Clairo AI’s platform offers users unfettered access to the most prominent, state-of-the-art Large Language Models (LLMs) that the industry has to offer. In the first article of Clairo’s LLM Series, we will dive into Meta’s LLaMA models. LLaMA models are available at several sizes, ranging from smaller, more efficient models like 7B, to extra-large models with billions of parameters, such as the 70B, and coming-soon 400B model, all designed to produce human-like, nuanced responses to user questions.
What architecture are the LLaMA models built on?
The LLaMA models are built on transformer architecture, a foundational model design in the field of natural language processing (NLP) and machine learning and the backbone for many LLMs. Transformers are a neural network architecture that analyse large quantities to make predictions and also generate content. Transformers work by converting an input sequence into an output sequence by learning the context and relationships among the elements within those sequences.
Transformers tend to have many layers and each layer contains two main parts: the self-attention mechanism, and the feed-forward network. The self-attention mechanism is the area where each word in a sentence is compared to every other word, determining the importance of each word and how much attention it should be receiving by the model. The feed-forward network is where the information gleaned by the self-attention mechanism is processed, allowing the model to make necessary predictions about the next word in the sentence.
What data are LLaMA models trained on?
LLaMA models are trained on a diverse corpus of text and training data from the internet, covering multiple domains to ensure comprehensive language understanding, such as scientific papers. These training materials are specifically designed to generate very technical responses for users.
When training a top-notch language model, the quality and size of the training dataset are crucial. For LLaMA models, curating a massive, high-quality dataset should be a priority. For instance, the smaller LLaMA models, like LLaMA 7B, are trained on an impressive one trillion tokens, while the larger models, such as LLaMA 65B and 33B, are trained on a staggering 1.4 trillion tokens. But what really sets the latest LLaMA3 models apart is that they're trained on 15 trillion tokens, all on publicly available sources. This massive dataset is seven times larger than the one used for LLaMA2 and includes four times more code.
Moreover, Meta AI ensured that over 5% of the LLaMA3 pre-training dataset consists of high-quality non-English data, covering more than 30 languages, to prepare for future multilingual use cases. While it is not expected to have the same level of performance in these languages as in English, the models are designed to excel in a wide range of linguistic contexts.
What are the benefits of LLaMA models for businesses?
LLaMA’s smaller sizes make their models some of the most efficient and accessible in the industry. Companies whose tasks require little computational resources, or even fast inference times, may opt for a LLaMA model due to its lightweight structure. Yet, the wide range of sizes also make these models scalable. Users are not confined to one model size and are able to adjust it accordingly as their use cases develop.
What are the limitations of LLaMA models?
The benefits of LLaMA’s models are also what make it a less powerful model.
- Despite their efficiency, LLaMA models lack some of the computational power and parameters of other models in the industry, and might be constrained when making certain predictions.
- Latency-sensitive applications, like chatbots or systems that require real-time conversational outputs, may not be the best use case for a larger LLaMA model, due to longer inference times.
- LLaMA models require such huge computational resources in the form of GPUs and TPUs, that maintenance can be costly, resource-intensive, and consume large amounts of energy, making them unsustainable. However, Clairo AI’s commitment to environmental practices is pioneering in the emerging realm of Green AI, aiming to mitigate the environmental footprint of these high-intensity models.
What specific business tasks can LLaMA be used for?
Due to its wide range of sizes, and the vast quantity of data its models are trained on, LLaMA can be beneficial to businesses across a variety of sectors and business cases. Due to its processing speed and accuracy, LLaMA could be used for:
- Text summarisation tasks designed to extract key information from documents, contracts, articles, and other text-heavy works
- Language translation tools for businesses with global audiences and clientele
- Content-generation and high-quality text production, such as for marketing copy, blog or news articles
More broadly, LLaMA models are increasingly being adopted in the healthcare and legal industries. In healthcare, LLaMA is being integrated for day-to-day tasks, such as clinical documentation, medical transcription, as well as in more groundbreaking processes. Vast patient data analysis is crucial in identifying patterns in ongoing research studies, leading to a better understanding of illnesses and their diagnoses.
On the other hand, LLaMA can automate the more mundane processes in the legal industry, like processing and evaluating legal documents and reviewing contracts. Due to its sharp ability to summarise complex text, LLaMA has the unique ability to analyse nuance and parse legal documents for misplaced opinion, where objectivity should be upheld, ensuring contract and document compliance.
Like with any LLM, the data privacy concerns associated with LLaMA models are real and should be taken seriously when deploying new models into any business. Clairo AI prioritises the security and integrity of all data imported into the platform, thus mitigating these concerns and ensuring our users can integrate LLaMA’s capabilities into their business operations with ease and peace of mind.
