""" """ import random import config from app_util import * user_simulator_pre_doc = """\ You are a helpful assistant, and the agent acts as user. """ user_simulator_post_doc = """\ ## How does it work? There are maily two types of user simulator: - prompt-based user-simulator (role-play) - model-based user-simulator This demo is a model-based user simulator. """ # In most cases, large language models (LLMs) are used to serve as assistant generator. # Besides, it can also used as user simulator. assistant_simulator_pre_doc = """\ You are a user, and the agent acts as assistant. """ assistant_simulator_post_doc = """\ """ self_chat_pre_doc = """\ Self-chat is a demo which make the model talk to itself. Dual-agent. """ self_chat_post_doc = """\ ## How does it work? It is a combination of user simulator and response generator. """ survey = """\ ## knowledge distillation 知识蒸馏 Essentially, it is a form of model compression. ## distilling knowledge != knowledge distillation 知识的形式可以是 QA纯文本，也可以是 QA+概率。 ## 有不用概率的知识蒸馏吗？ """ gr.set_static_paths(paths=["assets/"]) """
user avatar
""" css=""" .image_center { display: block; margin: auto; } """ with gr.Blocks(head=None, css=css) as demo: # Knowledge Distillation through Self Chatting # Distilling the Knowledge from LLM through Self Chatting # Generating Synthetic Data through Self Chat gr.HTML("""

Generating Synthetic Data via Self-Chatting

""") with gr.Row(): with gr.Column(scale=5): system = gr.Dropdown( choices=system_list, # value=system_list[0], allow_custom_value=True, interactive=True, label="System message", scale=5, ) chatbot = gr.Chatbot(show_copy_button=True, show_share_button=True, # avatar_images=("assets/man.png", "assets/bot.png"), avatar_images=("assets/man.png", "assets/女客服.png"), likeable=True) # gr.Textbox("For faster inference, you can build locally with ") # ss with gr.Tab("Self Chat") as tab_dual_agent: gr.Markdown(self_chat_pre_doc) input_text_1 = gr.Textbox(show_label=False, placeholder="...", lines=10, visible=False) generate_btn = gr.Button("🤖Self-Chat🤖", variant="primary") with gr.Row(): retry_btn = gr.Button("🔄 Regenerate", variant="secondary", size="sm") undo_btn = gr.Button("↩️ Undo", variant="secondary", size="sm") # clear_btn = gr.Button("🗑️ Clear", variant="secondary", size="sm") clear_btn = gr.Button("🧹 Clear History", variant="secondary", size="sm") gr.Markdown(self_chat_post_doc) # 也叫 chat-assistant, 🎧，🤖 ，💁， with gr.Tab("Response Generator") as tab_assistant_agent: gr.Markdown(assistant_simulator_pre_doc) with gr.Row(): # gr.HTML( # value='
Big Boat
', # elem_classes=["image_center"] # ) gr.Image("assets/man.png", interactive=False, show_download_button=False, width=40, height=40, min_width=40, show_share_button=False, show_fullscreen_button=False, container=False, elem_classes=["image_center"]) input_text_2 = gr.Textbox(show_label=False, lines=2, placeholder="Please type user input", container=False, scale=12) generate_btn_2 = gr.Button("Send", variant="primary", min_width=80) with gr.Row(): retry_btn_2 = gr.Button("🔄 Regenerate", variant="secondary", size="sm", ) undo_btn_2 = gr.Button("↩️ Undo", variant="secondary", size="sm", ) clear_btn_2 = gr.Button("🧹 Clear History", variant="secondary", size="sm") gr.Markdown(assistant_simulator_post_doc) # with gr.Tab("User Simulator") as tab_user_agent: # 👨，🔊， gr.Markdown(user_simulator_pre_doc) with gr.Row(): # gr.HTML(value='
user avatar
') # gr.Image("assets/女客服.jpg", gr.Image("assets/女客服.png", # gr.Image("assets/男客服.png", interactive=False, show_download_button=False, width=40, height=40, min_width=40, show_share_button=False, show_fullscreen_button=False, container=False, elem_classes=["image_center"]) input_text_3 = gr.Textbox(show_label=False, lines=2, placeholder="Please type assistant response", container=False, scale=12) generate_btn_3 = gr.Button("Send", variant="primary", min_width=80) with gr.Row(): retry_btn_3 = gr.Button("🔄 Regenerate", variant="secondary", size="sm") undo_btn_3 = gr.Button("↩️ Undo", variant="secondary", size="sm") # clear_btn_3 = gr.Button("🗑️ Clear", variant="secondary", size="sm") clear_btn_3 = gr.Button("🧹 Clear History", variant="secondary", size="sm") # 🧹 Clear History (清除历史) gr.Markdown(user_simulator_post_doc) with gr.Column(variant="compact", scale=1, min_width=300): # with gr.Column(): model = gr.Dropdown( ["Qwen2-0.5B-Instruct", "llama3.1", "gemini", "MiniCPM3-4B"], value="Qwen2-0.5B-Instruct", label="Model", interactive=True, # visible=False ) with gr.Accordion(label="Parameters", open=True): slider_max_new_tokens = gr.Slider(minimum=1, maximum=4096, value=config.DEFAULT_MAX_NEW_TOKENS, step=1, label="Max New tokens") slider_temperature = gr.Slider(minimum=0.1, maximum=10.0, value=config.DEFAULT_TEMPERATURE, step=0.1, label="Temperature", info="Larger temperature increase the randomness") slider_top_p = gr.Slider( minimum=0.1, maximum=1.0, value=config.DEFAULT_TOP_P, step=0.05, label="Top-p (nucleus sampling)", ) slider_top_k = gr.Slider( minimum=1, maximum=200, value=config.DEFAULT_TOP_K, step=1, label="Top-k", ) # TODO: gr.State 不能通过API传参。 gr_false = gr.State(False) history = gr.State([{"role": "system", "content": system_list[0]}]) # 有用信息只有个system，其他和chatbot内容重叠 system.change(reset_state, inputs=[system], outputs=[chatbot, history]) ######## tab1: self-chat generate_btn.click(chat, [chatbot, history], outputs=[chatbot, history], show_progress="full") retry_btn.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) \ .then(chat, [chatbot, history], outputs=[chatbot, history], show_progress="full", show_api=False) undo_btn.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) clear_btn.click(reset_state, inputs=[system], outputs=[chatbot, history], show_api=False) ######## tab2: response-generator generate_btn_2.click(append_user_to_history, [input_text_2, chatbot, history], outputs=[chatbot, history], show_api=False) \ .then(generate_assistant_message, [chatbot, history], outputs=[chatbot, history], show_progress="full", show_api=False) retry_btn_2.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) \ .then(chat, [chatbot, history], outputs=[chatbot, history], show_progress="full", show_api=False) undo_btn_2.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) clear_btn_2.click(reset_state, inputs=[system], outputs=[chatbot, history], show_api=False) \ .then(reset_user_input, outputs=[input_text_2], show_api=False) tab_assistant_agent.select(generate_assistant_message, [chatbot, history, gr_false], outputs=[chatbot, history], show_progress="full", show_api=False) # 点击tab，生成response (不warning) ######## tab3: user-simulator generate_btn_3.click(append_assistant_to_history, [input_text_3, chatbot, history], outputs=[chatbot, history], show_api=False) \ .then(generate_user_message, [chatbot, history], outputs=[chatbot, history], show_progress="full", show_api=False) retry_btn_3.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) \ .then(chat, [chatbot, history], outputs=[chatbot, history], show_progress="full", show_api=False) undo_btn_3.click(undo_generate, [chatbot, history], outputs=[chatbot, history], show_api=False) clear_btn_3.click(reset_state, inputs=[system], outputs=[chatbot, history], show_api=False) \ .then(reset_user_input, outputs=[input_text_3], show_api=False) tab_user_agent.select(generate_user_message, [chatbot, history, gr_false], outputs=[chatbot, history], show_progress="full", show_api=False) # 点击tab，生成user-input slider_max_new_tokens.change(set_max_new_tokens, inputs=[slider_max_new_tokens]) slider_temperature.change(set_temperature, inputs=[slider_temperature]) slider_top_p.change(set_top_p, inputs=[slider_top_p]) slider_top_k.change(set_top_k, inputs=[slider_top_k]) demo.load(lambda: gr.update(value=random.choice(system_list)), None, system, show_api=False) # demo.queue().launch(share=False, server_name="0.0.0.0", debug=True) # demo.queue().launch(concurrency_count=1, max_size=5) demo.queue().launch()