README.md

metadata

license: apache-2.0
task_categories:
  - question-answering
  - feature-extraction
  - text-classification
  - summarization
language:
  - en
tags:
  - thinking
  - reasoning
  - tools
  - grpo
  - function-calling
  - Opus
  - gpt
  - qwen
size_categories:
  - 1K<n<10K

🧠 GRPO 4K Reasoning Tools

A compact, high-quality dataset designed to train and evaluate reasoning-capable LLMs with tool usage and function calling. This dataset focuses on structured thinking, multi-step reasoning, and practical tool integration across diverse NLP tasks.

📌 Overview

GRPO 4K Reasoning Tools is a curated dataset of ~4K examples that combines:

• 🧩 Step-by-step reasoning (“thinking” traces)
• 🛠️ Tool usage / function calling patterns
• 🧠 Multi-task learning signals

It is suitable for training or fine-tuning models to:

• Think before answering
• Decide when to use tools
• Produce structured and reliable outputs

🎯 Tasks Covered

This dataset spans multiple NLP task categories:

• Question Answering
• Feature Extraction
• Text Classification
• Summarization

Each example is designed to encourage reasoning-first behavior, often requiring intermediate steps before producing the final answer.

✨ Key Features

• 🧠 Reasoning-Centric Includes explicit reasoning steps to improve chain-of-thought capabilities.

• 🛠️ Tool-Augmented Examples demonstrate when and how to call tools (function calling format).

• 🔄 Multi-Model Friendly Compatible with training setups for models like GPT-style, Qwen, and other instruction-tuned LLMs.

• 📏 Compact but Dense (~4K samples) Carefully curated for quality over quantity.

• ⚙️ Structured Outputs Useful for training structured generation (JSON, tool calls, etc.).

📂 Dataset Structure

Each sample typically contains:

{
  "category": "math"
  "system": "User query or task description",
  "user": "Optional context",
  "thinking": "Step-by-step reasoning process",
  "messages": [
    {
      "role": "assistant",
      "content": "<think>...</think>..."
    }
  ],
  "assistant": "Final answer"
}

Fields Explained

Field	Description
instruction	Task description or user query
input	Additional context (optional)
thinking	Intermediate reasoning steps
tool_calls	Function/tool usage (if applicable)
output	Final response

🧪 Use Cases

• Fine-tuning LLMs for:

  • Tool use (function calling)
  • Structured reasoning
  • Multi-step problem solving

• Evaluating:

  • Reasoning quality
  • Tool selection accuracy
  • Output consistency

• Research in:

  • GRPO (Generalized Reinforcement Policy Optimization)
  • Chain-of-thought learning
  • Tool-augmented agents

🚀 Getting Started

Load Dataset (Hugging Face)

from datasets import load_dataset

dataset = load_dataset("nphearum/grpo-4k-reasoning-tools")

print(dataset["train"][0])

🧠 Training Tips

• Use reasoning traces (thinking) as supervision targets

• Optionally:

  • Mask reasoning during inference
  • Train with or without tool calls depending on your objective

• Combine with:

  • Instruction tuning datasets
  • Tool-use benchmarks

🏷️ Tags

reasoning • thinking • tools • function-calling • grpo • multi-task

📊 Dataset Size

• ~4K examples
• Category: 1K < n < 10K

📜 License

Apache License 2.0

🤝 Contributing

Contributions, improvements, and extensions are welcome! Feel free to:

• Add more tool-use scenarios
• Improve reasoning quality
• Expand task diversity

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📬 Contact

Maintained by @nphearum For questions or collaboration, open an issue or discussion on the repository.

If you want, I can also tailor this README for:

• Hugging Face dataset card format (with YAML + metrics)
• GitHub repo README (with badges, visuals)
• Training pipeline examples (GRPO / RLHF / SFT)

Just tell me 👍