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Fine-tuning vs Full Training

Should you train a model from scratch or adapt an existing one? The answer is almost always fine-tuning.

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The Difference

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Fine-tuning

Start with a pre-trained model and teach it your specific task. 
Pre-trained BERT → Your sentiment classifier
Pre-trained LLaMA → Your chatbot
Pre-trained ResNet → Your product detector
The model already understands language/images. You’re teaching it your specific needs.

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Full Training

Start with random weights and train on massive data from scratch. 
Random weights → Millions of examples → New model
Building all knowledge from zero.

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The Complexity Difference

Fine-tuning:
  • Start with working model
  • Adjust existing knowledge
  • Hours to days of training
  • Manageable on single GPU
Full training:
  • Start from random noise
  • Build all knowledge from scratch
  • Weeks to months of training
  • Complex distributed training

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When to Fine-tune (99% of cases)

  • Adding specific knowledge to a model
  • Adapting to your domain
  • Customizing behavior
  • Working with limited data
  • Normal budgets
Examples:
  • Customer service bot
  • Medical document classifier
  • Code generator for your API
  • Sentiment analysis for reviews

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When to Train from Scratch (1% of cases)

  • Creating a foundational model (GPT, BERT, etc.)
  • Completely novel architecture
  • Unique data type not seen before
  • Research purposes
  • Unlimited resources
Examples:
  • OpenAI training GPT
  • Google training Gemini
  • Meta training LLaMA

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Why Fine-tuning Wins

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Transfer Learning

The model already knows:
  • Grammar and language structure
  • Object shapes and textures
  • Common sense reasoning
  • World knowledge
You just teach:
  • Your specific vocabulary
  • Your task requirements
  • Your domain knowledge

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Efficiency

Starting from scratch means teaching:
  • What words are
  • How sentences work
  • Basic concepts
  • Everything from zero
It’s like teaching someone to be a chef when they already know how to cook vs teaching someone who’s never seen food.

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Quick Comparison

AspectFine-tuningFull Training
Data neededHundreds to thousandsMillions
TimeHours to daysWeeks to months
Starting pointPre-trained modelRandom weights
InfrastructureSingle GPU worksMulti-GPU setup
Code complexitySimple scriptsComplex pipelines
Risk of failureLowHigh

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The Fine-tuning Process

  1. Choose base model: Pick one trained on similar data
  2. Prepare your data: Format for your specific task
  3. Set hyperparameters: Usually lower learning rate
  4. Train: Typically 3-10 epochs
  5. Evaluate: Check if it learned your task

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Common Misconceptions

“My data is unique, I need full training”
  • No. Even unique domains benefit from transfer learning.
“Fine-tuning limits creativity”
  • No. You can dramatically change model behavior.
“Full training gives better results”
  • Rarely. Fine-tuning usually wins with less data.

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Full Training in Practice

Karpathy’s nanochat shows what full training actually involves. Even for a “minimal” ChatGPT clone:
  • Custom tokenization
  • Distributed training setup
  • Data pipeline management
  • Evaluation harnesses
  • Web serving infrastructure
  • Managing the entire pipeline end-to-end
And that’s designed to be as simple as possible. Real production training is far more complex.

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Practical Advice

Start with fine-tuning. Always. If you’re asking “should I train from scratch?” the answer is no. Full training is fascinating to understand, important for pushing the field forward, but rarely the right choice for solving practical problems.

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Next Steps

Choosing Your Approach

Detailed decision guide

Model Types

Pick your base model