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open-weight model

A machine learning model whose weights (learned parameters) are publicly accessible, enabling local deployment, auditing, and customization without proprietary restrictions. Differs from closed-weight models (e.g., most commercial LLMs) where weights are withheld.

Key characteristics:

• Weights available for download (e.g., via public repositories)
• May not include full training code/data (only weights)
• Enables offline use, customization, and transparency
• Often distributed under permissive licenses

Recent examples:

• OpenAI’s [[concepts/gpt-oss-20b|gpt-oss-20b]] and [[concepts/gpt-4|gpt-oss]]-120b variants (as detailed in Jeredblu running LLM locally)
• Upstage’s Solar Open 2, an open-weight LLM highlighted for agentic capabilities in office productivity and coding tasks Solar Open 2 LLM: Agentic Capabilities for Productivity and Coding Demonstrations
• Allows local inference without API dependency

References:

• Solar Open 2 LLM: Agentic Capabilities for Productivity and Coding Demonstrations