DIY Open Source Auto-Gen Multi-LLM AI Adventure

My DIY Multi-Model AI Adventure

 Well, butter my biscuits – I just had the wildest AI adventure! As someone keen to tap into advanced language models, I discovered a way to set up my own fleet locally using Light and Autocode... 

 Salivating Over Sophisticated AI You know how all the trendy tech heads are clamoring to use those giant language models like Codex and GPT-4? I was drooling to join that club. But paying for access to those cloud APIs can drain your wallet faster than my Uncle Rick at a Vegas buffet! 

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Discovering Light to Run Models Locally So I started Googling cheaper options and stumbled upon this handy tool called Light. 

Turns out Light lets you: 

- Grab sizzlin’ hot models like 

Code Llamaa and Mistral

 - Run them right on your own machine! 

No expensive cloud subscriptions are needed. 

Spinning Up Multiple Models

 A few clicks and terminal commands later, and BOOM

 – I’ve got two advanced AI systems chilling on my humble laptop hard drive, ready to party! 

First, you'll need to download something called "Light." Think of Light as a kind of app store for AI systems. It lets you browse different models like Codex and GPT-4 then install them locally. Light essentially provides: 

- Local web server for each model

 - Browser-based access to poke at their brains Felt like a kid set loose in the AI model candy store! 

No fancy coding skills are required - just follow these simple steps!

Leveraging Multiple LLM Models Locally

The Light server provides a way to run large language models (LLMs) like Codex and GPT-3 on a local machine. It exposes an API that allows seamlessly integrating multiple models into a single application.

To set up a local LLM environment:

1. Install Light

2. Download desired models:

light lm -m model.olama.code:v2.1.0 -d code-llama 
light lm -m model.olama.mistral.code:2.7.0 -d mistral

This spins up local endpoints for each model.

3. In a Python script, import Autocode and define configuration dictionaries pointing to the endpoints:

mistral_config = {
  "base_url": "http://127.0.0.1:7861",
  "api_key": "no"  
}

code_llama_config = {
  # Details for code llama endpoint
}

4. Create Autocode agents and pass the configs so they leverage the desired models:

user = ac.User(mistral_config)  
assistant = ac.Assistant(code_llama_config)

conversation = ac.GroupChat([user, assistant])  

This approach was tested by having one agent write a script and another execute it, showing flexible orchestration.

In summary, key benefits of this local LLM setup:

• Light enables spinning up multiple models on one machine
• Autocode provides configurable agents to leverage different models
• System allows creating multi-agent applications with diverse capabilities

Once Light is set up, find the models you want under its "Models" tab. You can search by name or capability. I'd recommend starting with something pre-trained like "Code Llama" - it comes ready to generate code!

Click to add your selected models to Light. This will download them onto your computer as if you were installing any normal software. Now comes the fun part...

Open up Light's terminal and type:

<light lm -m name-of-model>

Do this for each AI system you downloaded. Light will spin them up on your computer and give you a unique web address that lets you access them.

Think of each address like an AI assistant you can command. To try it out, copy the URLs into your web browser. The models will greet you and ask what they can help with!

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Making Models Collaborate with Autocode 

But the real magic happened when I wanted my new robot buddies to collaborate...

 For that, I turned to the legendary software 

Autocode– think of it like a virtual workspace where AI agents can ping-pong messages and combine forces on projects. 

 Sitting Back as Models Create Together 

Import Autocode into a Python script, plug in your model URLs, and watch them have their own discussions! How cool is that? You now have multiple AI experts chilling on your computer ready for your next experiment

Well let me tell you, it was glorious watching Mistral type up a basic Python script, then Code Llama picking it up to execute line-by-line! They got a little sassy on output formats, but hey, that's coders for ya. 

Prima donnas, the lot of 'em! All joking aside though, super slick seeing them hand off tasks and build on each other's work. My inner AI engineer was grinning ear-to-ear watching these man-made minds create together! 

Takeaways: 

Low-Cost Multi-Agent AI Potential If looking to tap language models, I'd definitely recommend the Light & Autocode combo. 

No need for expensive cloud platform subscriptions: 

- Build your own multi-agent AI squad completely on a personal rig 

- Let the models collaborate on projects in an automated way Pretty snazzy stuff! L

et me know if you end up trying it yourself.

Thank you for taking time from your schedule to support our Content by being with us here today. 

Friends, let me tell you a little story. I recently came across a message that troubled me. It seemed to promote division and intolerance. My first instinct was to lash out in anger - to judge the creators harshly.

But then I paused. I realized that meeting negativity with more negativity rarely solves problems. It often only breeds more misunderstanding.

So I've been thinking...how can we move forward in a spirit of openness, empathy and goodwill? What words and deeds can we put into the world to bring people together?

I don't have all the answers today. But I know progress starts with an open heart. With a willingness to understand each other, even when it's difficult. And with the courage to build bridges where there are divides.

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So that's what I aim to do - little by little, day by day. And I hope you'll join me. There is light ahead, my friends. If we seek it out together. 

Seth West 

Owner and Founder

 S.D.C. 

(Stormfront Digital Connection) 

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