AI Techniques

Open-Weight vs. Open-Source Models

An open-weight model publishes its trained parameters so anyone can run or fine-tune it (e.g., Llama, Mistral, Qwen, DeepSeek), but the training data and recipe may be undisclosed. A truly open-source model additionally releases the training data, code, and documentation. The distinction matters for reproducibility, audit, and regulatory compliance.

Related terms

Foundation Models
Large AI models trained on broad data that can be adapted to many downstream tasks. These models serve as a foundation for building specialized applications through fine-tuning or prompting.
Fine-tuning
Continuing to train a pretrained model on a task-specific dataset to change its behavior, style, or domain knowledge. Techniques range from full fine-tuning (all weights) to parameter-efficient methods like LoRA and QLoRA that update only small adapter layers.

Also referenced: Large Language Model.

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RAG vs Fine-tuning vs Prompt Engineering: Decision Framework

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Where this fits

Open-Weight vs. Open-Source Models is part of the AI Techniques vocabulary used in the Generative AI Maturity Framework. See the full glossary for the complete set of 149 defined terms, or take the free maturity assessment to see where your organisation stands.