Naseer-010/Qwen3-8B-Finetuned-DIME

Model Overview

This model is a Qwen3-8B checkpoint fine-tuned using Group Relative Policy Optimization (GRPO) on the DIME (Distributed Infrastructure Management Environment) benchmark. Its core function is to operate as an autonomous Site-Reliability Engineer (SRE) within a simulated 8-node Kubernetes cluster, interpreting per-node telemetry (CPU, memory, queue depths, tail-latency) and issuing kubectl commands to ensure cluster stability. The fine-tuning process involved a completely redesigned, differentiable seven-component reward signal to overcome gradient-blocking issues encountered with the original reward function.

Key Capabilities

• Autonomous SRE Functionality: Observes cluster telemetry and generates kubectl commands to manage an 8-node Kubernetes cluster.
• Enhanced Performance: Achieves a +44.2% relative improvement over the zero-shot Qwen3-8B on the 14-task DIME benchmark, demonstrating superior handling of various failure scenarios like node failures, memory leaks, and traffic spikes.
• Robust Reward Engineering: Utilizes a sophisticated, bounded seven-component reward signal ($R_{env}$) for effective learning, ensuring non-zero gradient flow during training.
• Kubernetes Interaction: Outputs syntactically correct kubectl commands, guided by an internal triage tree logic.

When to Use This Model

• Simulated SRE Tasks: Ideal for research and development in autonomous system management, particularly for Kubernetes environments.
• Reinforcement Learning Research: Useful for studying reward engineering, policy optimization, and agent behavior in complex, partially observable environments.
• Benchmarking: Can serve as a strong baseline or comparison point for other models aiming to solve infrastructure management challenges.