from threading import Thread import gradio as gr import spaces from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer TITLE = "

Chat with Gemma-2-27B-Chinese-Chat

" DESCRIPTION = "

Visit our model page for details.

" DEFAULT_SYSTEM = "You are a helpful assistant." TOOL_EXAMPLE = '''You have access to the following tools: ```python def generate_password(length: int, include_symbols: Optional[bool]): """ Generate a random password. Args: length (int): The length of the password include_symbols (Optional[bool]): Include symbols in the password """ pass ``` