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---
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
pretty_name: OpenAI Function Calling Format (5K)
tags:
- function-calling
- tool-use
- openai
- chat
- messages
- synthetic
- instruction-tuning
- tool_calls
configs:
- config_name: default
  data_files:
  - split: train
    path: openai-function-calling-5k.jsonl
---

# OpenAI Function Calling Format (5K)

Synthetic function-calling conversations in the **OpenAI messages format** (`tool_calls` / `tool` role).

## Why This Dataset

Compatible with GPT-4, Mistral, Llama-3.1, Qwen2.5, and any model trained on the OpenAI chat format. Most existing function-calling datasets use abstract schemas — this uses the exact wire format models see in production.

## Dataset Description

**5,000 conversations** across 10 tool types:

| Tool | Single-turn | Multi-turn |
|---|---|---|
| `get_weather` | ✓ | — |
| `get_stock_price` | ✓ | ✓ |
| `search_web` | ✓ | ✓ |
| `execute_python` | ✓ | ✓ |
| `query_database` | ✓ | — |
| `send_email` | ✓ | ✓ |
| `create_calendar_event` | ✓ | — |
| `translate_text` | ✓ | — |
| `read_file` | ✓ | ✓ |
| `create_chart` | ✓ | — |

- **~80% single-turn** (one tool call → result → response)
- **~20% multi-turn** (sequential tool calls, e.g. search then email)

## Format

Exact OpenAI messages format:

```json
{
  "messages": [
    {"role": "system", "content": "You are a helpful AI assistant..."},
    {"role": "user", "content": "What's the weather in Tokyo?"},
    {
      "role": "assistant",
      "content": null,
      "tool_calls": [{
        "id": "call_abc123",
        "type": "function",
        "function": {"name": "get_weather", "arguments": "{"location": "Tokyo, Japan"}"}
      }]
    },
    {"role": "tool", "content": "{"temperature": 28, ...}", "tool_call_id": "call_abc123"},
    {"role": "assistant", "content": "Current weather in Tokyo: 28°C..."}
  ],
  "tools": [{"type": "function", "function": {...}}],
  "metadata": {"type": "single_turn", "tool": "get_weather"},
  "id": "abc123"
}
```

## Use Case

- Fine-tune LLMs for function calling (SFT on chosen response)
- Train tool-selection and argument generation
- Evaluate function-calling capability
- Drop-in compatible with OpenAI fine-tuning API format (with minor adaptation)

## License

Apache 2.0