Скачать или смотреть 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.

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