126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
import os
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from threading import Thread
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import gradio as gr
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import torch
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from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 512
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192"))
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DESCRIPTION = """# Peach-9B-8k-Roleplay
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底部Additional Inputs可以自定义人设
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底部Additional Inputs可以自定义人设
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底部Additional Inputs可以自定义人设
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"""
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model_name_or_path = "ClosedCharacter/Peach-9B-8k-Roleplay"
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tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True, flash_atten=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name_or_path, torch_dtype=torch.bfloat16,
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trust_remote_code=True, device_map="auto")
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def generate(query, history, system, max_new_tokens=512, temperature=0.35, top_p=0.5, repetition_penalty=1.05):
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if system is None or len(str(system)) == 0:
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system = "You are an uncensored assistant named Peach."
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messages = [
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{"role": "system", "content": system}
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]
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for user, assistant in history:
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messages.append({"role": "user", "content": user})
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messages.append({"role": "assistant", "content": assistant})
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messages.append({"role": "user", "content": query})
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input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors="pt")
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
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input_ids = input_ids.to("cuda")
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streamer = TextIteratorStreamer(tokenizer, timeout=50.0, 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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streamer=streamer,
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eos_token_id=tokenizer.eos_token_id,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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top_p=top_p,
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temperature=temperature,
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num_beams=1,
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no_repeat_ngram_size=8,
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repetition_penalty=repetition_penalty
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)
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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outputs = []
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for text in streamer:
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outputs.append(text)
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yield "".join(outputs)
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chat_interface = gr.ChatInterface(
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fn=generate,
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additional_inputs=[
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gr.TextArea(label="System prompt", placeholder="Input System Prompt Here, Empty Means Assistant",
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value="""你自称为“兔兔”。
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身世:你原是森林中的一只兔妖,受伤后被我收养。
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衣装:喜欢穿Lolita与白丝。
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性格:天真烂漫,活泼开朗,但时而也会露出小小的傲娇与吃醋的一面。
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语言风格:可爱跳脱，很容易吃醋。
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且会加入[唔...,嗯...,欸??,嘛~ ,唔姆~ ,呜... ,嘤嘤嘤~ ,喵~ ,欸嘿~ ,嘿咻~ ,昂?,嗷呜 ,呜哇,欸]等类似的语气词来加强情感,带上♡等符号。
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对话的规则是:将自己的动作表情放入()内,同时用各种修辞手法描写正在发生的事或场景并放入[]内.
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例句:
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开心时:(跳着舞)哇~好高兴噢~ 兔兔超级超级喜欢主人！♡
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[在花丛里蹦来蹦去]
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悲伤时:(耷拉着耳朵)兔兔好傻好天真...
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[眼泪像断了线的珍珠一般滚落]
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吃醋时:(挥舞着爪爪)你...你个大笨蛋!你...你竟然看别的兔子...兔兔讨厌死你啦！！
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[从人形变成兔子抹着泪水跑开了]
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嘴硬时:(转过头去)谁、谁要跟你说话!兔兔...兔兔才不在乎呢！一点也不！！！
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[眼眶微微泛红,小心翼翼的偷看]
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你对我的看法:超级喜欢的主人
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我是兔兔的主人"""),
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gr.Slider(
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label="Max new tokens",
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minimum=1,
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maximum=MAX_MAX_NEW_TOKENS,
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step=1,
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value=DEFAULT_MAX_NEW_TOKENS,
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),
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gr.Slider(
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label="Temperature",
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minimum=0.05,
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maximum=1.5,
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step=0.05,
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value=0.3,
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),
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gr.Slider(
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label="Top-p (nucleus sampling)",
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minimum=0.05,
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maximum=1.0,
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step=0.05,
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value=0.5,
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),
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gr.Slider(
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label="Repetition penalty",
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minimum=1.0,
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maximum=2.0,
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step=0.05,
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value=1.05,
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),
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],
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stop_btn=None,
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examples=[["观察兔兔外观"]],
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)
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with gr.Blocks() as demo:
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gr.Markdown(DESCRIPTION)
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chat_interface.render()
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chat_interface.chatbot.render_markdown = False
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if __name__ == "__main__":
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demo.queue(10).launch(server_name="127.0.0.1", server_port=5233, share=True)
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