abhi26/openpipe-chat-complete-scientific-reasoning

Openpipe Chat Complete Scientific Reasoning

This dataset contains 100 high-quality examples for chat completion fine-tuning, formatted for OpenPipe, focused on scientific reasoning and analysis.

Dataset Description

This dataset was generated using an enhanced DSPy-based pipeline that creates structured reasoning traces for scientific questions. Each example follows the OpenAI chat completion format required by OpenPipe:

• OpenAI Chat Format: Standard messages array with system, user, and assistant messages
• High-quality responses: Structured responses with <thinking>, <reasoning>, and <answer> sections
• Rich metadata: All metadata values are strings as required by OpenPipe
• Context Integration: Research context embedded in system messages

Dataset Statistics

• Total Examples: 100
• Training Examples: 90
• Test Examples: 10

Dataset Structure

Each example contains:

{
    "messages": [
        {"role": "system", "content": "System message with embedded context"},
        {"role": "user", "content": "User question"},
        {"role": "assistant", "content": "High-quality structured response"}
    ],
    "split": "TRAIN" | "TEST",
    "metadata": {
        "question_id": "unique_identifier",
        "document": "source_document",
        "reasoning_type": "scientific_analysis",
        "domain": "scientific_research",
        "task_type": "chat_completion",
        // All values are strings as required by OpenPipe
    }
}

Usage with OpenPipe

This dataset is specifically formatted for OpenPipe fine-tuning:

# Install OpenPipe CLI
pip install openpipe

# Upload dataset
openpipe upload dataset.jsonl

# Start fine-tuning
openpipe train --dataset-id YOUR_DATASET_ID

Key Features

• OpenPipe Compatible: Follows exact format requirements for OpenPipe platform
• Context Integration: Research context embedded in system messages for better performance
• Structured Reasoning: All responses follow <thinking>, <reasoning>, <answer> format
• String Metadata: All metadata values converted to strings as required
• Quality Control: Automatic mapping between research questions and DSPy responses
• Chat Completion Ready: Optimized for instruction following and reasoning tasks

Generation Details

• Source: DSPy-enhanced pipeline with research question mapping
• Created: 2025-08-10
• Format: OpenAI chat completion format
• Context Policy: Full context preservation embedded in system messages
• Quality Control: Automatic mapping between research questions and DSPy responses

Citation

If you use this dataset, please cite:

@dataset{abhi26_openpipe_chat_complete_scientific_reasoning,
    title={Openpipe Chat Complete Scientific Reasoning},
    author={Enhanced Chat Dataset Generator},
    year={2024},
    url={https://huggingface.co/datasets/abhi26/openpipe-chat-complete-scientific-reasoning}
}

License

This dataset is released under the MIT License.