In an age where AI is increasingly centralized, I wanted to reclaim a bit of autonomy. So I set out to build my own local AI platform—one that’s fast, private, and fully under my control. If you’ve been curious about running large language models on your own machine, this guide is for you.

Here’s how I set up a local AI assistant using LM Studio and the Mistral model family.

1. Downloading LM Studio

The first step was choosing the right interface. I went with LM Studio—a sleek, cross-platform desktop app that makes running local models surprisingly intuitive.

I downloaded the latest version for my OS (macOS, Windows, or Linux).
Installation was straightforward—just a few clicks and I was in.

LM Studio handles model loading, prompt formatting, and even chat history. It’s the perfect launchpad for local experimentation.

2. Downloading the Right Mistral Model

Next, I needed a model. I opted for Mistral 7B, a powerful open-weight model that balances performance and resource efficiency.

Within LM Studio, I navigated to the “Models” tab and searched for “Mistral.”
I selected the instruct-tuned variant (ideal for chat and Q&A tasks).
Downloaded the GGUF version compatible with my system (e.g., q4_K_M for lower RAM usage).

Tip: If you’re unsure which quantization to choose, LM Studio provides helpful guidance based on your hardware.

3. Adding the Model

Once downloaded, LM Studio automatically detected the model and added it to my local library.

I verified the model path and ensured it was correctly indexed.
If needed, you can manually add models by pointing LM Studio to the GGUF file.

This step was seamless—no terminal commands or config files required.

4. Setting Up the Prompt Template

Prompt templates define how user input is wrapped before being sent to the model. For Mistral, I used the following format:

<s>[INST] {{ prompt }} [/INST]

In LM Studio, I opened the “Prompt Template” section.
I replaced the default with the Mistral-compatible format.
This ensures the model interprets instructions correctly and responds in a conversational tone.

5. Configuring the System Template

The system template sets the tone and behavior of the model—like a personality primer.

Here’s what I used:

You are a helpful, articulate assistant. Respond concisely and clearly. Avoid repetition. If you don’t know something, say so.

I pasted this into the “System Prompt” field.
This helped steer the model toward informative, grounded responses.

You can tweak this to suit your use case—whether you want a creative writing partner or a technical assistant.

6. Up & Running

With everything in place, I hit “Start Chat”—and just like that, I had a fully functional local AI assistant.

LM Studio + Local Mistral = Unlimited Use
No internet required.
No data sent to the cloud.
Just me and my model, working side by side.
No token limits
No rate caps
No API keys
No usage tracking

You can run it as long as your system has power and RAM. It’s your private, offline, unrestricted AI lab.

7. Final Thoughts

Setting up a local AI platform isn’t just about privacy or performance—it’s about empowerment. You don’t need a data center or a PhD to run cutting-edge models. With tools like LM Studio and Mistral, the future of AI is personal, portable, and profoundly accessible.

If you’re curious about going deeper—fine-tuning models, chaining tools, or building your own AI workflows—let’s connect. I’m always up for a good conversation about the future we’re building.

~Mohan Krishnamurthy

www.leomohan.net

#Copilot #LMStudio #Mistral #LocalAIToolKit - in collaboration with Copilot.

Setting Up Your Own AI Platform: A Step-by-Step Guide