Fine-Tuning an Open-Source LLM with Axolotl Using Direct Preference Optimization (DPO) - SitePoint
Briefly

from Sitepoint2 months ago
Fine-tuning allows you to own your intellectual property and reduces your reliance on third parties. Using smaller models can yield comparable results at a fraction of the cost.
Sitepointhttps://www.sitepoint.com/fine-tuning-llm-with-direct-preference-optimization-dpo/
LLMs have unlocked countless new opportunities for AI applications, making it easier than ever to fine-tune models without needing to write code.
Sitepointhttps://www.sitepoint.com/fine-tuning-llm-with-direct-preference-optimization-dpo/
Base models serve as a starting point for developing specialized models and are pretrained on large amounts of unstructured text, but may produce incoherent outputs.
Sitepointhttps://www.sitepoint.com/fine-tuning-llm-with-direct-preference-optimization-dpo/
Understanding the types of LLMs—base, instruct, and chat models—sets the groundwork for effectively harnessing the potential of AI-driven language applications.
Sitepointhttps://www.sitepoint.com/fine-tuning-llm-with-direct-preference-optimization-dpo/
Read at Sitepoint
#llm #ai-applications #fine-tuning #machine-learning #language-models
[
|
]