---
license: mit
base_model:
- Qwen/Qwen3-1.7B
pipeline_tag: text-generation
---

# Ollama DevOps Agent

A 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.

## Features

- **Sequential Tool Execution**: Calls ONE tool at a time, waits for results, then proceeds
- **Structured Reasoning**: Uses `<think>` and `<plan>` tags to show thought process
- **Validation-Aware**: Checks command outputs for errors before proceeding
- **Multi-Step Tasks**: Handles complex workflows requiring multiple tool calls
- **Approval Mode**: User confirmation before executing each tool call for enhanced safety (enabled by default)
- **Resource Efficient**: Optimized for local development (1GB GGUF model)
- **Fast**: Completes typical DevOps tasks in ~10 seconds

## What's Special About This Model?

This model is fine-tuned specifically for DevOps automation with improved reasoning capabilities:

- **Docker & Kubernetes Expert**: Trained on 300+ Docker and Kubernetes workflows (90% of training data)
- **One tool at a time**: Unlike base models that try to call all tools at once, this model executes sequentially
- **Explicit planning**: Shows reasoning with `<think>` and `<plan>` before acting
- **Uses actual values**: Extracts and uses real values from tool responses in subsequent calls
- **Error handling**: Validates each step and tries alternative approaches on failure

### Training Data Focus

The model has been trained on:
- **Docker workflows**: Building images, containers, Docker Compose, optimization
- **Kubernetes operations**: Pods, deployments, services, configurations
- **General DevOps**: File operations, system commands, basic troubleshooting

⚠️ **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.

### Example Output

```
Task: Get all pods in default namespace

Step 1: Execute kubectl command
<tool_call>
{"name": "bash", "arguments": {"command": "kubectl get pods -n default"}}
</tool_call>

[Receives pod list]

Step 2: Provide summary
<tool_call>
{"name": "final_answer", "arguments": {"answer": "Successfully retrieved 10 pods in default namespace..."}}
</tool_call>
```


## Quick Start
### 🎯 **Recommended: Native Installation**

For the best experience with full DevOps capabilities:

```bash
curl -fsSL https://raw.githubusercontent.com/ubermorgenland/devops-agent/main/install.sh | bash
```

This will automatically:
- Install Ollama (if not present)
- Install Python dependencies
- Download the model from Hugging Face
- Create the Ollama model
- Set up the `devops-agent` CLI command

**Why native installation?**
- ✅ **Full system access** - manage real infrastructure
- ✅ **No credential mounting** - works with your existing setup
- ✅ **Better performance** - no container overhead
- ✅ **Simpler usage** - just run `devops-agent`

---