austindixson/agent-dataset-hybrid-v2

Agent Dataset Hybrid v2

Fine-tuning dataset for training AI agents with tool-use capabilities and reasoning skills.

⚠️ Attribution Notice

This dataset incorporates data from the following sources:

Original Sources

1. Opus-4.6-Reasoning-3000x-filtered by nohurry

   • License: Apache 2.0
   • Contribution: Mathematical and logical reasoning samples with explicit thinking tags
   • Samples in this dataset: ~2,093 (5.2%)

2. Qwen3.5-reasoning-700x by Jackrong

   • License: Apache 2.0
   • Contribution: Natural language Chain-of-Thought reasoning
   • Samples in this dataset: ~569 (1.4%)

3. Agent conversations (primary dataset) by austindixson

   • License: Apache 2.0
   • Contribution: Claude Code agent workflows with tool-use
   • Samples in this dataset: ~39,971 (93.3%)

License Notice

This derivative work is licensed under the Apache 2.0 License.

Original datasets are used in accordance with their Apache 2.0 licenses. This dataset:

• ✅ Includes attribution to original creators
• ✅ States this is a derivative work
• ✅ Uses the same Apache 2.0 license
• ✅ Includes license notice

Dataset Description

Multi-purpose fine-tuning dataset combining:

• Tool-Use Workflows: Real agent conversations with file operations, code execution, search
• Mathematical Reasoning: Step-by-step problem solving with <thinking> tags
• Natural Reasoning: Conversational Chain-of-Thought processes

Dataset Statistics

• Training: 38,369 conversations (965,819 turns)
• Validation: 4,264 conversations (44,644 turns)
• Total: 42,633 conversations (1,010,463 turns)
• Average turns: 25.3 per conversation
• Tool-use conversations: ~3,867 (9.7%)

Data Format

Each example is a conversation with multiple turns:

{
  "conversations": [
    {"from": "human", "value": "User message"},
    {"from": "gpt", "value": "Assistant response"},
    {"from": "tool", "value": "Tool execution result"},
    {"from": "gpt", "value": "Assistant response to tool result"}
  ]
}

Use Cases

Perfect for fine-tuning models on:

• Tool-use and function calling
• Code generation and debugging
• Multi-step reasoning
• Agent workflows
• Software development tasks
• Mathematical problem solving

Training Recommendations

• Models: Claude, GPT-4, LLaMA, Gemma, GLM
• Method: QLoRA, full fine-tuning
• Sequence length: 8192+ tokens recommended
• Batch size: 16-32 effective

Citation

If you use this dataset, please cite the original sources:

@dataset{opus_reasoning,
  title = {Opus-4.6-Reasoning-3000x-filtered},
  author = {nohurry},
  year = {2024},
  publisher = {HuggingFace}
}

@dataset{qwen_reasoning,
  title = {Qwen3.5-reasoning-700x},
  author = {Jackrong},
  year = {2024},
  publisher = {HuggingFace}
}

@dataset{agent_hybrid,
  title = {Agent Dataset Hybrid v2},
  author = {austindixson},
  year = {2025},
  publisher = {HuggingFace}
}

License

Copyright 2025 austindixson

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Changes from Original Datasets

This dataset:

• Combines multiple datasets into a unified format
• Converts all datasets to a conversational format
• Adds tool-use execution results from Claude Code conversations
• Filters and curates samples for quality
• Splits into train/validation sets

All modifications comply with Apache 2.0 license requirements.