Unlock the Power of Llama Models: Your Guide to Building with Open AI

Looking to leverage the power of large language models? Llama Models offer an accessible and open-source solution for developers, researchers, and businesses alike. This guide will walk you through understanding, downloading, and running these powerful models to fuel your generative AI projects.

What are Llama Models and Why Should You Care?

Llama models are designed to be a foundational system for the global community. They empower innovation in generative AI.

Here's what makes Llama stand out:

• Open Access: Easy to access cutting-edge LLMs to foster innovation.
• Broad Ecosystem: Downloaded hundreds of millions of times with thousands of community projects and broad platform support.
• Trust & Safety: A comprehensive approach to trust and safety in AI development.

Llama Models: A Quick Comparison

Model	Launch date	Model sizes	Context Length	Tokenizer	Acceptable use policy	License	Model Card
Llama 2	7/18/2023	7B, 13B, 70B	4K	Sentencepiece	Use Policy	License	Model Card
Llama 3	4/18/2024	8B, 70B	8K	TikToken-based	Use Policy	License	Model Card
Llama 3.1	7/23/2024	8B, 70B, 405B	128K	TikToken-based	Use Policy	License	Model Card
Llama 3.2	9/25/2024	1B, 3B	128K	TikToken-based	Use Policy	License	Model Card
Llama 3.2-Vision	9/25/2024	11B, 90B	128K	TikToken-based	Use Policy	License	Model Card

Getting Started: Downloading and Accessing Llama Models

Ready to dive in? Here’s how to download the model weights and tokenizer to start building your AI applications:

1. Visit the Meta Llama website: Head to the Meta Llama website.
2. Accept the license: Make sure to carefully read and agree to the license terms.
3. Wait for approval: Once your request is approved, you'll receive a signed URL via email that gives you access.
4. Install the Llama CLI: Use pip: pip install llama-stack. Start here if you have received an email already
5. List available models: Run llama model list to see the latest Llama models available. For older versions, use llama model list --show-all.
6. Download your chosen model: Execute llama download --source meta --model-id CHOSEN_MODEL_ID and enter the URL when prompted.

Important: These links expire after 24 hours or a certain number of downloads. If you encounter errors, simply re-request the link.

Running Llama Models: A Step-by-Step Guide

Once you've downloaded the models, you'll want to put them to work. Use pip install llama_models[torch] to run the model.

Follow these steps to get your models running:

1. Install Dependencies: Ensure you have the necessary dependencies installed.

2. Run Example Scripts: Navigate to the llama_models/scripts/ directory and run the scripts provided.

3. Chat Completion (Instruct Model):: Use this script with an Instruct (Chat) model

   #!/bin/bash
   
   CHECKPOINT_DIR=~/.llama/checkpoints/Meta-Llama3.1-8B-Instruct
   PYTHONPATH=$(git rev-parse --show-toplevel) torchrun llama_models/scripts/example_chat_completion.py $CHECKPOINT_DIR
   

4. Text Completion (Base Model): For a Base model, update the CHECKPOINT_DIR path and use the script llama_models/scripts/example_text_completion.py.

You can use the above steps on both Llama3 and Llama3.1 series of models.

Scaling Up: Running Large Models with Tensor Parallelism

For larger models, you'll need to leverage tensor parallelism for efficient processing.

Modify your script as follows:

#!/bin/bash

NGPUS=8
PYTHONPATH=$(git rev-parse --show-toplevel) torchrun \
 --nproc_per_node=$NGPUS \
 llama_models/scripts/example_chat_completion.py $CHECKPOINT_DIR \
 --model_parallel_size $NGPUS

For increased flexibility, consider exploring the Llama Stack repository, which offers advanced inference options, including FP8 inference.

Accessing Llama Models via Hugging Face

Llama models are also available on Hugging Face for both transformers and native llama3 formats.

To download weights from Hugging Face:

1. Visit a Model Repo: For example, meta-llama/Meta-Llama-3.1-8B-Instruct.
2. Accept the License: Read and accept the license agreement.
3. Access the Files: Once approved, you'll have access to all Llama 3.1 models and previous versions.

To proceed, click on "Files and versions" tab. To download from command line, run pip install huggingface-hub then;

huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir meta-llama/Meta-Llama-3.1-8B-Instruct

To download and cache weights via the transformer pipeline snippet:

import transformers
import torch

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

pipeline = transformers.pipeline(
 "text-generation",
 model="meta-llama/Meta-Llama-3.1-8B-Instruct",
 model_kwargs={"torch_dtype": torch.bfloat16},
 device="cuda",
)

Installing the Llama Models Package

You can easily install this repository as a package by running: pip install llama-models.

Responsible Use of Llama Models

It’s critical to remember that Llama models are new technology with potential risks. You can find the Responsible Use Guide to guide developers.

Addressing Issues and Questions

Encountering issues? Report them through:

• Model issues: https://github.com/meta-llama/llama-models/issues
• Risky content: developers.facebook.com/llama_output_feedback
• Bugs and security: facebook.com/whitehat/info

For common questions, refer to the FAQ.

By following this guide, you'll be well-equipped to harness the power of Llama Models to build innovative and responsible AI applications. Utilize the long-tail keyword: "Llama models download guide" to further optimize your search.