September 17, 2024
Meta, like many other leading tech companies, has developed its own generative AI model known as Llama. What sets Llama apart is its open nature, allowing developers to download and use it with considerable freedom, though there are some conditions to keep in mind. This approach contrasts with models like Anthropic’s Claude, OpenAI’s GPT-4o (the engine behind ChatGPT), and Google’s Gemini, which are available solely through APIs.
To give developers more flexibility, Meta has partnered with cloud service providers such as AWS, Google Cloud, and Microsoft Azure to offer cloud-hosted versions of Llama. Additionally, Meta has built tools that simplify the process for developers to fine-tune and adapt the model to meet their specific requirements.
Below is a detailed description of Llama, including what it can do, the different versions it offers, and how you can use it effectively.
What is Llama?
Llama is not merely one model; it encompasses a range of models, such as:
- Llama 8B
- Llama 70B
- Llama 405B
The most recent versions (Llama 3.1 8B, Llama 3.1 70B, and Llama 3.1 405B) were released in July 2024. These models are trained on a wide range of data sources, such as web pages in different languages, public code, files available online, and synthetic data produced by other AI systems.
The Llama 3.1 8B and 70B are compact models that can run a variety of devices from laptops to servers. The Llama 3.1 405B, on the other hand, is a large-scale model that typically requires data center hardware. While the smaller models may not be as powerful as the 405B, they offer faster performance and are optimized for reduced storage and latency.
All Llama models feature a context window of 128,000 tokens, allowing them to handle around 100,000 words (or 300 pages of text). This is roughly the length of books like Wuthering Heights or Harry Potter and the Prisoner of Azkaban. A long context window helps the model retain information from recent documents and data, reducing the risk of straying off-topic.
What Can Llama Do?
Like other generative AI models, Llama can help with a variety of tasks, such as coding, answering simple math questions and summarizing documents in eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish and Thai. It can handle most text-based tasks, such as analyzing files like PDFs and spreadsheets, but it currently doesn’t have the ability to generate or process images — something that could change in the future.
Llama models can integrate with third-party apps, tools, and APIs to perform a variety of tasks. They are configured to use Brave Search to answer questions about current events, the Wolfram Alpha API for math and science queries, and a Python interpreter for code validation. According to Meta, Llama 3.1 can even use some tools it hasn’t been explicitly trained on, though the reliability of this feature is still uncertain.
Where Can You Use Llama?
If you want to connect directly with Llama, the Meta AI chatbot experience is supported on platforms like Facebook Messenger, WhatsApp, Instagram, Oculus, and Meta.ai.
For developers, Llama is available for download and can be scaled to several popular cloud platforms. Meta has partnered with over 25 companies to host Llama, including Nvidia, Databricks, Groq, Dell, and Snowflake. Many of these partners provide additional tools that allow Llama to access proprietary data or run more efficiently.
Meta suggests using the smaller Llama models, specifically the 8B and 70B, for general purposes such as running chatbots or creating code. On the other hand, the larger Llama 405B is more appropriate for tasks such as model distillation, which involves transferring knowledge from a larger model to a smaller one, as well as generating synthetic data to train other models.
Developers operating applications with more than 700 million monthly users must acquire a special license from Meta to access Llama, and this will be granted at Meta's discretion.
What Tools Does Meta Offer for Llama?
Meta has introduced several tools to improve Llama's security:
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Llama Guard: A moderation framework that identifies problematic content such as hate speech, self-harm, and copyright infringement.
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Prompt Guard: A tool specifically designed to protect Llama from harmful messages that seek to bypass its security measures.
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CyberSecEval: A cybersecurity risk assessment suite that evaluates model security, focusing on threats such as automated social engineering and offensive cyber activities.
For example, Llama Guard can detect harmful or illegal content that is entered or generated into Llama, allowing developers to customize which categories are blocked. Prompt Guard focuses on defending against "message injection attacks" that attempt to manipulate the model. CyberSecEval offers benchmarks for assessing security risks associated with Llama models.
Llama’s Limitations
Llama, like other generative AI models, has its limitations and potential risks. One of the main concerns is whether Meta used copyrighted materials to train Llama. If that is the case, users could face liability for any copyrighted content the model generates.
Recent reports indicate that Meta has been using copyrighted e-books for AI training, despite having received legal warnings. The company also incorporates content from Instagram and Facebook into its model training, making it difficult for users to opt-out. Meta is facing multiple lawsuits, including one from authors such as Sarah Silverman, who claim that the company has used copyrighted material without permission.
An important consideration is programming, as Llama could produce buggy or unsafe code. It is critical that developers have a human expert review any AI-generated code before implementing it in their applications.
While Meta's Llama model offers considerable flexibility and opportunities for developers, it is important to recognize the potential risks and limitations that come with it.
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