Clarify port and api key assignments

README.md

@@ -138,7 +138,7 @@ go build -o llamactl ./cmd/server
 1. Open http://localhost:8080
 2. Click "Create Instance"
 3. Choose backend type (llama.cpp, MLX, or vLLM)
-4. Configure your model and options
+4. Configure your model and options (ports and API keys are auto-assigned)
 5. Start the instance and use it with any OpenAI-compatible client
 
 ## Configuration

docs/managing-instances.md

@@ -59,6 +59,10 @@ Each instance is displayed as a card showing:
     - **llama.cpp**: Threads, context size, GPU layers, port, etc.
     - **MLX**: Temperature, top-p, adapter path, Python environment, etc.
     - **vLLM**: Tensor parallel size, GPU memory utilization, quantization, etc.
+
+!!! tip "Auto-Assignment"
+    Llamactl automatically assigns ports from the configured port range (default: 8000-9000) and generates API keys if authentication is enabled. You typically don't need to manually specify these values.
+
 8. Click **"Create"** to save the instance  
 
 **Via API**

docs/quick-start.md

@@ -33,6 +33,9 @@ You should see the Llamactl web interface.
    - **Model**: Model path or huggingface repo
    - **Additional Options**: Backend-specific parameters
 
+!!! tip "Auto-Assignment"
+    Llamactl automatically assigns ports from the configured port range (default: 8000-9000) and generates API keys if authentication is enabled. You typically don't need to manually specify these values.
+
 3. Click "Create Instance"
 
 ## Start Your Instance