# Source: https://docs.lancedb.com/integrations/ai/synthetic-data-kit.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lancedb.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Meta Llama Synthetic Data Kit

> Use Meta Llama's Synthetic Data Kit with LanceDB to generate high-quality synthetic datasets for LLM fine-tuning and training.

[Sythetic Data Kit](https://github.com/meta-llama/synthetic-data-kit) is a tool from Meta LLAMA that helps you generate high-quality synthetic datasets for fine-tuning large language models (LLMs). It simplifies the process of preparing data for fine-tuning by providing a command-line interface (CLI) with a modular four-command flow.

One of the key features of the `synthetic-data-kit` is its use of the Lance format for storing and ingesting datasets. This allows for efficient storage and retrieval of data, which is crucial when working with large datasets.

### Key Features:

* **Data Ingestion:** The toolkit can ingest various file formats, including PDF, HTML, YouTube transcripts, DOCX, PPT, and TXT.
* **Fine-tuning Format Creation:** It can create different fine-tuning formats, such as question-answer (QA) pairs, QA pairs with Chain-of-Thought (CoT), and summarization formats.
* **Data Curation:** The tool uses Llama as a judge to curate high-quality examples, ensuring the quality of the generated dataset.
* **Flexible Saving Options:** You can save the generated datasets in various formats compatible with your fine-tuning workflow, including Hugging Face, JSONL, and JSON.

### How it Works:

The synthetic-data-kit follows a simple four-step process:

1. **Ingest:** Import your input files into the toolkit. The data is stored in the Lance format for efficient processing.
2. **Create:** Generate diverse fine-tuning datasets, such as reasoning, summarization, and QA pairs, from the ingested documents.
3. **Curate:** Use Llama to filter and select high-quality examples from the generated dataset.
4. **Save-as:** Export the curated dataset in your preferred format.

### Usage

The `synthetic-data-kit` uses Lance format to store and manage the data that you ingest. The workflow is a series of commands that build on each other, starting with the `ingest` command.

Here is an example of the end-to-end workflow:

1. **Ingest Data into a LanceDB dataset**

   This command takes a directory of source files and creates a LanceDB dataset from them.

   ```bash  theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
   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
   ```

2. **Create fine-tuning data**

   This command uses the LanceDB dataset created in the previous step to generate synthetic data in the desired format.

   ```bash  theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
   synthetic_data create data/parsed/report.lance
   ```

3. **Curate the data**

   This step uses a language model to curate the generated data and ensure its quality.

   ```bash  theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
   synthetic_data curate report.json
   ```

4. **Save the final dataset**

   Finally, save the curated data to a file in the desired format.

   ```bash  theme={"theme":{"light":"vitesse-light","dark":"catppuccin-mocha"}}
   synthetic_data save-as  report.json --save_path ./my_finetuning_data.jsonl
   ```

This workflow allows you to go from a collection of documents to a high-quality, fine-tuning dataset with just a few commands. The use of LanceDB in the background makes the process efficient and scalable.

### Getting Started:

To get started with the synthetic-data-kit, you can clone the [GitHub Repository](https://github.com/meta-llama/synthetic-data-kit) and install the necessary dependencies.

> **Note:** You will also need access to a Llama model, either running locally or via a hosted API.