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import os |
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import time |
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import spaces |
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from threading import Thread |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer |
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import gradio as gr |
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MODEL = "weblab-GENIAC/Tanuki-8B-dpo-v1.0" |
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HF_TOKEN = os.environ.get("HF_TOKEN", None) |
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TITLE = "<h1><center>Tanuki-8B-dpo-v1.0</center></h1>" |
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DESCRIPTION = """ |
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<div class="model-description"> |
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<p> |
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🦡 <a href="https://huggingface.co/weblab-GENIAC/Tanuki-8B-dpo-v1.0"><b>Tanuki 8B</b>(weblab-GENIAC/Tanuki-8B-dpo-v1.0)</a>は、 |
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経産省及びNEDOが推進する日本国内の生成AI基盤モデル開発を推進する「GENIAC」プロジェクトにおいて、松尾・岩澤研究室が開発・公開したLLMとなります。 |
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本プロジェクトは松尾研が提供する大規模言語モデル講座（2023年9月開催、2,000名が受講）の修了生及び一般公募によって集まった有志の開発者（⺠間企業・研究者・学⽣で構成）が、最新の研究成果や技術的な知見を取り入れ、開発を行ったモデルです。 |
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</p> |
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<p>🤖 このデモでは、Tanuki 8Bとチャットを行うことが可能です。(注:フルバーションの<a href="https://huggingface.co/weblab-GENIAC/Tanuki-8x8B-dpo-v1.0">Tanuki 8x8B</a>ではございません。)</p> |
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<p>📄 モデルの詳細については、<a href="http://weblab.t.u-tokyo.ac.jp/2024-08-30">プレスリリース</a>をご覧ください。お問い合わせは<a href="https://weblab.t.u-tokyo.ac.jp/contact/">こちら</a>までどうぞ。</p> |
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<p>関連サイト: <a href="https://weblab.t.u-tokyo.ac.jp/geniac_llm">GENIAC 松尾研 LLM開発プロジェクト</a></p> |
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</div> |
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""" |
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PLACEHOLDER = """ |
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<div class="image-placeholder"> |
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<img src="https://weblab.t.u-tokyo.ac.jp/wp-content/uploads/2024/06/GENIAC-image-cutting3-1.jpg" alt="Tanuki-8B Image"> |
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<h1>Tanuki-8B</h1> |
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</div> |
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""" |
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CSS = """ |
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.duplicate-button { |
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margin: auto !important; |
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color: white !important; |
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background: black !important; |
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border-radius: 100vh !important; |
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} |
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h3 { |
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text-align: center; |
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} |
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.model-description { |
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padding: 0.5em 1em; |
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margin: 2em 0; |
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border-top: solid 5px #5d627b; |
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box-shadow: 0 1px 1px rgba(0, 0, 0, 0.22); |
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border-radius: 5px; |
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} |
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.model-description p { |
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margin: 0; |
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padding: 0; |
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color: #5d627b; |
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} |
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.image-placeholder { |
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text-align: center; |
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display: flex; |
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flex-direction: column; |
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align-items: center; |
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} |
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.image-placeholder img { |
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width: 100%; |
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height: auto; |
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opacity: 0.55; |
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} |
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.image-placeholder h1 { |
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font-size: 28px; |
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margin-bottom: 2px; |
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opacity: 0.55; |
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} |
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""" |
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ANALYTICS_HEAD = """ |
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<script async src="https://www.googletagmanager.com/gtag/js?id=G-JLBL393020"></script> |
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""" |
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ANALYTICS_JS = """ |
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function() { |
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window.dataLayer = window.dataLayer || []; |
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function gtag(){dataLayer.push(arguments);} |
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gtag('js', new Date()); |
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gtag('config', 'G-JLBL393020'); |
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} |
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""" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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model = AutoModelForCausalLM.from_pretrained( |
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MODEL, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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) |
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print(model) |
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@spaces.GPU() |
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def stream_chat( |
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message: str, |
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history: list, |
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system_prompt: str, |
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temperature: float = 0.3, |
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max_new_tokens: int = 1024, |
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top_p: float = 1.0, |
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top_k: int = 20, |
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): |
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print(f'message: {message}') |
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print(f'history: {history}') |
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conversation = [ |
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{"role": "system", "content": system_prompt} |
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] |
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for prompt, answer in history: |
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if prompt == None: |
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prompt = " " |
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if answer == None: |
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answer = " " |
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conversation.extend([ |
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{"role": "user", "content": prompt}, |
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{"role": "assistant", "content": answer}, |
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]) |
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conversation.append({"role": "user", "content": message}) |
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device) |
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streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) |
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generate_kwargs = dict( |
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input_ids=input_ids, |
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max_new_tokens = max_new_tokens, |
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do_sample = False if temperature == 0 else True, |
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top_p = top_p, |
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top_k = top_k, |
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temperature = temperature, |
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streamer=streamer, |
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) |
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with torch.no_grad(): |
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thread = Thread(target=model.generate, kwargs=generate_kwargs) |
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thread.start() |
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buffer = "" |
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for new_text in streamer: |
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buffer += new_text |
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yield buffer |
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chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) |
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with gr.Blocks(head=ANALYTICS_HEAD, css=CSS, theme="soft") as demo: |
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demo.load(None, js=ANALYTICS_JS) |
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gr.HTML(TITLE) |
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gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") |
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gr.Markdown(DESCRIPTION) |
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gr.ChatInterface( |
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fn=stream_chat, |
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chatbot=chatbot, |
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fill_height=True, |
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additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=True, render=False), |
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additional_inputs=[ |
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gr.Textbox( |
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value="以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。", |
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label="System Prompt", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0, |
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maximum=1, |
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step=0.1, |
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value=0, |
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label="Temperature", |
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render=False, |
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), |
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gr.Slider( |
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minimum=128, |
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maximum=8192, |
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step=1, |
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value=1024, |
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label="Max new tokens", |
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render=False, |
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), |
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gr.Slider( |
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minimum=0.0, |
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maximum=1.0, |
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step=0.1, |
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value=1.0, |
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label="top_p", |
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render=False, |
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), |
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gr.Slider( |
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minimum=1, |
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maximum=20, |
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step=1, |
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value=20, |
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label="top_k", |
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render=False, |
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), |
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], |
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examples=[ |
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["日本で有名なものと言えば"], |
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["人工知能とは何ですか"], |
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["C言語で素数を判定するコードを書いて"], |
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["たぬきが主人公の物語を書いて"] |
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], |
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cache_examples=False, |
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) |
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if __name__ == "__main__": |
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demo.launch() |
