Synthetic Data Kit

Tool for generating high-quality synthetic datasets to fine-tune LLMs.

Generate Reasoning Traces, QA Pairs, save them to a fine-tuning format with a simple CLI.

Checkout our guide on using the tool to unlock task-specific reasoning in Llama-3 family

What does Synthetic Data Kit offer?

Fine-Tuning Large Language Models is easy. There are many mature tools that you can use to fine-tune Llama model family using various post-training techniques.

Why target data preparation?

Multiple tools support standardized formats. However, most of the times your dataset is not structured in "user", "assistant" threads or in a certain format that plays well with a fine-tuning packages.

This toolkit simplifies the journey of:

• Using a LLM (vLLM or any local/external API endpoint) to generate examples
• Modular 4 command flow
• Converting your existing files to fine-tuning friendly formats
• Creating synthetic datasets
• Supporting various formats of post-training fine-tuning

How does Synthetic Data Kit offer it?

The tool is designed to follow a simple CLI structure with 4 commands:

• ingest various file formats
• create your fine-tuning format: QA pairs, QA pairs with CoT, summary format
• curate: Using Llama as a judge to curate high quality examples.
• save-as: After that you can simply save these to a format that your fine-tuning workflow requires.

You can override any parameter or detail by either using the CLI or overriding the default YAML config.

Installation

From PyPI

# Create a new environment

conda create -n synthetic-data python=3.10 

conda activate synthetic-data

pip install synthetic-data-kit

(Alternatively) From Source

git clone https://github.com/meta-llama/synthetic-data-kit.git
cd synthetic-data-kit
pip install -e .

To get an overview of commands type:

synthetic-data-kit --help

1. Tool Setup

• The tool can process both individual files and entire directories.

# Create directory structure for the 4-stage pipeline
mkdir -p data/{input,parsed,generated,curated,final}

# Or use the legacy structure (still supported)
mkdir -p data/{pdf,html,youtube,docx,ppt,txt,output,generated,cleaned,final}

• You also need a LLM backend that you will utilize for generating your dataset, if using vLLM:

# Start vLLM server
# Note you will need to grab your HF Authentication from: https://huggingface.co/settings/tokens
vllm serve meta-llama/Llama-3.3-70B-Instruct --port 8000

2. Usage

The flow follows 4 simple steps: ingest, create, curate, save-as. You can process individual files or entire directories. All data is now stored in Lance format by default.

# Check if your backend is running
synthetic-data-kit system-check

# SINGLE FILE PROCESSING (Original approach)
# Parse a document to a Lance dataset
synthetic-data-kit ingest docs/report.pdf
# This saves file to data/parsed/report.lance

# Generate QA pairs (default)
synthetic-data-kit create data/parsed/report.lance --type qa

OR 

# Generate Chain of Thought (CoT) reasoning examples
synthetic-data-kit create data/parsed/report.txt --type cot

# Both of these save file to data/generated/report_qa_pairs.json

# Filter content based on quality
synthetic-data-kit curate data/generated/report_qa_pairs.json

# Convert to alpaca fine-tuning format and save as HF arrow file
synthetic-data-kit save-as data/curated/report_cleaned.json --format alpaca --storage hf

2.1 Batch Directory Processing (New)

Process entire directories of files with a single command:

# Parse all documents in a directory
synthetic-data-kit ingest ./documents/
# Processes all .pdf, .html, .docx, .pptx, .txt files
# Saves parsed text files to data/parsed/

# Generate QA pairs for all text files
synthetic-data-kit create ./data/parsed/ --type qa
# Processes all .txt files in the directory
# Saves QA pairs to data/generated/

# Curate all generated files
synthetic-data-kit curate ./data/generated/ --threshold 8.0
# Processes all .json files in the directory
# Saves curated files to data/curated/

# Convert all curated files to training format
synthetic-data-kit save-as ./data/curated/ --format alpaca
# Processes all .json files in the directory
# Saves final files to data/final/

2.2 Preview Mode

Use --preview to see what files would be processed without actually processing them:

# Preview files before processing
synthetic-data-kit ingest ./documents --preview
# Shows: directory stats, file counts by extension, list of files

synthetic-data-kit create ./data/parsed --preview
# Shows: .txt files that would be processed

Configuration

The toolkit uses a YAML configuration file (default: configs/config.yaml).

