welcome formatting

demo_watermark.py

@@ -482,19 +482,11 @@ def run_gradio(args, model=None, device=None, tokenizer=None):
                             """
                             This demo uses open-source language models. Today, these models are less powerful than proprietary commercial tools like ChatGPT, Claude, Bard, or Bing/Sydney. 
 
-                            Models like [BLOOM (175B)](https://huggingface.co/bigscience/bloom) are designed to "complete" your prompt, and are not fine-tuned to follow instructions. 
-                            For best results, prompt that model with a few sentences that form the beginning of a paragraph, and then allow it to "continue" your paragraph. 
-                            Some examples include the opening paragraph of a wikipedia article, or the first few sentences of a story. 
-                            Longer prompts that end mid-sentence will result in more fluent generations.
-
-                            Some of the models available in this demo are fine-tuned to follow instructions but have different strengths and will showcase different
-                            types of watermark behavior. [BLOOMZ](https://huggingface.co/bigscience/bloomz) is an instruction tuned variant of BLOOM capable of following instructions in dozens of languages zero-shot 
-                            and can generate long and coherent paragraphs and stories given the right prompt.
-                            The FLAN models [FLAN-t5-xxl (11B)](https://huggingface.co/google/flan-t5-xxl) and [FLAN-UL2 (20B)](https://huggingface.co/google/flan-ul2) are fine-tuned on a variety of in-context few-shot learning NLP tasks, 
-                            such as reasoning, and question answering.
-
-                            Generally, short, low entropy scenarios where the model has very few choices in terms of correct/suitable responses to the prompt 
-                            will not exhibit as strong of a watermark presence, while longer watermarked outputs will produce higher detection statistics.
+                            Models like [BLOOM (175B)](https://huggingface.co/bigscience/bloom) are designed to "complete" your prompt, and are not fine-tuned to follow instructions. For best results, prompt that model with a few sentences that form the beginning of a paragraph, and then allow it to "continue" your paragraph. Some examples include the opening paragraph of a wikipedia article, or the first few sentences of a story. Longer prompts that end mid-sentence will result in more fluent generations.
+
+                            Some of the models available in this demo are fine-tuned to follow instructions but have different strengths and will showcase differenttypes of watermark behavior. [BLOOMZ](https://huggingface.co/bigscience/bloomz) is an instruction tuned variant of BLOOM capable of following instructions in dozens of languages zero-shot and can generate long and coherent paragraphs and stories given the right prompt. The FLAN models [FLAN-t5-xxl (11B)](https://huggingface.co/google/flan-t5-xxl) and [FLAN-UL2 (20B)](https://huggingface.co/google/flan-ul2) are fine-tuned on a variety of in-context few-shot learning NLP tasks, such as reasoning, and question answering.
+
+                            Generally, short, low entropy scenarios where the model has very few choices in terms of correct/suitable responses to the prompt will not exhibit as strong of a watermark presence, while longer watermarked outputs will produce higher detection statistics.
                             """
                             )
                     gr.Markdown(