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Clarify port and api key assignments
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@@ -138,7 +138,7 @@ go build -o llamactl ./cmd/server
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1. Open http://localhost:8080
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1. Open http://localhost:8080
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2. Click "Create Instance"
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2. Click "Create Instance"
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3. Choose backend type (llama.cpp, MLX, or vLLM)
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3. Choose backend type (llama.cpp, MLX, or vLLM)
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4. Configure your model and options
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4. Configure your model and options (ports and API keys are auto-assigned)
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5. Start the instance and use it with any OpenAI-compatible client
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5. Start the instance and use it with any OpenAI-compatible client
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## Configuration
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## Configuration
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@@ -59,6 +59,10 @@ Each instance is displayed as a card showing:
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- **llama.cpp**: Threads, context size, GPU layers, port, etc.
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- **llama.cpp**: Threads, context size, GPU layers, port, etc.
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- **MLX**: Temperature, top-p, adapter path, Python environment, etc.
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- **MLX**: Temperature, top-p, adapter path, Python environment, etc.
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- **vLLM**: Tensor parallel size, GPU memory utilization, quantization, etc.
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- **vLLM**: Tensor parallel size, GPU memory utilization, quantization, etc.
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!!! tip "Auto-Assignment"
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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.
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8. Click **"Create"** to save the instance
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8. Click **"Create"** to save the instance
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**Via API**
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**Via API**
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@@ -33,6 +33,9 @@ You should see the Llamactl web interface.
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- **Model**: Model path or huggingface repo
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- **Model**: Model path or huggingface repo
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- **Additional Options**: Backend-specific parameters
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- **Additional Options**: Backend-specific parameters
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!!! tip "Auto-Assignment"
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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.
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3. Click "Create Instance"
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3. Click "Create Instance"
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## Start Your Instance
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## Start Your Instance
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