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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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model_name_or_path = "losed-Character/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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