Spaces:
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Running
on
Zero
import os | |
import time | |
import spaces | |
from threading import Thread | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer | |
import gradio as gr | |
MODEL = "weblab-GENIAC/Tanuki-8B-dpo-v1.0" | |
HF_TOKEN = os.environ.get("HF_TOKEN", None) | |
TITLE = "<h1><center>Tanuki-8B-dpo-v1.0</center></h1>" | |
DESCRIPTION = """ | |
<div class="model-description"> | |
<p> | |
🦡 <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、 | |
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。 | |
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。 | |
</p> | |
<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> | |
<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> | |
<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> | |
</div> | |
""" | |
PLACEHOLDER = """ | |
<div class="image-placeholder"> | |
<img src="https://weblab.t.u-tokyo.ac.jp/wp-content/uploads/2024/06/GENIAC-image-cutting3-1.jpg" alt="Tanuki-8B Image"> | |
<h1>Tanuki-8B</h1> | |
</div> | |
""" | |
CSS = """ | |
.duplicate-button { | |
margin: auto !important; | |
color: white !important; | |
background: black !important; | |
border-radius: 100vh !important; | |
} | |
h3 { | |
text-align: center; | |
} | |
.model-description { | |
padding: 0.5em 1em; | |
margin: 2em 0; | |
border-top: solid 5px #5d627b; | |
box-shadow: 0 1px 1px rgba(0, 0, 0, 0.22); | |
border-radius: 5px; | |
} | |
.model-description p { | |
margin: 0; | |
padding: 0; | |
color: #5d627b; | |
} | |
.image-placeholder { | |
text-align: center; | |
display: flex; | |
flex-direction: column; | |
align-items: center; | |
} | |
.image-placeholder img { | |
width: 100%; | |
height: auto; | |
opacity: 0.55; | |
} | |
.image-placeholder h1 { | |
font-size: 28px; | |
margin-bottom: 2px; | |
opacity: 0.55; | |
} | |
""" | |
ANALYTICS_HEAD = """ | |
<script async src="https://www.googletagmanager.com/gtag/js?id=G-JLBL393020"></script> | |
""" | |
ANALYTICS_JS = """ | |
function() { | |
window.dataLayer = window.dataLayer || []; | |
function gtag(){dataLayer.push(arguments);} | |
gtag('js', new Date()); | |
gtag('config', 'G-JLBL393020'); | |
} | |
""" | |
tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
model = AutoModelForCausalLM.from_pretrained( | |
MODEL, | |
torch_dtype=torch.bfloat16, | |
device_map="auto", | |
) | |
print(model) | |
def stream_chat( | |
message: str, | |
history: list, | |
system_prompt: str, | |
temperature: float = 0.3, | |
max_new_tokens: int = 1024, | |
top_p: float = 1.0, | |
top_k: int = 20, | |
): | |
print(f'message: {message}') | |
print(f'history: {history}') | |
conversation = [ | |
{"role": "system", "content": system_prompt} | |
] | |
for prompt, answer in history: | |
if prompt == None: | |
prompt = " " | |
if answer == None: | |
answer = " " | |
conversation.extend([ | |
{"role": "user", "content": prompt}, | |
{"role": "assistant", "content": answer}, | |
]) | |
conversation.append({"role": "user", "content": message}) | |
input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt").to(model.device) | |
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True) | |
generate_kwargs = dict( | |
input_ids=input_ids, | |
max_new_tokens = max_new_tokens, | |
do_sample = False if temperature == 0 else True, | |
top_p = top_p, | |
top_k = top_k, | |
temperature = temperature, | |
streamer=streamer, | |
) | |
with torch.no_grad(): | |
thread = Thread(target=model.generate, kwargs=generate_kwargs) | |
thread.start() | |
buffer = "" | |
for new_text in streamer: | |
buffer += new_text | |
yield buffer | |
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) | |
with gr.Blocks(head=ANALYTICS_HEAD, css=CSS, theme="soft") as demo: | |
demo.load(None, js=ANALYTICS_JS) | |
gr.HTML(TITLE) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
gr.Markdown(DESCRIPTION) | |
gr.ChatInterface( | |
fn=stream_chat, | |
chatbot=chatbot, | |
fill_height=True, | |
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=True, render=False), | |
additional_inputs=[ | |
gr.Textbox( | |
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。", | |
label="System Prompt", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0, | |
maximum=1, | |
step=0.1, | |
value=0, | |
label="Temperature", | |
render=False, | |
), | |
gr.Slider( | |
minimum=128, | |
maximum=8192, | |
step=1, | |
value=1024, | |
label="Max new tokens", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
step=0.1, | |
value=1.0, | |
label="top_p", | |
render=False, | |
), | |
gr.Slider( | |
minimum=1, | |
maximum=20, | |
step=1, | |
value=20, | |
label="top_k", | |
render=False, | |
), | |
], | |
examples=[ | |
["日本で有名なものと言えば"], | |
["人工知能とは何ですか"], | |
["C言語で素数を判定するコードを書いて"], | |
["たぬきが主人公の物語を書いて"] | |
], | |
cache_examples=False, | |
) | |
if __name__ == "__main__": | |
demo.launch() |