Introducing Llama 3: Meta’s New Open LLM

in

We then use the `AutoTokenizer` class to load the tokenizer used by Llama 3 and create a new training script using the `Trainer` class provided by Hugging Face Transformers.
In this example, we fine-tune our model on a dataset of 1000 examples with a batch size of 8 and an epochs parameter set to 5. We also use the `learning_rate` argument to specify the learning rate for our optimizer (in this case, Adam).
Here’s what the training script looks like:

# Import necessary libraries
from transformers import AutoTokenizer, Trainer
import torch

# Load pretrained Llama 3 checkpoint and tokenizer
tokenizer = AutoTokenizer.from_pretrained("facebook/llama-2-13b") # Load the tokenizer from the pretrained Llama 3 checkpoint
model = AutoModelForSequenceClassification.from_pretrained("facebook/llama-2-13b", num_labels=len(classes)) # Load the model from the pretrained Llama 3 checkpoint and specify the number of labels

# Define training arguments
args = {
    "learning_rate": 5e-6, # Set the learning rate to 5e-6
    "num_train_epochs": 5, # Train for 5 epochs
    "per_device_train_batch_size": 8, # Use a batch size of 8 per device (we have 4 GPUs)
}

# Create training script using Trainer class from Hugging Face Transformers library
trainer = Trainer(model=model, args=args, train_dataset=train_dataset, eval_dataset=val_dataset) # Create a Trainer object with the specified model, arguments, and datasets
trainer.train() # Train the model using the Trainer object for 5 epochs with a batch size of 8 per device (we have 4 GPUs)

In this example, we’re using Hugging Face Transformers to fine-tune our Llama 3 model on a custom dataset. We first load the pretrained checkpoint and tokenizer for Llama 3 from Meta’s GitHub repository. Then, we define some training arguments (such as learning rate, number of epochs, and batch size) using a dictionary called `args`.
We then create a new training script using the `Trainer` class provided by Hugging Face Transformers library. This script takes care of loading our dataset, creating a data collator to prepare the input data for our model, and running the actual training process. We pass in our pretrained Llama 3 model (which we loaded earlier), as well as some other arguments like `train_dataset` and `eval_dataset`.
Finally, we call the `trainer.train()` method to start the training process. This will run for 5 epochs with a batch size of 8 per device (we have 4 GPUs).

SICORPS