Interactive 3D/Instruction Tuning - Raw vs Fine-Tuned LLMs
Instruction template (ChatML format)
<|system|>
You are a helpful assistant that summarizes text concisely and accurately.
<|user|>
Summarize the following article in 2 sentences.
Large language models have demonstrated remarkable …
<|assistant|>
[model generates here]
Raw Pretrained Model
Continues the text literally - ignores the instruction
Large language models have demonstrated remarkable capabilities across a wide range of natural language processing tasks. Trained on massive corpora of text data using self-supervised objectives, these models develop generalizable representations of language. These representations can then be fine-tuned on downstream tasks with relatively little labeled data, achieving state-of-the-art performance...
Does not follow instructionRepeats input
Instruction-Tuned Model
Follows the instruction, produces the expected output format
Large language models are powerful NLP systems trained on vast text data using self-supervised learning. Their learned representations transfer effectively to downstream tasks, often achieving state-of-the-art results with minimal fine-tuning data.
Instruction tuning (Wei et al., 2022) fine-tunes pretrained models on thousands of (instruction, output) pairs across many task types. Result: the model generalizes to follow novel instructions at inference time.
Instruction Tuning - Raw vs Fine-Tuned LLMs - Interactive Visualization
Instruction tuning (Wei et al., 2022) fine-tunes pretrained language models on thousands of (instruction, output) pairs across diverse task types. Without it, a pretrained model simply continues text as a completion - it ignores the intent of the prompt. After instruction tuning on datasets like FLAN or Alpaca, the same model follows novel instructions at inference time without task-specific training.
Side-by-side comparison of raw pretrained completion vs instruction-tuned response on the same prompt
ChatML template overlay showing system/user/assistant sections with token boundaries
Four task types: summarization, translation, code generation, and question answering
Task coverage badges showing how instruction tuning generalizes across eight task categories
Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.
