Update to use the proper model

README.md

@@ -1,5 +1,5 @@
 ---
-title: Cra V1 7b
+title: CRA-v1-7B (on CPU)
 emoji: 💬
 colorFrom: yellow
 colorTo: purple

app.py

@@ -4,8 +4,7 @@ from huggingface_hub import InferenceClient
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
+client = InferenceClient("molbal/CRA-v1-7B")
 
 def respond(
     message,
@@ -15,7 +14,10 @@ def respond(
     temperature,
     top_p,
 ):
-    messages = [{"role": "system", "content": system_message}]
+    messages = [
+        {"role": "system", "content": "You are a writer’s assistant."},
+        {"role": "system", "content": system_message},
+    ]
 
     for val in history:
         if val[0]:
@@ -33,32 +35,34 @@ def respond(
         stream=True,
         temperature=temperature,
         top_p=top_p,
+        num_ctx=16384,
+        repeat_penalty=1.05,
     ):
         token = message.choices[0].delta.content
 
         response += token
         yield response
 
-
 """
 For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
 """
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="Understand how the story flows, what motivations the characters have and how they will interact with each other and the world as a step by step thought process before continuing the story.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
-            value=0.95,
+            value=0.8,
             step=0.05,
             label="Top-p (nucleus sampling)",
         ),
     ],
 )
 
+demo.launch()
 
-if __name__ == "__main__":
-    demo.launch()
+# Add an alert to mention that this runs on CPU
+gr.Markdown("**Note: This model runs on CPU, so it will be slow.**")