maas-base/docs/tutorials/inference-with-tool-calling.md

Inference with Tool Calling

Tool calling allows you to connect models to external tools and systems. This is useful for many things such as empowering AI assistants with capabilities, or building deep integrations between your applications and the models.

In this tutorial, you’ll learn how to set up and use tool calling within MaaS-Base to extend your AI’s capabilities.

!!! note

1. Tool calling is supported in [vLLM](../user-guide/built-in-inference-backends.md#vllm) and [SGLang](../user-guide/built-in-inference-backends.md#sglang) inference backends.
2. Tool calling is essentially achieved through prompt engineering, requiring models to be trained with internalized templates to enable this capability. Therefore, not all LLMs support tool calling.

Prerequisites

Before proceeding, ensure the following:

• MaaS-Base is installed and running.
• A Linux worker node with a GPU is available. We'll use Qwen2.5-7B-Instruct as the model for this tutorial. The model requires a GPU with at least 18GB VRAM.
• Access to Hugging Face for downloading the model files.

Step 1: Deploy the Model

Deploy from Catalog

LLMs that support tool calling are marked with the tools capability in the catalog. When you select such a model from the catalog, tool calling is enabled by default.

Example of Custom Deployment Using llama-box

When you deploy GGUF models using llama-box, tool calling is enabled by default for models that support it.

1. Navigate to the Deployments page in the MaaS-Base UI and click the Deploy Model button. In the dropdown, select Hugging Face as the source for your model.
2. Enable the GGUF checkbox to filter models by GGUF format.
3. Use the search bar to find the Qwen/Qwen2.5-7B-Instruct-GGUF model.
4. Click the Save button to deploy the model.

Example of Custom Deployment Using vLLM

When you deploy models using vLLM, you need to enable tool calling with additional parameters.

1. Navigate to the Deployments page in the MaaS-Base UI and click the Deploy Model button. In the dropdown, select Hugging Face as the source for your model.
2. Use the search bar to find the Qwen/Qwen2.5-7B-Instruct model.
3. Expand the Advanced section in configurations and scroll down to the Backend Parameters section.
4. Click on the Add Parameter button and add the following parameters:

• --enable-auto-tool-choice
• --tool-call-parser=hermes

1. Click the Save button to deploy the model.

After deployment, you can monitor the model's status on the Deployments page.

Step 2: Generate an API Key

We will use the MaaS-Base API to interact with the model. To do this, you need to generate an API key:

1. Hover over the user avatar and navigate to the API Keys page.
2. Click the New API Key button.
3. Enter a name for the API key and click the Save button.
4. Copy the generated API key for later use.

Step 3: Do Inference

With the model deployed and an API key, you can call the model via the MaaS-Base API. Here is an example script using curl (replace <your-server-url> with your GPUStack server URL and <your-api-key> with the API key generated in the previous step):

export GPUSTACK_SERVER_URL=<your-server-url>
export GPUSTACK_API_KEY=<your-api-key>
curl $GPUSTACK_SERVER_URL/v1-openai/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $GPUSTACK_API_KEY" \
-d '{
  "model": "qwen2.5-7b-instruct",
  "messages": [
    {
      "role": "user",
      "content": "What'\''s the weather like in Boston today?"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            },
            "unit": {
              "type": "string",
              "enum": ["celsius", "fahrenheit"]
            }
          },
          "required": ["location"]
        }
      }
    }
  ],
  "tool_choice": "auto"
}'

Example response:

{
  "model": "qwen2.5-7b-instruct",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": null,
        "tool_calls": [
          {
            "id": "chatcmpl-tool-b99d32848b324eaea4bac5a5830d00b8",
            "type": "function",
            "function": {
              "name": "get_current_weather",
              "arguments": "{\"location\": \"Boston, MA\", \"unit\": \"fahrenheit\"}"
            }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ],
  "usage": {
    "prompt_tokens": 212,
    "total_tokens": 242,
    "completion_tokens": 30
  }
}