| Name | mlx-vlm JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | https://github.com/Blaizzy/mlx-vlm |
| Summary | Vision LLMs on Apple silicon with MLX and the Hugging Face Hub |
| upload_time | 2024-10-18 00:15:34 |
| maintainer | None |
| docs_url | None |
| author | Prince Canuma |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
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|
# 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 --temp 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"]
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, image, formatted_prompt, verbose=False)
print(output)
```
## 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, images, formatted_prompt, 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
```
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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"description": "# MLX-VLM\n\nMLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.\n\n## Table of Contents\n- [Installation](#installation)\n- [Usage](#usage)\n - [Command Line Interface (CLI)](#command-line-interface-cli)\n - [Chat UI with Gradio](#chat-ui-with-gradio)\n - [Python Script](#python-script)\n- [Multi-Image Chat Support](#multi-image-chat-support)\n - [Supported Models](#supported-models)\n - [Usage Examples](#usage-examples)\n- [Fine-tuning](#fine-tuning)\n\n## Installation\n\nThe easiest way to get started is to install the `mlx-vlm` package using pip:\n\n```sh\npip install mlx-vlm\n```\n\n## Usage\n\n### Command Line Interface (CLI)\n\nGenerate output from a model using the CLI:\n\n```sh\npython -m mlx_vlm.generate --model mlx-community/Qwen2-VL-2B-Instruct-4bit --max-tokens 100 --temp 0.0 --image http://images.cocodataset.org/val2017/000000039769.jpg\n```\n\n### Chat UI with Gradio\n\nLaunch a chat interface using Gradio:\n\n```sh\npython -m mlx_vlm.chat_ui --model mlx-community/Qwen2-VL-2B-Instruct-4bit\n```\n\n### Python Script\n\nHere's an example of how to use MLX-VLM in a Python script:\n\n```python\nimport mlx.core as mx\nfrom mlx_vlm import load, generate\nfrom mlx_vlm.prompt_utils import apply_chat_template\nfrom mlx_vlm.utils import load_config\n\n# Load the model\nmodel_path = \"mlx-community/Qwen2-VL-2B-Instruct-4bit\"\nmodel, processor = load(model_path)\nconfig = load_config(model_path)\n\n# Prepare input\nimage = [\"http://images.cocodataset.org/val2017/000000039769.jpg\"]\nprompt = \"Describe this image.\"\n\n# Apply chat template\nformatted_prompt = apply_chat_template(\n processor, config, prompt, num_images=len(image)\n)\n\n# Generate output\noutput = generate(model, processor, image, formatted_prompt, verbose=False)\nprint(output)\n```\n\n## Multi-Image Chat Support\n\nMLX-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.\n\n### Supported Models\n\nThe following models support multi-image chat:\n\n1. Idefics 2\n2. LLaVA (Interleave)\n3. Qwen2-VL\n4. Phi3-Vision\n5. Pixtral\n\n### Usage Examples\n\n#### Python Script\n\n```python\nfrom mlx_vlm import load, generate\nfrom mlx_vlm.prompt_utils import apply_chat_template\nfrom mlx_vlm.utils import load_config\n\nmodel_path = \"mlx-community/Qwen2-VL-2B-Instruct-4bit\"\nmodel, processor = load(model_path)\nconfig = load_config(model_path)\n\nimages = [\"path/to/image1.jpg\", \"path/to/image2.jpg\"]\nprompt = \"Compare these two images.\"\n\nformatted_prompt = apply_chat_template(\n processor, config, prompt, num_images=len(images)\n)\n\noutput = generate(model, processor, images, formatted_prompt, verbose=False)\nprint(output)\n```\n\n#### Command Line\n\n```sh\npython -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\n```\n\nThese examples demonstrate how to use multiple images with MLX-VLM for more complex visual reasoning tasks.\n\n# Fine-tuning\n\nMLX-VLM supports fine-tuning models with LoRA and QLoRA.\n\n## LoRA & QLoRA\n\nTo learn more about LoRA, please refer to the [LoRA.md](./mlx_vlm/LoRA.md) file.\n",
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