How to Create Synthetic Datasets for Fine-Tuning Llama скачать в хорошем качестве

How to Create Synthetic Datasets for Fine-Tuning Llama

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: https://bit.ly/4eLmwHW Check out the project on Pypi: https://bit.ly/4eDI4Ge Visit https://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.