Update app.py

app.py

@@ -2,10 +2,9 @@ import os
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
-hf_token= os.getenv("hf_token")
-tokenizer = AutoTokenizer.from_pretrained("afrizalha/Sasando-1-25M", token=hf_token)
-tiny = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-25M", token=hf_token)
-tinier = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-7M", token=hf_token)
+tokenizer = AutoTokenizer.from_pretrained("afrizalha/Sasando-1-25M")
+tiny = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-25M")
+tinier = AutoModelForCausalLM.from_pretrained("afrizalha/Sasando-1-7M")
 
 
 desc = """Sasando-1 is a tiny, highly experimental text generator built using the Phi-3 architecture. It comes with two variations of microscopic sizes: 7M and 25M parameters. It is trained on a tightly-controlled Indo4B dataset filtered to only have 18000 unique words. The method is inspired by Microsoft's TinyStories paper which demonstrates that a tiny language model can produce fluent text when trained on tightly-controlled dataset.\n\nTry prompting with two simple words, and let the model continue. Fun examples provided below."""