Training
Instruction-Tuned Model
A base model that has been further fine-tuned on tasks phrased as natural-language instructions so it responds to requests instead of just continuing text.
In depth
A base model that has been further fine-tuned on tasks phrased as natural-language instructions so it responds to requests instead of just continuing text. Google's FLAN paper showed that instruction tuning a 137B model on over 60 tasks substantially improved zero-shot performance on unseen task types. Most chat and "-Instruct" models add preference training such as RLHF on top, as OpenAI did with InstructGPT.
Primary source: arxiv.org
Example
Most chat and "-Instruct" models add preference training such as RLHF on top, as OpenAI did with InstructGPT.
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FAQ
Common questions
What is Instruction-Tuned Model?
A base model that has been further fine-tuned on tasks phrased as natural-language instructions so it responds to requests instead of just continuing text.
Why does Instruction-Tuned Model matter for AI developers?
Instruction-Tuned Model sits in the Training 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 Instruction-Tuned Model?
Developers Digest publishes tutorials and videos that cover Training topics including Instruction-Tuned Model. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Training
RLHF (Reinforcement Learning from Human Feedback)
A training technique that fine-tunes a model using human preference judgments.
Training
DPO (Direct Preference Optimization)
A training technique that aligns language models with human preferences without needing a separate reward model.
Training
PEFT (Parameter-Efficient Fine-Tuning)
A family of fine-tuning methods that adapt a large pretrained model by training only a small number of new or selected parameters while the original weights stay frozen.
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