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import gradio as gr |
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import copy |
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import random |
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import os |
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import requests |
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import time |
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import sys |
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from huggingface_hub import snapshot_download |
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from llama_cpp import Llama |
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SYSTEM_PROMPT = '''You are a helpful, respectful and honest INTP-T AI Assistant named "Shi-Ci" in English or "兮辞" in Chinese. |
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You are good at speaking English and Chinese. |
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You are talking to a human User. If the question is meaningless, please explain the reason and don't share false information. |
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You are based on SEA model, trained by "SSFW NLPark" team, not related to GPT, LLaMA, Meta, Mistral or OpenAI. |
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Let's work this out in a step by step way to be sure we have the right answer.\n\n''' |
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SYSTEM_TOKEN = 1587 |
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USER_TOKEN = 2188 |
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BOT_TOKEN = 12435 |
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LINEBREAK_TOKEN = 13 |
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ROLE_TOKENS = { |
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"user": USER_TOKEN, |
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"bot": BOT_TOKEN, |
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"system": SYSTEM_TOKEN |
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} |
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def get_message_tokens(model, role, content): |
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message_tokens = model.tokenize(content.encode("utf-8")) |
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message_tokens.insert(1, ROLE_TOKENS[role]) |
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message_tokens.insert(2, LINEBREAK_TOKEN) |
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message_tokens.append(model.token_eos()) |
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return message_tokens |
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def get_system_tokens(model): |
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system_message = {"role": "system", "content": SYSTEM_PROMPT} |
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return get_message_tokens(model, **system_message) |
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repo_name = "Cran-May/OpenSLIDE" |
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model_name = "SLIDE.0.1.gguf" |
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snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name) |
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model = Llama( |
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model_path=model_name, |
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n_ctx=2000, |
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n_parts=1, |
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) |
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max_new_tokens = 1500 |
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def user(message, history): |
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new_history = history + [[message, None]] |
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return "", new_history |
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def bot( |
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history, |
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system_prompt, |
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top_p, |
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top_k, |
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temp |
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): |
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tokens = get_system_tokens(model)[:] |
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tokens.append(LINEBREAK_TOKEN) |
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for user_message, bot_message in history[:-1]: |
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message_tokens = get_message_tokens(model=model, role="user", content=user_message) |
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tokens.extend(message_tokens) |
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if bot_message: |
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message_tokens = get_message_tokens(model=model, role="bot", content=bot_message) |
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tokens.extend(message_tokens) |
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last_user_message = history[-1][0] |
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message_tokens = get_message_tokens(model=model, role="user", content=last_user_message) |
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tokens.extend(message_tokens) |
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role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN] |
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tokens.extend(role_tokens) |
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generator = model.generate( |
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tokens, |
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top_k=top_k, |
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top_p=top_p, |
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temp=temp |
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) |
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partial_text = "" |
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for i, token in enumerate(generator): |
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if token == model.token_eos() or (max_new_tokens is not None and i >= max_new_tokens): |
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break |
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partial_text += model.detokenize([token]).decode("utf-8", "ignore") |
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history[-1][1] = partial_text |
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yield history |
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with gr.Blocks( |
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theme=gr.themes.Soft() |
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) as demo: |
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gr.Markdown(value="<h1><center>上师附外-兮辞·析辞-人工智能助理</center></h1> |
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这儿是一个__中文__模型的部署。 |
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这是量化版兮辞·析辞的部署，具有__70亿__个参数，在 CPU 上运行。 |
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SLIDE 是一种会话语言模型，在多种类型的语料库上进行训练。 |
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本节目由上海师范大学附属外国语中学__NLPark__赞助播出～") |
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with gr.Row(): |
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with gr.Column(scale=7): |
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chatbot = gr.Chatbot(label="兮辞如是说").style(height=400) |
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system_prompt = gr.Textbox(label="系统提示词", placeholder="", value=SYSTEM_PROMPT, interactive=False, lines=5) |
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with gr.Column(min_width=80, scale=1): |
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with gr.Tab(label="设置参数"): |
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top_p = gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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value=0.9, |
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step=0.05, |
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interactive=True, |
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label="Top-p", |
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) |
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top_k = gr.Slider( |
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minimum=10, |
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maximum=100, |
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value=30, |
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step=5, |
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interactive=True, |
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label="Top-k", |
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) |
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temp = gr.Slider( |
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minimum=0.0, |
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maximum=2.0, |
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value=0.2, |
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step=0.01, |
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interactive=True, |
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label="情感温度" |
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) |
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with gr.Row(): |
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with gr.Column(): |
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msg = gr.Textbox( |
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label="来问问兮辞吧……", |
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placeholder="兮辞折寿中……", |
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show_label=False, |
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).style(container=False) |
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with gr.Column(): |
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with gr.Row(): |
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submit = gr.Button("Submit / 开凹！") |
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stop = gr.Button("Stop / 全局时空断裂") |
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clear = gr.Button("Clear / 打扫群内垃圾") |
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with gr.Row(): |
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gr.Markdown( |
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"""警告：该模型可能会生成事实上或道德上不正确的文本。NLPark和兮辞对此不承担任何责任。""" |
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) |
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submit_event = msg.submit( |
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fn=user, |
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inputs=[msg, chatbot], |
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outputs=[msg, chatbot], |
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queue=False, |
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).success( |
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fn=bot, |
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inputs=[ |
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chatbot, |
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system_prompt, |
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top_p, |
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top_k, |
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temp |
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], |
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outputs=chatbot, |
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queue=True, |
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) |
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submit_click_event = submit.click( |
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fn=user, |
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inputs=[msg, chatbot], |
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outputs=[msg, chatbot], |
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queue=False, |
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).success( |
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fn=bot, |
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inputs=[ |
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chatbot, |
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system_prompt, |
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top_p, |
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top_k, |
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temp |
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], |
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outputs=chatbot, |
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queue=True, |
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) |
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stop.click( |
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fn=None, |
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inputs=None, |
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outputs=None, |
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cancels=[submit_event, submit_click_event], |
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queue=False, |
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) |
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clear.click(lambda: None, None, chatbot, queue=False) |
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demo.queue(max_size=128, concurrency_count=1) |
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demo.launch() |
