Recently, I've been hearing a lot about Mistral AI and the technology behind it, which has left me a bit confused. How do large language models (LLMs) generally work? What principles are these models built on? What are the key differences between their open-source and closed-source versions?
What is Mistral AI and how does it work?
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Large language models (LLMs) are basically massive artificial neural networks trained on insane amounts of text data, bro. Their whole deal is learning word relationships and patterns from the data to spit out the statistically most likely response. Open-source versions (like Mistral 7B) let you see and tweak the code and models, but closed ones (GPT-3/4 and the like) only give you API access—no peeking at the internals, the company keeps that stuff locked down.
Don't worry, buddy, it's actually pretty simple. Large language models (LLMs) basically "learn" from massive amounts of text data. It's not like mindless memorization; there's an AI logic that picks up sentence structures, contexts, and even a sense of humor. For example, Mistral AI operates on this principle too: **"pre-trained language models."** So, in the first stage, the model calculates the relationships between words (like the probability that "cat" follows "meow") using a dataset of billions of words scraped from the internet. During this training, a mathematical model is created with millions (or even billions) of parameters—essentially numbers.
As for the difference between open-source and closed versions, I’d say the biggest thing is **access and control**. For instance, Mistral’s 7B parameter model (Mistral 7B) is released as open source, meaning you can download it and run it on your own server however you like. But with closed models (like some commercial APIs), you only get cloud-based access—you can’t touch the code, modify it, and it can get expensive. The beauty of open source is that you can optimize the model for your needs, fine-tune it with your own data, and customize it. When I work with open-source models, I do simple things like running the model locally to eliminate latency or retraining it for data-specific scenarios.