Fine-Tuning LLaMA 2: A Comprehensive Guide
Introduction
LLaMA 2, a cutting-edge language model from Meta, has captured the attention of the AI community. In this tutorial, we will guide you through the process of fine-tuning LLaMA 2 on a new dataset using Google Colab.
Prerequisites
Before getting started, ensure you have the following: * A Google Colab account * A dataset to fine-tune LLaMA 2 on * Basic knowledge of Python and machine learning
Step-by-Step Fine-Tuning Guide
1. Import Required Libraries
Create a new Colab notebook and import the necessary libraries. ```python import torch import transformers from llama_finetuning import LLAMAForSequenceClassification ```
2. Load and Tokenize Dataset
Load your dataset and tokenize it using a suitable tokenizer. ```python tokenizer = transformers.AutoTokenizer.from_pretrained("facebook/meta-llama-base") train_dataset = transformers.datasets.load_dataset("squad", split="train") ```
3. Initialize Model
Initialize the LLaMA 2 model and specify the fine-tuning parameters. ```python model = LLAMAForSequenceClassification.from_pretrained("facebook/meta-llama-base") optimizer = torch.optim.AdamW(model.parameters(), lr=1e-5) ```
4. Training Loop
Implement a training loop to fine-tune the model. ```python for epoch in range(10): model.train() for batch in train_dataset: outputs = model(**batch) loss = outputs.loss loss.backward() optimizer.step() optimizer.zero_grad() ```
5. Evaluating Model Performance
Evaluate the fine-tuned model's performance on a validation set. ```python from sklearn.metrics import accuracy_score val_dataset = transformers.datasets.load_dataset("squad", split="validation") model.eval() with torch.no_grad(): for batch in val_dataset: outputs = model(**batch) preds = outputs.logits.argmax(-1) accuracy = accuracy_score(batch['answer'], preds) ```
6. Save Fine-Tuned Model
After fine-tuning, save the model for future use. ```python model.save_pretrained("my_fine-tuned_llama_2") ```
Conclusion
By following these steps, you can successfully fine-tune LLaMA 2 on a new dataset using Google Colab. This powerful language model can enhance the performance of various NLP tasks, including question answering, text classification, and dialogue generation.
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