MananSuri27/gemma4-31b-full-mini-swe-secbench
MananSuri27/gemma4-31b-full-mini-swe-secbench is a 31 billion parameter Gemma-4 based causal language model, fine-tuned by MananSuri27. This model specializes in security-related tasks, having been trained on Mini-SWE-Agent-style security benchmark trajectories. Its primary differentiation lies in its optimization for assistant action and tool-call turns within agent trajectories, making it suitable for automated security analysis and response.
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Overview
MananSuri27/gemma4-31b-full-mini-swe-secbench is a 31 billion parameter model based on the google/gemma-4-31B-it architecture. This model has undergone a full fine-tuning process specifically on Mini-SWE-Agent-style security benchmark trajectories.
Key Capabilities
- Security-focused Fine-tuning: The model is specialized for tasks related to security benchmarks, leveraging agent trajectories.
- Agent Interaction: It is trained on assistant action and tool-call turns, indicating its proficiency in understanding and generating responses within an agentic workflow.
- Gemma-4 Base: Built upon the robust Gemma-4-31B-it foundation, providing strong general language understanding before specialized fine-tuning.
Use Cases
This model is particularly well-suited for applications requiring an understanding of security-related scenarios and agent-based interactions. Developers can integrate it into systems that need to process or generate responses based on security benchmarks or agent actions. Its training on specific trajectories suggests utility in automated security analysis, vulnerability assessment, or security incident response systems where agent-like decision-making and tool interaction are crucial.