unsloth/gpt-oss-20b
The gpt-oss-20b model by OpenAI is a 21 billion parameter open-weight language model with 3.6 billion active parameters, designed for powerful reasoning and agentic tasks. It features a 32768 token context length and is optimized for lower latency and specialized use cases, capable of running within 16GB of memory due to native MXFP4 quantization. This model supports configurable reasoning effort, full chain-of-thought access, and is fine-tunable for specific applications.
Loading preview...
gpt-oss-20b: OpenAI's Open-Weight Model for Reasoning and Agentic Tasks
The gpt-oss-20b is a 21 billion parameter open-weight model from OpenAI, part of the gpt-oss series, specifically designed for lower latency and specialized use cases. It features 3.6 billion active parameters and a 32768 token context length. This model is trained with native MXFP4 precision for its Mixture-of-Experts (MoE) layer, allowing it to run efficiently within 16GB of memory.
Key Capabilities
- Permissive Apache 2.0 License: Enables broad experimentation, customization, and commercial deployment without copyleft restrictions.
- Configurable Reasoning Effort: Users can adjust reasoning levels (low, medium, high) to balance speed and detail for different tasks.
- Full Chain-of-Thought Access: Provides complete visibility into the model's reasoning process, aiding debugging and increasing trust.
- Fine-tunable: The model can be fully customized for specific use cases, even on consumer hardware.
- Agentic Capabilities: Includes native support for function calling, web browsing, Python code execution, and structured outputs.
- Harmony Response Format: Must be used with the harmony format for correct operation.
Good For
- Specialized Applications: Ideal for use cases requiring custom fine-tuning and specific task optimizations.
- Local Deployment: Suitable for running on consumer hardware or environments with memory constraints.
- Agentic Workflows: Excels in tasks involving tool use, such as web browsing and code execution.
- Debugging and Transparency: The full chain-of-thought access is beneficial for understanding and verifying model outputs.