Update app.py

app.py

@@ -108,16 +108,16 @@ def evaluate(
         )
     s = generation_output.sequences[0]
     output = tokenizer.decode(s)
-    return output.split("### Response:")[1].strip()
+    return output.split("### Response:")[1].strip().replace('</s>', '')
 
 
 g = gr.Interface(
     fn=evaluate,
     inputs=[
         gr.components.Textbox(
-            lines=2, label="Instruction", placeholder="Tell me about alpacas."
+            lines=2, label="Instruction", placeholder="例：日本語から英語に翻訳してください。"
         ),
-        gr.components.Textbox(lines=2, label="Input", placeholder="none"),
+        gr.components.Textbox(lines=2, label="Input", placeholder="天気がいいから、散歩しましょう。"),
         gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature"),
         gr.components.Slider(minimum=0, maximum=1, value=0.75, label="Top p"),
         gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k"),
@@ -132,8 +132,11 @@ g = gr.Interface(
             label="Output",
         )
     ],
-    title="🦙🌲 Alpaca-LoRA",
-    description="Alpaca-LoRA is a 7B-parameter LLaMA model finetuned to follow instructions. It is trained on the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) dataset and makes use of the Huggingface LLaMA implementation. For more information, please visit [the project's website](https://github.com/tloen/alpaca-lora).",
+    title="Llama2_7b_chat_Japanese_Lora",
+    description="Llama-2-7b-chat-Japanese-LoRA is a multi-purpose large language model for Japanese text.\n\
+        This model is presented by the joint effort of Sparticle Inc. and A. I. Hakusan Inc.\n\n\
+        Llama-2-7b-chat-Japanese-LoRAは日本語テキストのための多目的大規模言語モデルです。\n\
+        このモデルは、Sparticle株式会社と株式会社白山人工知能の共同開発により発表されました。",
 )
 g.queue(concurrency_count=1)
 g.launch()