Open Weight Model

An open weight model is an AI model where the trained parameters (weights) are publicly available for anyone to download, run, and often fine-tune, but the full training pipeline - including training data, preprocessing code, and training infrastructure details - is not disclosed. This distinguishes open weight models from fully open source AI, where all components needed to reproduce the model from scratch are provided.

Most major "open" model releases fall into the open weight category rather than true open source. Meta's Llama series, Mistral's models, Google's Gemma, and DeepSeek's releases all provide downloadable weights, often with custom licenses that impose usage restrictions such as user count limits or prohibited use cases. Users can run these models locally, fine-tune them for specific tasks, and deploy them in production, but they cannot fully reproduce or understand the training process because the data and complete methodology are withheld.

The distinction matters for several reasons. Open weight models give developers practical access to powerful AI without API costs or data privacy concerns, but they do not provide the transparency and reproducibility that scientific research demands. The training data, which heavily influences model behavior, biases, and capabilities, remains a black box. Despite this limitation, open weight models have dramatically expanded access to AI capabilities, enabling local deployment through tools like Ollama and llama.cpp and driving competition that pushes the entire field forward.