What is Open Source AI?
Open source AI (open source artificial intelligence in Turkish) are models whose weights and codes can be examined and run by anyone.
Definition and Word Origin
Unlike closed models, these models are transparent: Anyone who wants can download them, examine them with their own data, and make changes to them. Llama, Mistral and DeepSeek are known examples. It is arguable that training data should also be open; OSI conducts a separate definition study on this subject.
How to Know and Use in Daily Life?
Local chat: Personal assistant that works without internet.
Research: Base model tested on.
Institutional: In-house solution without moving data out.
Technical Depth and Architecture
Components:
Weights: Trained model files are distributed through Hugging Face.
Licence: Apache and MIT are considered permissive. Some community licenses limit commercial use, you'll need to read the text.
Quantization: The reduced version of the model (GGUF) runs on low memory.
Operating: Tools like Ollama open models with a single command:
ollama run llama3Hardware rule: As the parameter grows, it requires memory. Smaller models run on the laptop, larger ones run on the server.
Frequently Mixed Things
Can be mixed with Open Weights. Open Weights is just the weights being open. Open source AI, on the other hand, includes code and process transparency, its scope is broader.
Use in Different Disciplines
Specification: Recipe shared with ingredients and measurements.
Textbook: Open source that anyone can read and edit.
Seed bank: Ancestor seed shared by farmers.
Questions and answers
Are open source models weaker?
It used to be, but today many open models compete with their closed rivals. Closed models are ahead in the race for the top, but in practical matters the gap has closed.
Why should I use open source?
For data privacy, cost and full integration. Your data does not go out and you do not pay a license fee.
Is commercial use allowed?
It varies depending on the license. Apache and MIT are free, some community licenses impose user number or revenue limits.
Which one to start with?
Start locally with small, quantized models. If the need grows, you move it to the server.
Related terms
This explanation was written in plain language for TreScout with AI and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →