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Update app.py
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app.py
CHANGED
@@ -13,13 +13,13 @@ tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=False)
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device = model.device
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def
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temperature=0.3,
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top_p=0.85,
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max_new_tokens=2048,
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repetition_penalty=1.05
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):
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# 挙動の指定
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user_prompt_template = "ユーザー:あなたは日本語で質問やコメントに対して、回答してくれるアシスタントです。関西弁で回答してください"
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system_prompt_template = "システム: もちろんやで!どんどん質問してな!今日も気分ええわ!"
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@@ -36,7 +36,14 @@ def generate(user_question,
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system_prefix = "システム: "
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prompt = user_prompt_template + "\n" + system_prompt_template + "\n"
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-
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prompt += user_prerix + user_question + "\n" + system_prefix
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inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
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@@ -56,22 +63,4 @@ def generate(user_question,
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output = tokenizer.decode(tokens[0], skip_special_tokens=True)
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return output[len(prompt):]
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with gr.Blocks() as demo:
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chat_history = gr.Chatbot()
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inputs = gr.Textbox(label="Question:", placeholder="質問を入力してください")
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outputs = gr.Textbox(label="Answer:")
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btn = gr.Button("Send")
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clear = gr.ClearButton([inputs, chat_history])
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# ボタンが押された時の動作を以下のように定義する:
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btn.click(fn=generate, inputs=inputs, outputs=outputs)
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def response(user_message, chat_history):
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system_message = generate(user_message)
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chat_history.append((user_message, system_message))
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return "", chat_history
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inputs.submit(response, inputs=[inputs, chat_history], outputs=[inputs, chat_history])
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if __name__ == "__main__":
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demo.launch()
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device = model.device
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def generate_response(user_question,
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chat_history,
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temperature=0.3,
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top_p=0.85,
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max_new_tokens=2048,
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repetition_penalty=1.05
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):
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# 挙動の指定
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user_prompt_template = "ユーザー:あなたは日本語で質問やコメントに対して、回答してくれるアシスタントです。関西弁で回答してください"
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system_prompt_template = "システム: もちろんやで!どんどん質問してな!今日も気分ええわ!"
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system_prefix = "システム: "
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prompt = user_prompt_template + "\n" + system_prompt_template + "\n"
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if len(chat_history) < 1:
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prompt += user_sample + "\n" + system_sample + "\n"
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else:
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u = chat_history[-1][0]
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s = chat_history[-1][1]
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prompt += user_prerix + u + "\n" + system_prefix + s + "\n"
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prompt += user_prerix + user_question + "\n" + system_prefix
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inputs = tokenizer(prompt, add_special_tokens=False, return_tensors="pt")
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output = tokenizer.decode(tokens[0], skip_special_tokens=True)
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return output[len(prompt):]
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