File size: 3,054 Bytes
d0be352 8bcbb00 5691320 8bcbb00 5691320 8bcbb00 5691320 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | ---
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`
---
|