| --- |
| 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` |
|
|
| --- |
|
|
