Meta, formerly known as Facebook, has introduced its own flagship generative AI model called Llama. Unlike other major models in the tech industry, Llama is “open,” allowing developers to download and utilize it freely, within certain limitations. However, Meta has also partnered with cloud vendors like AWS, Google Cloud, and Microsoft Azure to offer cloud-hosted versions of Llama to users.
Llama is not just one model but a family of models, including Llama 8B, Llama 70B, and Llama 405B. The latest versions of Llama, such as Llama 3.1 8B, Llama 3.1 70B, and Llama 3.1 405B, were released in July 2024. These models are trained on various data sources, including web pages, public code, files on the web, and synthetic data generated by other AI models.
Llama models have specific capabilities, such as coding, answering math questions, and summarizing documents in multiple languages. They can analyze text-based workloads like PDFs and spreadsheets but are currently unable to process images. Llama models can be configured to use third-party apps, tools, and APIs for completing tasks, such as Brave Search for answering questions and Wolfram Alpha API for math-related queries.
Developers can download, use, and fine-tune Llama models on popular cloud platforms with the help of over 25 partner companies hosting Llama, including Nvidia, Databricks, and Dell. However, developers with more than 700 million monthly users must request a special license from Meta to deploy the model. The licensing terms restrict developers in certain ways, imposing limitations on large-scale deployment.
Risks and Limitations of Llama
Despite its capabilities, Llama comes with certain risks and limitations, similar to other generative AI models in the market. One major concern is the uncertainty around whether Meta trained Llama on copyrighted content, potentially making users liable for copyright infringement unknowingly. The controversial use of Instagram and Facebook data for training purposes has raised ethical and legal issues surrounding user consent and data privacy.
Additionally, there are risks associated with the output generated by Llama, especially in programming contexts. The model may produce buggy or insecure code, emphasizing the importance of human oversight in reviewing AI-generated code before integration into services or software.
Meta has introduced tools like Llama Guard, Prompt Guard, and CyberSecEval to enhance the safety and security of using its Llama models. Llama Guard detects problematic content, Prompt Guard protects against malicious inputs, and CyberSecEval assesses model security risks. These tools aim to mitigate potential risks associated with using generative AI models like Llama.
While Meta’s Llama generative AI model offers a range of capabilities for developers and users, it also presents risks and limitations that must be carefully considered. From copyright concerns to programming risks, understanding the implications of using Llama is crucial in ensuring responsible and ethical deployment of AI technologies in various applications. By acknowledging these challenges and implementing appropriate safeguards, developers can leverage the potential of Llama while minimizing associated risks.