Phi-3-Vision on Apple silicon with MLX
Project description
Phi-3-Vision for Apple MLX
Phi-3-Vision for Apple MLX is a powerful and flexible AI agent framework that leverages the Phi-3-Vision model to perform a wide range of tasks, from visual question answering to code generation and execution. This project aims to provide an easy-to-use interface for interacting with the Phi-3-Vision model, while also offering advanced features like custom toolchains and model quantization.
Phi-3-Vision is a state-of-the-art vision-language model that excels in understanding and generating content based on both textual and visual inputs. By integrating this model with Apple's MLX framework, we provide a high-performance solution optimized for Apple silicon.
Quick Start
1. Install Phi-3 Vision MLX:
To install Phi-3-Vision-MLX, run the following command:
pip install phi-3-vision-mlx
2. Launch Phi-3 Vision MLX:
To launch Phi-3-Vision-MLX:
phi3v
Or in a Python script:
from phi_3_vision_mlx import Agent
agent = Agent()
Usage
Visual Question Answering (VQA)
agent('What is shown in this image?', 'https://collectionapi.metmuseum.org/api/collection/v1/iiif/344291/725918/main-image')
agent.end()
Generative Feedback Loop
The agent can be used to generate code, execute it, and then modify it based on feedback:
agent('Plot a Lissajous Curve.')
agent('Modify the code to plot 3:4 frequency')
agent.end()
API Tool Use
You can use the agent to create images or generate speech using API calls:
agent('Draw "A perfectly red apple, 32k HDR, studio lighting"')
agent.end()
agent('Speak "People say nothing is impossible, but I do nothing every day."')
agent.end()
Custom Toolchain
Toolchains allow you to customize the agent's behavior for specific tasks. Here are three examples:
Example 1: In-Context Learning (ICL)
You can create a custom toolchain to add context to the prompt:
from phi_3_vision_mlx import load_text
# Create tool
def add_text(prompt):
prompt, path = prompt.split('@')
return f'{load_text(path)}\n<|end|>\n<|user|>{prompt}'
# Chain tools
toolchain = """
prompt = add_text(prompt)
responses = generate(prompt, images)
"""
# Create agent
agent = Agent(toolchain, early_stop=100)
# Run agent
agent('How to inspect API endpoints? @https://raw.githubusercontent.com/gradio-app/gradio/main/guides/08_gradio-clients-and-lite/01_getting-started-with-the-python-client.md')
This toolchain adds context to the prompt from an external source, enhancing the agent's knowledge for specific queries.
Example 2: Retrieval Augmented Generation (RAG)
You can create another custom toolchain for retrieval-augmented generation (RAG) to code:
from phi_3_vision_mlx import VDB
import datasets
# User proxy
user_input = 'Comparison of Sortino Ratio for Bitcoin and Ethereum.'
# Create tool
def rag(prompt, repo_id="JosefAlbers/sharegpt_python_mlx", n_topk=1):
ds = datasets.load_dataset(repo_id, split='train')
vdb = VDB(ds)
context = vdb(prompt, n_topk)[0][0]
return f'{context}\n<|end|>\n<|user|>Plot: {prompt}'
# Chain tools
toolchain_plot = """
prompt = rag(prompt)
responses = generate(prompt, images)
files = execute(responses, step)
"""
# Create agent
agent = Agent(toolchain_plot, False)
# Run agent
_, images = agent(user_input)
Example 3: Multi-Agent Interaction
You can also have multiple agents interacting to complete a task:
agent_writer = Agent(early_stop=100)
agent_writer(f'Write a stock analysis report on: {user_input}', images)
Batch Generation
For efficient processing of multiple prompts:
from phi_3_vision_mlx import generate
generate([
"Write an executive summary for a communications business plan",
"Write a resume.",
"Write a mystery horror.",
"Write a Neurology ICU Admission Note.",])
Model and Cache Quantization
Quantization can significantly reduce model size and improve inference speed:
generate("Write a cosmic horror.", quantize_cache=True)
generate("Write a cosmic horror.", quantize_model=True)
LoRA Training and Inference
Fine-tune the model for specific tasks:
from phi_3_vision_mlx import train_lora
train_lora(lora_layers=5, lora_rank=16, epochs=10, lr=1e-4, warmup=.5, mask_ratios=[.0], adapter_path='adapters', dataset_path = "JosefAlbers/akemiH_MedQA_Reason")
Use the fine-tuned model:
generate("Write a cosmic horror.", adapter_path='adapters')
Benchmarks
Task | Vanilla Model | Quantized Model | Quantized Cache | LoRA |
---|---|---|---|---|
Text Generation | 8.72 tps | 55.97 tps | 7.04 tps | 8.71 tps |
Image Captioning | 8.04 tps | 32.48 tps | 1.77 tps | 8.00 tps |
Batched Generation | 30.74 tps | 106.94 tps | 20.47 tps | 30.72 tps |
License
This project is licensed under the MIT License.
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