Llama - Introduction

What is Llama?

Llama is a family of Large Language Models developed by Meta AI team. Main aim of Llama is to increase the use of large language models by reducing the massive hardware and computational costs typically required for training and deploying such models.

Key Aspects of Llama Language model

• LLM, Large Language Model − Prime focus of Llama model is to understand, generate and process natural languages. Llama models are trained on vast amount of data to learn grammer, patterns, facts and reasoning abilities.

• Transformer Architecture − Similar to modern LLMs, Llama models are build upon transformer architecture, a decoder only based autoregressive model. For example, to predict next word based on preceding words.

• Developed by Meta AI − Llama is developed and maintained by Meta AI's Artifical Intelligence Research Division.

Evolutions of Llama models

• Llama 1 − Llama was initially releaased in Feb 2023, to demonstrate that smaller models can achieve comparable results as compared to other competitior like GPT-3 when trained on more data for longer periods.

• Llama 2 − In Jul 2023, Llama 2 was released to push further efficiency and performance. Meta AI released this version with less restrictive license so that it is more broadly accessible similarly to open source projects. Llama 2 contained base models as well as fine tuned instructed versions.

• Code Llama − Aug 2023, saw another release of Llama, specifically for code genearation and better understanding of codes.

• Llama 3 − Llama 3, released in Apr 2024 was a major release with improved reasoning and performance.

• Llama 3.1 − Llama 3.1, released in Jul 2024 came up with multilingual capabilities, a bigger context window and a very large 405 billion parameter model. This model focus was to compete with proprietary model while being openly availble.

• Llama 4 − Llama 4 is a the latest version released in Apr 2025, is having multimodel capabilities. It can understand and generate both text and images and is highly performant in understanding images and text.

Openly Available

Llama series is openly available to foster a development ecosystem. Although is not completely open source as a company may require a license from Meta if it exceeds a certain number of active monthly users. But its code is broadly accessible by the researchers and developers.

Technical Characteristics

• SwiGLU Activation Function −Llama employs SwiGLU as alternate activation function as compared to standard GeLU function used in popular transformers.

• Rotary Positional Embeddings (RoPE) − Llama allows model to learn dynamic positional representations instead of absolute positional embeddings.

• RMSNorm − It is a normalization technique different from normally used layer normalization techniques.

• Key-Value (KV) Cache and Grouped Multi-Query Attention − An optimization by reuse of keys and values vectors across multiple steps, leading to faster inferences.

Applications of Llama Models

Llama models can be fine tuned to accomplice many kinds of NLP tasks. Some of the applications are listed below −

• Writing Reports, creating creative contents

• Translation to many languages

• Automatic answering of questions, Chatbots

• Autocompletion of text

• Code generation, code review

• Create smaller, specialized models