In this post, we will discuss the architecture of the LLaMA family of models and focus on the modifications that are made on top of the original transformer model. In addition, we will also discuss how the second and third iterations of the model differ from LLaMA 1.

Feb 5, 2025 · This article provides an in-depth exploration of the LLaMA architecture, including its mathematical foundations, architectural innovations (such as rotary positional embeddings), and production-level training code on a small dataset using PyTorch.

Jul 29, 2023 · The LLaMA-2 paper describes the architecture in good detail to help data scientists recreate & fine-tune the models. (unlike OpenAI papers where you have to deduce it indirectly).

Oct 23, 2023 · In this article, which is the first in this series, I’ll guide you through the process of calculating the parameter count for the Llama-2–13B model. We’ll take a two-fold approach: first,...

Aug 29, 2024 · Large Language Models like Llama 3.1 are powerful, yet understanding their inner workings can be complex, especially when theory becomes disconnected from practical application. In this deep...

Meta developed and released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases.

Sep 1, 2024 · You’ll get an in-depth intuition of how each component of the Llama 3 model works under the hood. You’ll write codes to build each component of Llama 3 and then assemble them all together to build a fully functional Llama 3 model. You’ll also write codes to train your model with new custom datasets.

This project aims to optimize LLaMA model for visual information understanding like GPT-4 and further explore the potentional of large language model. Generally, we use CLIP vision encoder to extract image features, then image features are projected with MLP-based or Transformer-based connection network into text embedding dimensionality.

Explore the Llama architecture with an in-depth guide on its structure, key components, and design principles, helping you understand its powerful capabilities.

Apr 10, 2024 · In Llama, each token of the input embedding is represented by a vector of varying dimensions depending on the model size. Specifically, for the 6B parameter model, each token is represented by a 4096 dimensional vector. For the 13B …