In the ever-evolving landscape of artificial intelligence, Meta has taken a significant leap forward with its release of the Llama 3 family of large language models. Whether you’re a developer looking to integrate this powerful AI into your applications or a researcher exploring the capabilities of Llama 3, this guide will walk you through the process with clarity and ease.

Getting Started with Llama 3

The first step in leveraging Llama 3 is understanding how to get started. Llama 3 comes in various sizes, optimized for different text-generation tasks, making it versatile for commercial and research purposes.

Installation and Setup

To install and use Llama 3, you can utilize either the Hugging Face Transformers library or the original Llama codebase. Here’s how:

Using Transformers

• First, ensure you have the Transformers library and PyTorch installed.
• Use the following code snippet to load Llama 3:

import transformers
import torch

model_id = "meta-llamaMeta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16, "device": "cuda"},
)
messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
terminators = [pipeline.tokenizer.eos_token_id, pipeline.tokenizer.convert_tokens_to_ids("