yuvytung/gpt-oss-20b-slim

TEXT GENERATIONConcurrent Unit Cost:1Model Size:20BQuant:FP8Context Size:32kPublished:Nov 11, 2025License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

gpt-oss-20b-slim is a 21 billion parameter open-weight model from OpenAI, part of the gpt-oss series, designed for powerful reasoning and agentic tasks. It features configurable reasoning effort (low, medium, high) and full chain-of-thought access, making it suitable for debugging and complex problem-solving. Optimized for lower latency and local deployment, this model supports function calling, web browsing, and Python code execution, running efficiently within 16GB of memory due to MXFP4 quantization.

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gpt-oss-20b-slim: OpenAI's Open-Weight Model for Reasoning and Agentic Tasks

yuvytung/gpt-oss-20b-slim is a 21 billion parameter model from OpenAI's gpt-oss series, specifically designed for lower latency and specialized use cases. It operates efficiently within 16GB of memory, thanks to MXFP4 quantization of its Mixture-of-Experts (MoE) weights. This 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 level (low, medium, high) to balance speed and analytical depth for specific tasks.
  • Full Chain-of-Thought Access: Provides complete visibility into the model's reasoning process, aiding in debugging and increasing trust in outputs.
  • Agentic Functionality: Natively supports function calling, web browsing, and Python code execution, enabling complex agentic workflows.
  • Fine-tunable: The model can be fine-tuned on consumer hardware to adapt to specific use cases.
  • Harmony Response Format: Designed to be used with OpenAI's harmony response format for correct operation.

Good For

  • Local and Specialized Deployments: Its memory efficiency makes it ideal for running on consumer hardware or in environments with limited resources.
  • Debugging and Trust: Developers who need to understand and debug the model's decision-making process.
  • Agentic Applications: Building applications that require tool use, such as web browsing, function calling, or code execution.
  • Customization: Fine-tuning for domain-specific tasks or unique application requirements.

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