AI Development
Foundation Model
A model trained on broad data at scale that can be adapted to many downstream tasks through prompting or fine-tuning.
In depth
A model trained on broad data at scale that can be adapted to many downstream tasks through prompting or fine-tuning. Stanford researchers coined the term in a 2021 report, citing BERT, DALL-E and GPT-3, to stress that these models are central to applications yet incomplete on their own. Because many products are built on the same few base models, any defects in a foundation model carry over to everything adapted from it.
Primary source: arxiv.org
Example
In practice, developers reach for Foundation Model when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Foundation Model?
A model trained on broad data at scale that can be adapted to many downstream tasks through prompting or fine-tuning.
Why does Foundation Model matter for AI developers?
Foundation Model sits in the AI Development part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Foundation Model?
Developers Digest publishes tutorials and videos that cover AI Development topics including Foundation Model. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
AI Development
Frontier Model
The most capable general-purpose models available at a given time, typically the latest flagship releases from the leading labs.
AI Development
Fine-tuning
The process of training a pre-existing model on a custom dataset to specialize its behavior.
AI Development
FSDP (Fully Sharded Data Parallel)
A PyTorch training method that splits model parameters, gradients, and optimizer state across GPUs instead of copying them all onto every device as plain data parallelism does.
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