Using "facebook/blenderbot-400M-distill" model

app.py

@@ -1,20 +1,20 @@
 
 from transformers import pipeline
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
 import gradio as gr
 import torch
 
 # Use a pipeline as a high-level helper
 
-pipe = pipeline("conversational", model="PygmalionAI/pygmalion-6b")
+pipe = pipeline("conversational", model="facebook/blenderbot-400M-distill")
 
 title = "🤖AI ChatBot"
 description = "Building open-domain chatbots is a challenging area for machine learning research."
 examples = [["How are you?"]]
 
 
-tokenizer = AutoTokenizer.from_pretrained("PygmalionAI/pygmalion-6b")
-model = AutoModelForCausalLM.from_pretrained("PygmalionAI/pygmalion-6b")
+tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
 
 
 def predict(input, history=[]):