updated readme

README.md

@@ -5,10 +5,78 @@ base_model:
 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.
+# Ollama DevOps Agent
 
-Full outline can be found here: https://github.com/ubermorgenland/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.
 
-IMPORTANT: It is designed to work with specific client - requires that client installation.
+## 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`
+
+---