Mastering AI: Fine-Tuning Meta's LLaMA 2 for Optimal Performance

Unlock the full potential of Meta's LLaMA 2 with our comprehensive guide to fine-tuning. In this video, we walk you through the steps to customize this advanced language model for your specific needs. From setting up your environment to evaluating performance, learn how to make LLaMA 2 work for you. Perfect for AI enthusiasts, developers, and data scientists looking to enhance their AI capabilities. Don't miss out-watch now and transform your projects! Detailed guide here tothemoonwitha...
In this video, we cover several key Python code snippets to help you fine-tune LLaMA 2 effectively. Below are the snippets we discuss:
1 Loading the LLaMA 2 Model:
from transformers import LLaMAModel, LLaMATokenizer
tokenizer = LLaMATokenizer.from_pretrained('meta-llama/LLaMA-2')
model = LLaMAModel.from_pretrained('meta-llama/LLaMA-2')
2 Preparing Your Dataset:
from datasets import load_dataset
dataset = load_dataset('your_dataset_name')
3 Setting Up the Training Loop:
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir='./results',
num_train_epochs=3,
per_device_train_batch_size=4,
save_steps=10_000,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset['train'],
eval_dataset=dataset['test'],
)
trainer.train()
4 Evaluating the Model:
eval_results = trainer.evaluate()
print(f'Perplexity: {math.exp(eval_results["eval_loss"]):.2f}')
Timestamps:
00:00:00
Introduction to Meta's LLaMA 2
00:01:04
Understanding LLaMA 2 and Fine-Tuning
00:01:35
Preparing for LLaMA 2 Fine-Tuning
00:02:48
A Step-by-Step Guide
00:03:30
Evaluating LLaMA 2's Performance
00:03:59
Conclusion and Further Exploration
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