Techniques for customising Foundation Models
Aruna Pattam
LinkedIn Top Voice AI | Head, Generative AI | Thought Leader | Speaker | Master Data Scientist | MBA | Australia's National AI Think Tank Member | Australian
Foundation models are at the heart of Generative AI, trained on vast datasets to excel in tasks ranging from language processing to image recognition. To unlock their full potential, it's crucial to customize these models for specific tasks through fine-tuning with specialized data, adjusting parameters, or integrating new algorithms.
Explore essential techniques such as prompt engineering, fine-tuning, and retrieval-augmented generation to tailor these large language models (LLMs) to your specific needs. Whether you're looking to enhance accuracy, accelerate processing, or adapt to niche markets, this article provides a detailed guide on how to effectively customize foundation models.
Learn more by reading the full article here.
Polymath | BA MB BChir MA MRCS PGCME FHEA MA MBA PgCert PhD (in-progress) FRSA | AI Data Scientist | PhD Candidate | Startup Founder | Doctor | Children’s Author
9 个月Mind blown Aruna Pattam ??
GEN AI Evangelist | #TechSherpa | #LiftOthersUp
10 个月Adapting foundation models is key for optimal performance. Fine-tuning unleashes their true potential across industries. Customizing enables accurate, efficient AI solutions tailored to unique needs. Aruna Pattam
Founder at Occupational Therapy Brisbane
10 个月Customizing foundation models through fine-tuning, prompt engineering, and retrieval-augmented generation is crucial for maximizing their potential. It enhances accuracy, efficiency, and adaptability in various industries.
?? AI Strategy for CEOs | Fractional Chief AI Officer | High-End Advisory
10 个月Love the insight on customising models - crucial step indeed. Aruna Pattam