Mustafaege/qwen3.5-toolcalling-v1

Qwen3.5 Tool Calling Dataset v1

A tool-calling SFT dataset built from smirki/Tool-Calling-Dataset-UIGEN-X (a cleaned version of interstellarninja/hermes_reasoning_tool_use), converted from ShareGPT conversations format to Qwen3 messages format. Features deep reasoning chains with <think> tags followed by structured tool calls.

Dataset Summary

Property	Value
Total Samples	51,004
Train Split	45,904
Test Split	5,100
Source	smirki/Tool-Calling-Dataset-UIGEN-X
Format	Qwen3 messages
Language	English
License	Apache 2.0

What is Tool Calling?

Tool calling extends function calling with explicit reasoning traces — the model thinks through which tool to use and why before making the call. Key characteristics:

• <think>...</think> blocks for chain-of-thought reasoning
• Structured JSON tool call outputs
• Multi-turn conversations with tool responses

Dataset Structure

Data Fields

Field	Type	Description
messages	list[dict]	Conversation turns with role and content

Role Types

Role	Description
system	Tool definitions embedded in system prompt
user	User query or tool execution result
assistant	<think> reasoning block + JSON tool call or final answer

Format

Multi-Turn Tool Call with Reasoning

{
  "messages": [
    {
      "role": "system",
      "content": "You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes...\n\nYou are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags:\n<tools>\n{\"type\": \"function\", \"function\": {\"name\": \"get_weather\", \"description\": \"Get current weather\", \"parameters\": {\"type\": \"object\", \"properties\": {\"location\": {\"type\": \"string\"}}, \"required\": [\"location\"]}}}\n</tools>"
    },
    {
      "role": "user",
      "content": "What is the weather like in Istanbul right now?"
    },
    {
      "role": "assistant",
      "content": "<think>\nThe user wants current weather for Istanbul. I should use the get_weather function with location='Istanbul'.\n</think>\n<tool_call>\n{\"name\": \"get_weather\", \"arguments\": {\"location\": \"Istanbul\"}}\n</tool_call>"
    },
    {
      "role": "user",
      "content": "<tool_response>\n{\"temperature\": 18, \"condition\": \"partly cloudy\", \"humidity\": 65}\n</tool_response>"
    },
    {
      "role": "assistant",
      "content": "The weather in Istanbul is currently 18°C and partly cloudy with 65% humidity."
    }
  ]
}

Source Conversion

Source	Original Format	Conversion
smirki/Tool-Calling-Dataset-UIGEN-X	conversations[from/value] ShareGPT format	Role mapping: human→user, gpt→assistant

Conversion Logic

# ShareGPT conversations -> Qwen3 messages
role_map = {
    "human": "user",
    "gpt": "assistant",
    "system": "system"
}
# conversations[i]["from"] -> messages[i]["role"]
# conversations[i]["value"] -> messages[i]["content"]

Key Features

• Deep Reasoning: Each tool call preceded by <think> chain-of-thought
• Multi-turn: Conversations with tool responses and follow-up
• Diverse Tools: Weather, search, database, calculation, API calls
• Clean Data: Removed problematic tools column — tools are embedded in system messages (Axolotl-compatible style)

Usage

from datasets import load_dataset

dataset = load_dataset("Mustafaege/qwen3.5-toolcalling-v1")

print(dataset)
# DatasetDict({
#     train: Dataset({features: ['messages'], num_rows: 45904}),
#     test:  Dataset({features: ['messages'], num_rows: 5100})
# })

# Check reasoning in responses
sample = dataset['train'][0]
for msg in sample['messages']:
    if msg['role'] == 'assistant' and '<think>' in msg['content']:
        print("Found reasoning trace!")
        print(msg['content'][:200])
        break

Training with Unsloth

from unsloth import FastLanguageModel
from trl import SFTTrainer, SFTConfig

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "unsloth/Qwen3-0.6B",
    max_seq_length = 4096,
    load_in_4bit = True,
)

trainer = SFTTrainer(
    model = model,
    tokenizer = tokenizer,
    train_dataset = dataset['train'],
    args = SFTConfig(
        per_device_train_batch_size = 2,
        gradient_accumulation_steps = 8,
        max_seq_length = 4096,
    ),
)
trainer.train()

Related Datasets

Version	Samples	New Sources	Link
v1 (this)	51,004	UIGEN-X	Mustafaege/qwen3.5-toolcalling-v1
v2	~60K+	+ Jupyter Agent	Mustafaege/qwen3.5-toolcalling-v2

License

Apache 2.0 — see LICENSE for details.

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Built for Qwen3.5 fine-tuning. Part of the Mustafaege model series.