Uploaded July 2025 | Updated September 2026, 1 week ago
Creating post training datasets for fine-tuning your LLMs is time consuming. Synthetic-data-kit simplifies your journey to fine-tuning by allowing you to create synthetic datasets. This video introduces the Synthetic-data-kit, a powerful CLI tool designed to simplify and accelerate your journey to customizing Llama models.
Partner Engineer at Meta, Sanyam Bhutani, will walk you through a simple four-step process, demonstrating how to:
- Integrate with Llama models: Use a locally running model or a hosted API like the Llama-API.
- Ingest your input files: Easily import documents for dataset generation.
- Create diverse fine-tuning datasets: Generate Reasoning, Summarization, and QA Pairs from your ingested documents.
- Save in preferred formats: Export your datasets to Hugging Face, JSONL, or JSON files.
The Synthetic-data-kit empowers you to customize every aspect of the process via a CLI or config files, giving you full control over prompts and defaults.
Resources:
- Explore the Github repo: bit.ly/4eLmwHW
- Check out the project on Pypi: bit.ly/4eDI4Ge
Visit bit.ly/44m2QqA for more resources and to stay updated on new features and capabilities.
Don't miss out! Subscribe to learn more about Llama development and join our community on GitHub.
Creating post training datasets for fine-tuning your LLMs is time consuming. Synthetic-data-kit simplifies your journey to fine-tuning by allowing you to create synthetic datasets. This video introduces the Synthetic-data-kit, a powerful CLI tool designed to simplify and accelerate your journey to customizing Llama models.
Partner Engineer at Meta, Sanyam Bhutani, will walk you through a simple four-step process, demonstrating how to:
- Integrate with Llama models: Use a locally running model or a hosted API like the Llama-API.
- Ingest your input files: Easily import documents for dataset generation.
- Create diverse fine-tuning datasets: Generate Reasoning, Summarization, and QA Pairs from your ingested documents.
- Save in preferred formats: Export your datasets to Hugging Face, JSONL, or JSON files.
The Synthetic-data-kit empowers you to customize every aspect of the process via a CLI or config files, giving you full control over prompts and defaults.
Resources:
- Explore the Github repo: bit.ly/4eLmwHW
- Check out the project on Pypi: bit.ly/4eDI4Ge
Visit bit.ly/44m2QqA for more resources and to stay updated on new features and capabilities.
Don't miss out! Subscribe to learn more about Llama development and join our community on GitHub.
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