diff --git a/README.md b/README.md
index f9802b6..515e43f 100644
--- a/README.md
+++ b/README.md
@@ -20,8 +20,8 @@ Nesa: Run AI models end-to-end encrypted.
 </h3>
 
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+[![GitHub Repo stars](https://img.shields.io/github/stars/nesaorg/nesa)](https://github.com/nesaorg/nesa/stargazers)
+[![GitHub Repo forks](https://img.shields.io/github/forks/nesaorg/nesa)](https://github.com/nesaorg/nesa/network/members)
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 </div>
 
@@ -36,10 +36,10 @@ Forget multi-million dollar on-prem infrastructure, get the same privacy guarant
 <table border="0">
  <tr>
     <td><b style="font-size:30px">Full Privacy</b></td>
-    <td>nesa serves AI with zero visibility on underlying data and full blindness on query</b></td>
+    <td>nesa serves AI with zero visibility on underlying data and full blindness on query</td>
  </tr>
  <tr>
-    <td><b style="font-size:30px">Speedy </td>
+    <td><b style="font-size:30px">Speedy</b></td>
     <td>nesa delivers no latency on encrypted inference (<0.1% original execution time)</td>
  </tr>
 <tr>
@@ -53,11 +53,11 @@ Forget multi-million dollar on-prem infrastructure, get the same privacy guarant
  </tr>
 <tr>
     <td><b style="font-size:30px">ChatGPT Compatible</b></td>
-    <td>nesa has a ChatGPT-compatible API for running encrypted AI with a one line change</td>
+    <td>nesa has a ChatGPT-compatible API for running encrypted AI with a one-line change</td>
  </tr>
  <tr>
     <td><b style="font-size:30px">Quick Set-up</b></td>
-    <td>nesa is one click install and go. See documentation</td>
+    <td>nesa is one-click install and go. See documentation</td>
  </tr>
 </table>
 
@@ -141,7 +141,7 @@ Using a state-of-the-art large language model such as GPT-4o to evaluate whether
 
 ### Linguistic Domain Knowledge Attack
 
-Using domain knowledge to design the loss function L, so that the loss L can capture the semantic meaning in the (decrypted) input, output and between.
+Using domain knowledge to design the loss function L, so that the loss L can capture the semantic meaning in the (decrypted) input, output, and the relationship between them.
 
 ### Brute-force Algorithm Attack
 
@@ -180,7 +180,7 @@ Under the hood, the text you type is turned into encrypted tokens, the model pro
 
 If you’d like to peek under the hood, below are quick examples demonstrating how to load the models directly from Hugging Face and run basic inferences.
 
-##### Distillbert
+##### DistilBERT
 
 ```python
 import torch
@@ -245,4 +245,4 @@ Decoded Text: I'm super excited to join Nesa's Equivariant Encryption initiative
 We invite the community to examine and test the security claims of Equivariant Encryption. As part of our commitment to transparency and continual refinement, we have organized a competition encouraging participants to probe for weaknesses and demonstrate potential exploits.
 
 For details, please visit:
-[https://github.com/nesaorg/Equivariant-Encryption-for-AI/blob/main/CONTEST.md](https://github.com/nesaorg/Equivariant-Encryption-for-AI/blob/main/CONTEST.md)
+[https://github.com/nesaorg/nesa/blob/main/CONTEST.md](https://github.com/nesaorg/nesa/blob/main/CONTEST.md)
diff --git a/demo/docs/tokenize.md b/demo/docs/tokenize.md
index 67a51eb..e99eb57 100644
--- a/demo/docs/tokenize.md
+++ b/demo/docs/tokenize.md
@@ -15,7 +15,7 @@ from transformers import AutoTokenizer
 
 hf_token = "<HF TOKEN>"  # Replace with your token
 model_id = "nesaorg/Llama-3.2-1B-Instruct-Encrypted"
-tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, local_files_only=True)
+tokenizer = AutoTokenizer.from_pretrained(model_id, token=hf_token, local_files_only=False)
 ```
 
 ### Tokenize and Decode Text
diff --git a/demo/readme.md b/demo/readme.md
index 6c647d7..bc2a312 100644
--- a/demo/readme.md
+++ b/demo/readme.md
@@ -33,7 +33,6 @@ This folder contains a modified version of [oobabooga/text-generation-webui](htt
 
 4.	After installation completes, it should automatically launch a local client, or you can manually navigate to http://localhost:7860.
 
-Usage
 ## Usage
 We automatically download and select a model for the user, so they can start using right away after the local client loads. We've included information about switching between both demo models below: