Function calling allows models to interact with external tools and APIs in a structured way. This guide explains how to fine-tune models with function calling capabilities in Predibase.

​
Supported Models

Function calling fine-tuning is currently supported on the following models:
  • llama-3-2-1b-instruct
  • llama-3-2-3b-instruct
  • llama-3-3-70b-instruct
  • qwen2-5-1-5b-instruct
  • qwen2-5-7b-instruct
  • qwen2-5-14b-instruct
  • qwen2-5-32b-instruct
Support for additional models is coming soon.

​
Tool Schema

Your tools should follow the schema defined by Hugging Face. Here’s an example: 
[
  {
    "type": "function",
    "function": {
      "name": "multiply",
      "description": "A function that multiplies two numbers",
      "parameters": {
        "type": "object",
        "properties": {
          "a": {
            "type": "number",
            "description": "The first number to multiply"
          },
          "b": {
            "type": "number",
            "description": "The second number to multiply"
          }
        },
        "required": ["a", "b"]
      }
    }
  }
]

​
Chat Dataset Format

Use a chat-style dataset with a “tools” key at the same level as “messages”: 
{
  "messages": [
    {
      "role": "system",
      "content": "Predibot is a cool chatbot"
    },
    {
      "role": "user",
      "content": "What's 2 times 5"
    },
    {
      "role": "assistant",
      "tool_calls": [
        {
          "type": "function",
          "function": {
            "name": "multiply",
            "arguments": {
              "a": 2,
              "b": 5
            }
          }
        }
      ],
      "content": ""
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "multiply",
        "description": "A function that multiplies two numbers",
        "parameters": {
          "type": "object",
          "properties": {
            "a": {
              "type": "number",
              "description": "The first number to multiply"
            },
            "b": {
              "type": "number",
              "description": "The second number to multiply"
            }
          },
          "required": ["a", "b"]
        }
      }
    }
  ]
}

​
Required Configuration Parameters

When fine-tuning with function calling, you must enable apply_chat_template in your fine-tuning config. You can do this either through the SDK or by checking the box in the adapter version UI before training.

​
How Function Calling Works

Function calling involves an interactive flow between the user, assistant, and tools:
  1. System acknowledges tools may be used
  2. User provides prompt with tools
  3. Assistant makes appropriate tool calls
  4. User/system executes tool calls
  5. Tool returns results
  6. Assistant provides formatted response
Here’s a complete example of this interaction: 
{
  "messages": [
    {
      "role": "system",
      "content": "Predibot is a cool chatbot"
    },
    {
      "role": "user",
      "content": "I need help multiplying 2 numbers"
    },
    {
      "role": "assistant",
      "content": "I can help with that. What are the numbers?"
    },
    {
      "role": "user",
      "content": "What's 2 times 5"
    },
    {
      "role": "assistant",
      "tool_calls": [
        {
          "type": "function",
          "function": {
            "name": "multiply",
            "arguments": {
              "a": 2,
              "b": 5
            }
          }
        }
      ],
      "content": ""
    },
    {
      "role": "tool",
      "name": "multiply",
      "content": 10
    },
    {
      "role": "assistant",
      "content": "2 times 5 equals 10!"
    }
  ],
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "multiply",
        "description": "A function that multiplies two numbers",
        "parameters": {
          "type": "object",
          "properties": {
            "a": {
              "type": "number",
              "description": "The first number to multiply"
            },
            "b": {
              "type": "number",
              "description": "The second number to multiply"
            }
          },
          "required": ["a", "b"]
        }
      }
    }
  ]
}

​
Converting from ShareGPT Format

If your data is in ShareGPT format, you can use this Python script to convert it to Predibase’s format: 
import json

def convert_format(path_to_sharegpt_json: str):
    # Load the JSON into a Python dictionary
    with open(path_to_sharegpt_json, "r") as f:
        data = json.load(f)

    # Initialize the input structure
    input_data = {
        "messages": [],
        "tools": json.loads(data["tools"])
    }

    # Set the system message
    input_data["messages"].append({
        "role": "system",
        "content": data["system"]
    })

    # Process the conversations
    for conversation in data["conversations"]:
        if conversation["from"] == "user":
            input_data["messages"].append({
                "role": "user",
                "content": conversation["value"]
            })
        elif conversation["from"] == "assistant":
            input_data["messages"].append({
                "role": "assistant",
                "content": conversation["value"]
            })
        elif conversation["from"] == "function_call":
            function_call = json.loads(conversation["value"])
            input_data["messages"].append({
                "role": "assistant",
                "tool_calls": [{
                    "type": "function",
                    "function": {
                        "name": function_call["name"],
                        "arguments": function_call["arguments"]
                    }
                }],
                "content": ""
            })
        elif conversation["from"] == "observation":
            observation = json.loads(conversation["name"])
            input_data["messages"].append({
                "role": "tool",
                "name": function_call["name"],  # Use the actual tool name from the function call
                "content": observation["helper"]
            })

    return json.dumps(input_data, indent=4)
Example ShareGPT format: 
{
  "conversations": [
    {
      "from": "user",
      "value": "I need help multiplying 2 numbers"
    },
    {
      "from": "assistant",
      "value": "I can help with that. What are the numbers?"
    },
    {
      "from": "user",
      "value": "What's 2 times 5"
    },
    {
      "from": "function_call",
      "value": "{\"arguments\": {\"a\": \"2\", \"b\": \"5\"}, \"name\": \"multiply\"}"
    },
    {
      "from": "observation",
      "name": "{\"result\":\"success\",\"helper\":\"10\"}",
      "value": 10
    },
    {
      "from": "assistant",
      "value": "2 times 5 equals 10!"
    }
  ],
  "system": "Predibot is a cool chatbot",
  "tools": "[{\"name\": \"multiply\", \"description\": \"Use this function to multiply two numbers together\", \"parameters\": {\"type\": \"object\", \"properties\": {\"a\": {\"type\": \"number\", \"description\": \"The first number to multiply\"}, \"b\": {\"type\": \"string\", \"description\": \"The second number to multiply\"}}, \"required\": [\"a\", \"b\"]}}]"
}

​
Next Steps