From 7887133145df51ecd4648c1f14bae837dc6158ce Mon Sep 17 00:00:00 2001
From: Som <96942390+som-sama@users.noreply.github.com>
Date: Thu, 27 Jul 2023 09:26:20 +0530
Subject: [PATCH] Center align cute llama image in README

Added HTML code to center align the cute llama image in the README file. The image is wrapped in a <p> tag with the "align" attribute set to "center" for proper alignment. Additionally, an "alt" attribute is included for accessibility.
---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3490785..c37d6b6 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,8 @@
 ## llama2.c
 
-<img src="assets/llama_cute.jpg" width="300" height="300">
+<p align="center">
+  <img src="assets/llama_cute.jpg" width="300" height="300" alt="Cute Llama">
+</p>
 
 With the code in this repo you can train the Llama 2 LLM architecture from scratch in PyTorch, then export the weights to a binary file, and load that into one ~simple 500-line C file ([run.c](run.c)) that inferences the model. Alternatively, you can load, finetune, and inference Meta's Llama 2 (but this is still being actively fleshed out). Hence, this repo is a "fullstack" train + inference solution for Llama 2 LLM, with a focus on minimalism and simplicity. You might think that you need many billion parameter LLMs to do anything useful, but in fact very small LLMs can have surprisingly strong performance if you make the domain narrow enough. I recommend looking at the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) paper for inspiration.