ubermorgen/qwen3-devops

1---2license: mit3base_model:4- Qwen/Qwen3-1.7B5pipeline_tag: text-generation6---7 8# Ollama DevOps Agent9 10A lightweight AI-powered DevOps automation tool using a fine-tuned Qwen3-1.7B model with Ollama and SmolAgents. **Specialized for Docker and Kubernetes workflows** with sequential tool execution and structured reasoning.11 12## Features13 14- **Sequential Tool Execution**: Calls ONE tool at a time, waits for results, then proceeds15- **Structured Reasoning**: Uses `<think>` and `<plan>` tags to show thought process16- **Validation-Aware**: Checks command outputs for errors before proceeding17- **Multi-Step Tasks**: Handles complex workflows requiring multiple tool calls18- **Approval Mode**: User confirmation before executing each tool call for enhanced safety (enabled by default)19- **Resource Efficient**: Optimized for local development (1GB GGUF model)20- **Fast**: Completes typical DevOps tasks in ~10 seconds21 22## What's Special About This Model?23 24This model is fine-tuned specifically for DevOps automation with improved reasoning capabilities:25 26- **Docker & Kubernetes Expert**: Trained on 300+ Docker and Kubernetes workflows (90% of training data)27- **One tool at a time**: Unlike base models that try to call all tools at once, this model executes sequentially28- **Explicit planning**: Shows reasoning with `<think>` and `<plan>` before acting29- **Uses actual values**: Extracts and uses real values from tool responses in subsequent calls30- **Error handling**: Validates each step and tries alternative approaches on failure31 32### Training Data Focus33 34The model has been trained on:35- **Docker workflows**: Building images, containers, Docker Compose, optimization36- **Kubernetes operations**: Pods, deployments, services, configurations37- **General DevOps**: File operations, system commands, basic troubleshooting38 39⚠️ **Note**: The model has limited training on cloud-specific CLIs (gcloud, AWS CLI, Azure CLI). For best results, use it for Docker and Kubernetes tasks.40 41### Example Output42 43```44Task: Get all pods in default namespace45 46Step 1: Execute kubectl command47<tool_call>48{"name": "bash", "arguments": {"command": "kubectl get pods -n default"}}49</tool_call>50 51[Receives pod list]52 53Step 2: Provide summary54<tool_call>55{"name": "final_answer", "arguments": {"answer": "Successfully retrieved 10 pods in default namespace..."}}56</tool_call>57```58 59 60## Quick Start61### 🎯 **Recommended: Native Installation**62 63For the best experience with full DevOps capabilities:64 65```bash66curl -fsSL https://raw.githubusercontent.com/ubermorgenland/devops-agent/main/install.sh | bash67```68 69This will automatically:70- Install Ollama (if not present)71- Install Python dependencies72- Download the model from Hugging Face73- Create the Ollama model74- Set up the `devops-agent` CLI command75 76**Why native installation?**77- ✅ **Full system access** - manage real infrastructure78- ✅ **No credential mounting** - works with your existing setup79- ✅ **Better performance** - no container overhead80- ✅ **Simpler usage** - just run `devops-agent`81 82---83