Definition

Open-weight models are large AI systems whose learned parameters (weights) are made available to the public. This means anyone can download the model, run it locally, fine-tune it, or build custom applications. They’re different from open-source code, which shares how the model works—but not always the trained weights.

Example

“Mistral and LLaMA are open-weight models, meaning you can download them and train your own version.”

How It’s Used in AI

Open-weight models are used in private deployments, cost-efficient inference, edge computing, and academic research. They enable fine-tuning with methods like LoRA, and are central to the growth of open-source AI communities.

Brief History

The release of Meta’s LLaMA sparked massive interest in open-weight alternatives to closed models like GPT-4. Since then, models like Falcon, Mistral, and Gemma have fueled a growing open ecosystem.

Key Tools or Models

• LLaMA, Mistral, Gemma, Falcon, Mixtral

• Libraries: Hugging Face Transformers, PEFT

• Used with vector databases, RAG, and private LLM hosting

Pro Tip

Unlike closed models, open-weight models can be deployed privately—ideal for businesses with data privacy or control needs.