vlm
```
## Usage
### Command Line Interface (CLI)
Generate output from a model using the CLI:
```sh
python -m mlx_vlm.generate --model mlx-community/Qwen2-VL-2B-Instruct-4bit --max-tokens 100 --temperature 0.0 --image http://images.cocodataset.org/val2017/000000039769.jpg
```
### Chat UI with Gradio
Launch a chat interface using Gradio:
```sh
python -m mlx_vlm.chat_ui --model mlx-community/Qwen2-VL-2B-Instruct-4bit
```
### Python Script
Here's an example of how to use MLX-VLM in a Python script:
```python
import mlx.core as mx
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
# Load the model
model_path = "mlx-community/Qwen2-VL-2B-Instruct-4bit"
model, processor = load(model_path)
config = load_config(model_path)
# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
# image = [Image.open("...")] can also be used with PIL.Image.Image objects
prompt = "Describe this image."
# Apply chat template
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=len(image)
)
# Generate output
output = generate(model, processor, formatted_prompt, image, verbose=False)
print(output)
```
### Server (FastAPI)
To start the server
```sh
python -m mlx_vlm.server
```
Models can be loaded or unloaded dynamically and they are cached (one at a time) when the server is running.
Usage example:
```sh
curl -X POST "http://localhost:8000/generate" \
-H "Content-Type: application/json" \
-d '{
"model": "mlx-community/Qwen2.5-VL-32B-Instruct-8bit",
"image": ["/path/to/repo/examples/images/renewables_california.png"],
"prompt": "This is today'\''s chart fo energy demand in California. Can you provide an analysis of the chart and comment on the implications for renewable energy in California?",
"system": "You are a helpful assistant.",
"stream": true,
"max_tokens": 1000
}'
```
## Multi-Image Chat Support
MLX-VLM supports analyzing multiple images simultaneously with select models. This feature enables more complex visual reasoning tasks and comprehensive analysis across multiple images in a single conversation.
### Supported Models
The following models support multi-image chat:
1. Idefics 2
2. LLaVA (Interleave)
3. Qwen2-VL
4. Phi3-Vision
5. Pixtral
### Usage Examples
#### Python Script
```python
from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config
model_path = "mlx-community/Qwen2-VL-2B-Instruct-4bit"
model, processor = load(model_path)
config = load_config(model_path)
images = ["path/to/image1.jpg", "path/to/image2.jpg"]
prompt = "Compare these two images."
formatted_prompt = apply_chat_template(
processor, config, prompt, num_images=len(images)
)
output = generate(model, processor, formatted_prompt, images, verbose=False)
print(output)
```
#### Command Line
```sh
python -m mlx_vlm.generate --model mlx-community/Qwen2-VL-2B-Instruct-4bit --max-tokens 100 --prompt "Compare these images" --image path/to/image1.jpg path/to/image2.jpg
```
## Video Understanding
MLX-VLM also supports video analysis such as captioning, summarization, and more, with select models.
### Supported Models
The following models support video chat:
1. Qwen2-VL
2. Qwen2.5-VL
3. Idefics3
4. LLaVA
With more coming soon.
### Usage Examples
#### Command Line
```sh
python -m mlx_vlm.video_generate --model mlx-community/Qwen2-VL-2B-Instruct-4bit --max-tokens 100 --prompt "Describe this video" --video path/to/video.mp4 --max-pixels 224 224 --fps 1.0
```
These examples demonstrate how to use multiple images with MLX-VLM for more complex visual reasoning tasks.
# Fine-tuning
MLX-VLM supports fine-tuning models with LoRA and QLoRA.
## LoRA & QLoRA
To learn more about LoRA, please refer to the [LoRA.md](./mlx_vlm/LORA.MD) file.
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