Sprinter-Spb/gpt-oss-20b
Sprinter-Spb/gpt-oss-20b is a 20 billion parameter open-weight language model developed by OpenAI, designed for powerful reasoning and agentic tasks. It features configurable reasoning effort, full chain-of-thought access, and native capabilities for function calling, web browsing, and Python code execution. This model is optimized for lower latency and specialized use cases, running efficiently within 16GB of memory due to MXFP4 quantization.
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Overview
Sprinter-Spb/gpt-oss-20b is a 20 billion parameter open-weight model from OpenAI's gpt-oss series, designed for robust reasoning and agentic tasks. It is a smaller counterpart to the 120 billion parameter gpt-oss-120b, optimized for lower latency and local or specialized use cases, capable of running within 16GB of memory. The model is released under a permissive Apache 2.0 license, allowing for broad experimentation, customization, and commercial deployment.
Key Capabilities
- Configurable Reasoning Effort: Users can adjust the reasoning effort (low, medium, high) to balance speed and detail based on task requirements.
- Full Chain-of-Thought: Provides complete access to the model's internal reasoning process, aiding in debugging and increasing trust in outputs.
- Agentic Features: Includes native support for function calling with defined schemas, web browsing, and Python code execution.
- Fine-tunable: The model can be fine-tuned for specific use cases, even on consumer hardware.
- Memory Efficiency: Post-trained with MXFP4 quantization, enabling it to run efficiently within 16GB of memory.
Good For
- Developers seeking a powerful, open-weight model for reasoning and agentic tasks.
- Applications requiring configurable reasoning levels and transparent chain-of-thought.
- Use cases benefiting from native tool use capabilities like web browsing and code execution.
- Deployment on consumer hardware or environments with memory constraints, due to its optimized size and quantization.