Note, this can be overridden via either CLI arguments OR passing a custom YAML file

# Example configuration using vLLM
llm:
  provider: "vllm"

vllm:
  api_base: "http://localhost:8000/v1"
  model: "meta-llama/Llama-3.3-70B-Instruct"
  sleep_time: 0.1

generation:
  temperature: 0.7
  chunk_size: 4000
  num_pairs: 25
  max_context_length: 8000

curate:
  threshold: 7.0
  batch_size: 8

or using an API endpoint:

# Example configuration using the llama API
llm:
  provider: "api-endpoint"

api-endpoint:
  api_base: "https://api.llama.com/v1"
  api_key: "llama-api-key"
  model: "Llama-4-Maverick-17B-128E-Instruct-FP8"
  sleep_time: 0.5

Customizing Configuration

Create a overriding configuration file and use it with the -c flag:

synthetic-data-kit -c my_config.yaml ingest docs/paper.pdf

Examples

Processing a Single PDF Document

# Ingest PDF
synthetic-data-kit ingest research_paper.pdf

# Generate QA pairs
synthetic-data-kit create data/parsed/research_paper.txt -n 30

# Curate data
synthetic-data-kit curate data/generated/research_paper_qa_pairs.json -t 8.5

# Save in OpenAI fine-tuning format (JSON)
synthetic-data-kit save-as data/curated/research_paper_cleaned.json -f ft

# Save in OpenAI fine-tuning format (HF dataset)
synthetic-data-kit save-as data/curated/research_paper_cleaned.json -f ft --storage hf

Processing Multiple Documents (Directory)

# Process all research papers in a directory
synthetic-data-kit ingest ./research_papers/

# Generate QA pairs for all parsed documents
synthetic-data-kit create ./data/parsed/ --type qa -n 30

# Curate all generated files
synthetic-data-kit curate ./data/generated/ -t 8.5

# Save all curated files in OpenAI fine-tuning format
synthetic-data-kit save-as ./data/curated/ -f ft --storage hf

Preview Before Processing

# See what files would be processed
synthetic-data-kit ingest ./research_papers --preview
# Output:
# Directory: ./research_papers
# Total files: 15
# Supported files: 12
# Extensions: .pdf (8), .docx (3), .txt (1)
# Files: paper1.pdf, paper2.pdf, ...

# Preview with verbose output
synthetic-data-kit create ./data/parsed --preview --verbose

Processing a YouTube Video

# Extract transcript
synthetic-data-kit ingest "https://www.youtube.com/watch?v=dQw4w9WgXcQ"

# Generate QA pairs with specific model
synthetic_data_kit create data/parsed/youtube_dQw4w9WgXcQ.lance

Multimodal Usage

The tool can also handle multimodal data, extracting both text and images from documents.

# Ingest a PDF with multimodal support
synthetic-data-kit ingest docs/report.pdf --multimodal

# This will create a Lance dataset at data/parsed/report.lance
# with 'text' and 'image' columns.

# Generate multimodal-qa pairs from the ingested data
synthetic-data-kit create data/parsed/report.lance --type multimodal-qa

Processing Multiple Files

# NEW: Process entire directories (recommended)
synthetic-data-kit ingest ./data/input/
synthetic-data-kit create ./data/parsed/ --type qa -n 20
synthetic-data-kit curate ./data/generated/ -t 7.5
synthetic-data-kit save-as ./data/curated/ -f chatml

# LEGACY: Bash script to process multiple files (still supported)
for file in data/pdf/*.pdf; do
  filename=$(basename "$file" .pdf)
  
  synthetic-data-kit ingest "$file"
  synthetic-data-kit create "data/parsed/${filename}.txt" -n 20
  synthetic-data-kit curate "data/generated/${filename}_qa_pairs.json" -t 7.5
  synthetic-data-kit save-as "data/curated/${filename}_cleaned.json" -f chatml
done

Document Processing & Chunking

How Chunking Works

The Synthetic Data Kit automatically handles documents of any size using an intelligent processing strategy:

• Small documents (< 8000 characters): Processed in a single API call for maximum context and quality
• Large documents (≥ 8000 characters): Automatically split into chunks with overlap to maintain context

Controlling Chunking Behavior

You can customize chunking with CLI flags or config settings for both single files and directories:

# Single file with custom chunking
synthetic-data-kit create document.txt --type qa --chunk-size 2000 --chunk-overlap 100

# Directory processing with custom chunking
synthetic-data-kit create ./data/parsed/ --type cot --num-pairs 50 --chunk-size 6000 --verbose

