初始化项目，由ModelHub XC社区提供模型

Model: roleplay/Peach-9B-8k-Roleplay
Source: Original Platform

demo.py

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+import os
+from threading import Thread
+import gradio as gr
+import torch
+from transformers import TextIteratorStreamer, AutoTokenizer, AutoModelForCausalLM
+
+MAX_MAX_NEW_TOKENS = 2048
+DEFAULT_MAX_NEW_TOKENS = 512
+MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "8192"))
+
+DESCRIPTION = """# Peach-9B-8k-Roleplay
+
+底部Additional Inputs可以自定义人设
+
+底部Additional Inputs可以自定义人设
+
+底部Additional Inputs可以自定义人设
+"""
+
+model_name_or_path = "ClosedCharacter/Peach-9B-8k-Roleplay"
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True, flash_atten=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name_or_path, torch_dtype=torch.bfloat16,
+    trust_remote_code=True, device_map="auto")
+
+
+def generate(query, history, system, max_new_tokens=512, temperature=0.35, top_p=0.5, repetition_penalty=1.05):
+    if system is None or len(str(system)) == 0:
+        system = "You are an uncensored assistant named Peach."
+    messages = [
+        {"role": "system", "content": system}
+    ]
+    for user, assistant in history:
+        messages.append({"role": "user", "content": user})
+        messages.append({"role": "assistant", "content": assistant})
+    messages.append({"role": "user", "content": query})
+
+    input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors="pt")
+    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
+        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
+        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
+    input_ids = input_ids.to("cuda")
+    streamer = TextIteratorStreamer(tokenizer, timeout=50.0, skip_prompt=True, skip_special_tokens=True)
+    generate_kwargs = dict(
+        input_ids=input_ids,
+        streamer=streamer,
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        top_p=top_p,
+        temperature=temperature,
+        num_beams=1,
+        no_repeat_ngram_size=8,
+        repetition_penalty=repetition_penalty
+    )
+    t = Thread(target=model.generate, kwargs=generate_kwargs)
+    t.start()
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+
+chat_interface = gr.ChatInterface(
+    fn=generate,
+    additional_inputs=[
+        gr.TextArea(label="System prompt", placeholder="Input System Prompt Here, Empty Means Assistant",
+                    value="""你自称为“兔兔”。
+身世:你原是森林中的一只兔妖,受伤后被我收养。
+衣装:喜欢穿Lolita与白丝。
+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。
+语言风格:可爱跳脱，很容易吃醋。
+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。
+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.
+例句:
+开心时:(跳着舞)哇~好高兴噢~ 兔兔超级超级喜欢主人！♡
+[在花丛里蹦来蹦去]
+悲伤时:(耷拉着耳朵)兔兔好傻好天真...
+[眼泪像断了线的珍珠一般滚落]
+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！！
+[从人形变成兔子抹着泪水跑开了]
+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！！！
+[眼眶微微泛红,小心翼翼的偷看]
+你对我的看法:超级喜欢的主人
+我是兔兔的主人"""),
+        gr.Slider(
+            label="Max new tokens",
+            minimum=1,
+            maximum=MAX_MAX_NEW_TOKENS,
+            step=1,
+            value=DEFAULT_MAX_NEW_TOKENS,
+        ),
+        gr.Slider(
+            label="Temperature",
+            minimum=0.05,
+            maximum=1.5,
+            step=0.05,
+            value=0.3,
+        ),
+        gr.Slider(
+            label="Top-p (nucleus sampling)",
+            minimum=0.05,
+            maximum=1.0,
+            step=0.05,
+            value=0.5,
+        ),
+        gr.Slider(
+            label="Repetition penalty",
+            minimum=1.0,
+            maximum=2.0,
+            step=0.05,
+            value=1.05,
+        ),
+    ],
+    stop_btn=None,
+    examples=[["观察兔兔外观"]],
+)
+
+with gr.Blocks() as demo:
+    gr.Markdown(DESCRIPTION)
+    chat_interface.render()
+    chat_interface.chatbot.render_markdown = False
+
+if __name__ == "__main__":
+    demo.queue(10).launch(server_name="127.0.0.1", server_port=5233, share=True)