README.md

metadata

license: apache-2.0
language:
  - en
pretty_name: Reasoning and Tool Calling
task_categories:
  - text-generation
tags:
  - reasoning
  - tool-calling
  - function-calling
configs:
  - config_name: conversations
    default: true
    data_files:
      - split: train
        path: data/conversations/train-*.parquet
      - split: test
        path: data/conversations/test-*.parquet
  - config_name: calibration_histories
    data_files:
      - split: calibration
        path: data/calibration_histories/calibration.parquet
  - config_name: calibration_windows
    data_files:
      - split: calibration
        path: data/calibration_windows/calibration.parquet

Reasoning and Tool Calling

This dataset converts tagged reasoning and tool-use conversations into typed messages and tool definitions. It also includes calibration data derived from those converted conversations.

The source is Mustafaege/qwen3.5-toolcalling-v2 at revision 8f0343a5613879fefda0eb002d10ff7150a2c588.

How this differs from the source

The source stores 92,153 train conversations and 10,240 test conversations in a tagged message format. Protocol instructions, tool declarations, reasoning, calls, and tool results can appear inside message text.

This release keeps rows that can be converted to one typed tool-calling schema without guessing about call order, tool identity, or message boundaries. It contains 56,032 train rows and 6,243 test rows.

Property	Source	This release
Train rows	92,153	56,032
Test rows	10,240	6,243
Message form	Tagged text	Typed messages with reasoning_content and tool_calls
Tool declarations	Embedded in protocol text	Structured tools field
Tool arguments	Embedded JSON	Canonical JSON string
Tool results	Tagged text	tool messages linked by call identifier
Row identity	Source position	Source revision, file hash, row number, and content hash
Calibration data	Not included	25 rendered histories and four token windows

The conversion does not add conversations. It extracts protocol structure, normalizes the representation, and omits rows whose structure cannot be mapped under the rules below.

A row enters conversations when all of these conditions hold:

• it contains at least one tool call;
• tool declarations can be extracted without ambiguity;
• every called tool has a matching declaration;
• call arguments contain valid JSON;
• tool results can be paired with calls in sequence;
• reasoning and answer text occupy valid positions in that sequence;
• source-format control tags do not remain after conversion.

The main primary exclusion reasons were:

Primary reason	Train	Test
Assistant content appears before pending tool results	15,120	1,667
No tool call	10,745	1,176
An unresolved call precedes another call	4,650	534
Embedded tool declarations are ambiguous	2,194	251
Empty tool response	1,378	163
Other tag, JSON, tool-name, or sequence failures	2,034	206
Rows omitted	36,121	3,997

Configurations

Configuration	Split	Rows	Intended use
conversations	train	56,032	Training, calibration-candidate selection, or format study
conversations	test	6,243	The same conversion applied to the source test split
calibration_histories	calibration	25	Inspecting the selected rendered histories and their token IDs
calibration_windows	calibration	4	Reusing the exact four 2,048-token calibration windows

The train and test names preserve the source assignments.

Conversation representation

Each row in conversations contains:

• source dataset, revision, split, file, file SHA-256, row number, and content identity SHA-256;
• style and tool-call category;
• typed messages;
• typed tool declarations;
• counts for source messages, users, reasoning messages, answers, tools, calls, results, and terminal unresolved calls.

Assistant messages can contain:

• content for visible answer text;
• reasoning_content for extracted reasoning;
• tool_calls with call ID, type, tool name, and arguments_json.

Tool messages carry tool_call_id. Tool declarations contain name, description, and parameters_json.

arguments_json and parameters_json remain strings because argument values and JSON Schema properties have heterogeneous types across tools. Decode them with a JSON parser when object values are needed.

Calibration views

The conversations configuration does not depend on a model tokenizer. The two calibration configurations record a DeepSeek-V4-Flash-0731 rendering and use its tokenizer. They are included for users who need the exact rendered input; other uses should start from conversations.

The calibration view was formed from 25 accepted train histories:

Category	Histories
One tool call	9
Multiple tool calls	8
Reasoning with at most three tool calls	5
Reasoning with more than three tool calls	3

The histories were rendered with thinking enabled and DSML tool calls. They were tokenized and concatenated within each category, and one 2,048-token slice was taken from each category stream. calibration_windows contains those four slices, for 8,192 tokens in total.

calibration_histories stores each selected conversation, its rendered prompt, token IDs, category, stream offsets, and content hashes.

calibration_windows stores each slice's token IDs, category, stream offsets, marker counts, selection rule, and contributing history identities. Tokenizer hashes identify the vocabulary used for these token IDs.

License and modifications

The source declares the Apache License 2.0. This release uses the same license.