Open-weight LLMs vs Open-source LLMs

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We often use these 2 terms interchangeably, but they are not the same.

Let us understand the difference in simple language:

Open-weight LLMs vs Open-source LLMs

Open-Weight LLMs

What it means: The model weights are released to the public.

You can:

– Use the model as-is.

– Fine-tune it.

– Run it on your own hardware.

You cannot always:

– See the source code for training.

– Understand how it was trained (data, methods, code might be private).

Example: LLaMA 3, Mistral, Falcon – weights are released but not necessarily under fully permissive licenses.

Open-Source LLMs

What it means: Everything is open – the model weights, the training code, data, and often the training logs and hyperparameters.

You can:

– Modify and retrain the model.

– Use it for any purpose (if the license allows).

– Learn from the full pipeline.

Example: GPT-J, Phi-2, TinyLLaMA, Pythia, some or all parts of the training stack are truly open.

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