add citation

paper.bib

@@ -22,4 +22,11 @@ @article{gradio
   author = {Abid, Abubakar and Abdalla, Ali and Abid, Ali and Khan, Dawood and Alfozan, Abdulrahman and Zou, James},
   journal = {arXiv preprint arXiv:1906.02569},
   year = {2019},
+}
+
+@article{llama3,
+  title={Llama 3 Model Card},
+  author={AI@Meta},
+  year={2024},
+  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
 }

paper.md

@@ -36,7 +36,7 @@ textwiz-memory llama3-8B 1122 512
 
 # State of the field
 
-The most commonly used Python package for using LLMs is `transformers` [@transformers]. However, as explained, every model usually has its own implementation/usage details, making it hard to run the same text generation pipeline out-of-the-box for different models. Common pitfalls are also the responsability of the users to navigate. `TextWiz` goes one big step beyond, in order to provide one common interface and alleviate all common issues. It is also much simpler to use for beginners. Here is a very simple code snippet required to have a conversation with  using `transformers`:
+The most commonly used Python package for using LLMs is `transformers` [@transformers]. However, as explained, every model usually has its own implementation/usage details, making it hard to run the same text generation pipeline out-of-the-box for different models. Common pitfalls are also the responsability of the users to navigate. `TextWiz` goes one big step beyond, in order to provide one common interface and alleviate all common issues. It is also much simpler to use for beginners. Here is a very simple code snippet required to have a conversation with Llama3 [@llama3] using `transformers`:
 
 ```py
 from transformers import AutoTokenizer, AutoModelForCausalLM