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Fine-Tuning in LLMs
Question: What is fine-tuning in LLMs, and how does it differ from pretraining?
Answer: Fine-tuning is the process of adapting a pre-trained Large Language Model (LLM) to a specific task or domain by training it further on a smaller, task-specific dataset. It differs from pretraining as follows:
- Pretraining: Involves training the model on a massive dataset using unsupervised learning, focusing on understanding language patterns.
- Fine-tuning: Customizes the pre-trained model for a specific task like sentiment analysis or summarization using supervised learning.
- Fine-tuning requires less data and computational resources compared to pretraining.
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from transformers import Trainer, TrainingArguments
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# Define the training arguments
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training_args = TrainingArguments(
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output_dir="./results",
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num_train_epochs=3,
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per_device_train_batch_size=16,
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)
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# Fine-tune the pre-trained model
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trainer = Trainer(
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model=pretrained_model,
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args=training_args,
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train_dataset=train_data,
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)
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trainer.train()