Run Mistral On A Mac

The new model Mistral 7B from the French startup has recently hit the headlines as a powerful competitor to GPT and Llama models in the same size category.  

Mistral is lightweight, capable, and perfect for running locally on personal computers, making it a great model for autonomous AI tasks in business and personal use. 

In this guide, you’ll learn the tech specs, the tools, and the key steps to install and run Mistral on a Mac.

Let’s get started! 

What you need to run Mistral on a Mac

To start with Mistral, you must check if your computer has the required specifications. To run the Mistral 7B model, you need: 

• A Mac M4 
• 16 GB RAM
• At least 50 GB of storage 
• 10 minutes of your time

You can also run Mistral on other versions of Macs, as Apple’s M1, M2, and M3 chips are engineered for machine learning workloads. Yet, the Mac M4 Mistral performance will be the best in the range. 

Tools to run Mistral on Mac

LM Studio is the best tool for running Mistral on Mac. Its beginner-friendly interface allows anyone to download and run Mistral for personal or business use in several steps. 

Integrated with Hugging Face, it gives access to over 1 million models, including Mistral models, and presents a friendly chat interface for deploying models locally and for free. 

How to run Mistral on Mac locally? 

To run Mistral on Mac, you have to follow these steps: 

1. Download and install LM Studio on your Mac
2. Select the needed model from the list
3. Download the selected model
4. Run the model. 

Let’s review these steps closely. 

Download and install LM Studio on your Mac

To download LM Studio, go to the official app’s website. 

After downloading LM Studio on your Mac, the system asks you to save the app to the Applications folder.

After saving the app to the apps folder, you have everything ready to install Mistral on Mac.

Search for Mistral models

LM Studio has over 600 different Mistral models to work with. Different model variations are better tuned for various specific tasks. For example, if you need to run Mistral for coding, use Codestral. It is available in LM Studio in 22B and 7B versions, yet the 7B version is suitable for running on a 16 GB Mac. 

If your task requires understanding images, graphs, and charts, you should use Pixtral models. For translation tasks, the Mistral Nemo model will work best. 

 

To find the model you need, do the following: 

Step 1. Go to the LM Studio search functionality.

Step 2. Type the name of the model in the Search field.

Step 3. Scroll down to select the variant of the model you need.

Download the selected model 

The download process takes up to a minute.

Run the model

After downloading, you must load the model to start working with it.

LM Studio allows you to work with Mistral using a straightforward chat interface. You can save your queries as separate projects and return to them later.

Why run Mistral on Mac locally? 

Mistral 7B is more capable of doing different tasks than other models of the same size. 

That is because of advancements in architecture, which allow Mistral to perform complex tasks with fewer parameters.

Mistral’s model outperforms the 13B Llama-2 in Math, coding, reasoning, and comprehension. All this is done with 7 billion parameters, allowing it to run this model on a personal PC.

This way, you get a great and performant model to customize it for your business needs completely for free. This is possible due to Mistral’s advancements in the architecture of the large language models. 

Mistral architecture advancements

The Mistral 7B rollout in September 2023 has marked the shift in AI development. 

Previously, there has been a trend to improve performance by increasing model size. So, xAI’s Grok-1 uses 314 billion parameters, Google’s PaLM 2 uses 340 billion parameters, and OpenAI’s GPT-4, although they are not disclosing their parameter count, is believed to use over a trillion parameters. 

Yet, training these large models is time-consuming and requires massive computational resources that exceed the latest Mac specifications hundreds of times.

Mistral made significant advancements in transformer architecture. They removed some layers and made their models perform a wide range of tasks with fewer parameters. 

Thanks to their advancements, highly performant 7B Mistral models can be run on a Mac with 16 GB of RAM.  

The free, lightweight models promote machine learning adoption by medium and small businesses and individuals, who can now use Mistral models for various use cases. 

Mistral use cases 

Mistral models are suitable for a range of applications, such as business, software development, research, and personal use.

Here are some examples: 

Business: 

• Chatbots: You can use Mistral to create chatbots and virtual assistants for customer support tasks. Yet, when selecting a model for chatbot building in LM Studio, pay attention to the model description. 
• Document summarization: Mistral can perform intelligent document processing tasks like base document summarization or data extraction.
• Content generation: Create content for marketing campaigns.
• Sentiment analysis: You can check the overall attitude of your audience toward your brand by asking Mistral to analyze your social media comments and reviews. 

Software development: 

• Code generation: Mistral supports multiple software languages and frameworks so that you can develop mobile applications, website code, or other projects. You can use it for the individual development of simple apps or their features, or code suggestions.
• Debugging: Mistral can read your code and point out possible improvements.
• Test generation: Use Mistral to enhance your code testing. 

Research: 

• Text summarization: Mistral is a great tool for summarizing sources for your research and navigating you through various learning resources.
• Translation: Mistral can help you translate publications or papers into other languages. 
• Mathematical reasoning: Mistral is capable of mathematical reasoning if you need to support your research with calculations. 

Benefits of running Mistral on Mac

There are multiple benefits of running Mistral locally on your Mac. They include:

• Customization: Mistral offers hundreds of pretrained and base models, which you can tune for a number of business tasks
• Privacy: since you run Mistral on Mac locally, you don’t have to share your data with a third party and can be sure about privacy and compliance with regulations
• Offline access: You can run Mistral offline, meaning that you don’t lose your results due to unstable internet connections
• Free downloads: Mistral code is open-source, so you don’t have to pay fees to download the models. 

Summing Up

Mistral is a highly performant and lightweight LLM that you can run on your Mac. Due to its enhanced architecture, the model is accessible to many medium and small businesses and individual users wishing to explore the AI world. 

Want to start your own Mistral project but lack a powerful Mac?

No worries! You can rent a Mac online to deploy LLMs and perform tasks like code generation, text analysis, translation, research, and more. Give it a try with our flexible rent-a-Mac plans.