Basic Datasets¶

Basic datasets generate simple question-answer pairs without reasoning traces or tool calls.

When to Use¶

General instruction-following tasks
Domain-specific Q&A (e.g., customer support, FAQs)
Models that don't need to show reasoning
Quick dataset generation with minimal configuration

Configuration¶

topics:
  prompt: "Python programming fundamentals"
  mode: tree
  depth: 2
  degree: 3

generation:
  system_prompt: "Generate clear, educational Q&A pairs."
  instructions: "Create diverse questions with detailed answers."

  conversation:
    type: basic

  llm:
    provider: "openai"
    model: "gpt-4o"

output:
  system_prompt: |
    You are a helpful assistant.
  num_samples: 10
  save_as: "dataset.jsonl"

The key setting is conversation.type: basic.

Output Format¶

Basic datasets produce standard chat-format JSONL:

{
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "What are Python's numeric data types?"
    },
    {
      "role": "assistant",
      "content": "Python has three built-in numeric types: integers (int), floating-point numbers (float), and complex numbers (complex)..."
    }
  ]
}

CLI Usage¶

Generate a basic dataset from the command line:

deepfabric generate config.yaml

Or with inline options:

deepfabric generate \
  --topic-prompt "Machine learning basics" \
  --conversation-type basic \
  --num-samples 50 \
  --provider openai \
  --model gpt-4o \
  --output-save-as ml-dataset.jsonl

Tips¶

Topic depth and degree control dataset diversity. A tree with depth: 3 and degree: 3 produces 40 unique topics (1 + 3 + 9 + 27).

System prompts differ between generation and output: - generation.system_prompt - Instructions for the LLM generating examples - output.system_prompt - The system message included in training data

Batch size affects generation speed. Higher values (batch_size: 5) parallelize requests but may hit rate limits.