AI — Fine-Tuning
What is Fine-Tuning?
Training a pre-trained model on specific data.
When to Fine-Tune
| Scenario | Recommendation |
|---|---|
| General tasks | Use pre-trained |
| Domain-specific | Fine-tune |
| Custom format | Fine-tune |
| Limited data | Few-shot |
OpenAI Fine-Tuning
from openai import OpenAI
client = OpenAI(api_key="your-key")
# Upload training data
file = client.files.create(
file=open("training.jsonl", "rb"),
purpose="fine-tune"
)
# Create fine-tune job
job = client.fine_tuning.jobs.create(
training_file=file.id,
model="gpt-3.5-turbo"
)
Hugging Face Fine-Tuning
from transformers import AutoModelForSequenceClassification, Trainer
model = AutoModelForSequenceClassification.from_pretrained("bert-base")
trainer = Trainer(model=model, train_dataset=train_dataset)
trainer.train()
Mini Practice
- Prepare training data
- Fine-tune a model
- Evaluate performance
- Deploy fine-tuned model
Up Next
Continue with Model Evaluation — evaluating models.
Related Topics
Frequently Asked Questions about Fine-Tuning
What is Fine-Tuning in AI?
Fine-Tuning is a fundamental concept in AI. This lesson explains it step by step with clear examples, making it easy for beginners to understand.
How do I learn Fine-Tuning?
Start by reading the explanation above, then try the code examples. Practice by modifying the examples and experimenting with different values. Hands-on practice is the best way to learn Fine-Tuning.
Why is Fine-Tuning important in AI?
Fine-Tuning is essential for AI development. Understanding this concept will help you write better code and solve real-world problems more effectively.