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LLAMA Models & Fine-tuning

Mastering the art of adapting large language models for specific tasks

Fine-tuning Process

Fine-tuning is like teaching a knowledgeable student to become an expert in a specific field. Let's see how it works:

Pre-trained Model

Custom Dataset

Fine-tuning Process

Specialized Model

Start with a base LLAMA model trained on general knowledge

Training Progress

During fine-tuning, we monitor two key metrics: training loss (how far off our predictions are) and accuracy (how often we're correct). Here's what typical progress looks like:

Key Concepts

Data Preparation

Clean, high-quality training data is crucial. Format your data as instruction-response pairs for best results.

Hyperparameters

Key settings like learning rate, batch size, and number of epochs greatly affect training success.

Model Selection

Choose the right LLAMA model size based on your task complexity and computational resources.

Practical Example

Medical Domain Adaptation

Let's see how we'd fine-tune LLAMA for medical text analysis:

1. Data Preparation

# Example instruction-response pair
{
  "instruction": "Explain the symptoms of type 2 diabetes",
  "response": "Common symptoms include increased thirst,
    frequent urination, fatigue, and blurred vision..."
}

2. Training Configuration

training_args = {
    "learning_rate": 2e-5,
    "num_epochs": 3,
    "batch_size": 4,
    "weight_decay": 0.01
}

3. Evaluation

  • • Test on held-out medical questions
  • • Compare against medical knowledge base
  • • Validate with domain experts
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