# Preview directory processing with chunking details
synthetic-data-kit create ./data/parsed/ --preview --verbose

Chunking Parameters

Parameter	Default	Description
--chunk-size	4000	Size of text chunks in characters
--chunk-overlap	200	Overlap between chunks to preserve context
--verbose	false	Show chunking details and progress

Understanding Chunking Output

When using --verbose, you'll see chunking information for both single files and directories:

# Single file verbose output
synthetic-data-kit create large_document.txt --type qa --num-pairs 20 --verbose

# Directory verbose output
synthetic-data-kit create ./data/parsed/ --type qa --num-pairs 20 --verbose

Output:

# Single file output
Generating QA pairs...
Document split into 8 chunks
Using batch size of 32
Processing 8 chunks to generate QA pairs...
  Generated 3 pairs from chunk 1 (total: 3/20)
  Generated 2 pairs from chunk 2 (total: 5/20)
  ...
  Reached target of 20 pairs. Stopping processing.
Generated 20 QA pairs total (requested: 20)

# Directory output
Processing directory: ./data/parsed/
Supported files: 5 (.txt files)
Progress: ████████████████████████████████████████ 100% (5/5 files)
✓ document1.txt: Generated 20 QA pairs
✓ document2.txt: Generated 18 QA pairs
✗ document3.txt: Failed - Invalid format
✓ document4.txt: Generated 20 QA pairs
✓ document5.txt: Generated 15 QA pairs

Processing Summary:
Total files: 5
Successful: 4
Failed: 1
Total pairs generated: 73

Chunking logic

Both QA and CoT generation use the same chunking logic for files and directories:

# Single file processing
synthetic-data-kit create document.txt --type qa --num-pairs 100 --chunk-size 3000
synthetic-data-kit create document.txt --type cot --num-pairs 20 --chunk-size 3000

# Directory processing
synthetic-data-kit create ./data/parsed/ --type qa --num-pairs 100 --chunk-size 3000
synthetic-data-kit create ./data/parsed/ --type cot --num-pairs 20 --chunk-size 3000

Advanced Usage

Custom Prompt Templates

Edit the prompts section in your configuration file to customize generation behavior:

prompts:
  qa_generation: |
    You are creating question-answer pairs for fine-tuning a legal assistant.
    Focus on technical legal concepts, precedents, and statutory interpretation.
    
    Below is a chunk of text about: {summary}...
    
    Create {num_pairs} high-quality question-answer pairs based ONLY on this text.
    
    Return ONLY valid JSON formatted as:
    [
      {
        "question": "Detailed legal question?",
        "answer": "Precise legal answer."
      },
      ...
    ]
    
    Text:
    ---
    {text}
    ---

Mental Model:

graph LR
    SDK --> SystemCheck[system-check]
    SDK[synthetic-data-kit] --> Ingest[ingest]
    SDK --> Create[create]
    SDK --> Curate[curate]
    SDK --> SaveAs[save-as]
    
    Ingest --> PDFFile[PDF File]
    Ingest --> HTMLFile[HTML File]
    Ingest --> YouTubeURL[File Format]

    
    Create --> CoT[CoT]
    Create --> QA[QA Pairs]
    Create --> Summary[Summary]
    
    Curate --> Filter[Filter by Quality]
    
    SaveAs --> JSONL[JSONL Format]
    SaveAs --> Alpaca[Alpaca Format]
    SaveAs --> FT[Fine-Tuning Format]
    SaveAs --> ChatML[ChatML Format]

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Troubleshooting FAQs:

vLLM Server Issues

• Ensure vLLM is installed: pip install vllm
• Start server with: vllm serve <model_name> --port 8000
• Check connection: synthetic-data-kit system-check

Memory Issues

If you encounter CUDA out of memory errors:

• Use a smaller model
• Reduce batch size in config
• Start vLLM with --gpu-memory-utilization 0.85

JSON Parsing Issues

If you encounter issues with the curate command:

• Use the -v flag to enable verbose output
• Set smaller batch sizes in your config.yaml
• Ensure the LLM model supports proper JSON output
• Install json5 for enhanced JSON parsing: pip install json5

Parser Errors

• Ensure required dependencies are installed for specific parsers:
  • PDF: pip install pdfminer.six
  • HTML: pip install beautifulsoup4
  • YouTube: pip install pytubefix youtube-transcript-api
  • DOCX: pip install python-docx
  • PPTX: pip install python-pptx

License

Read more about the License

Contributing

Contributions are welcome! Read our contributing guide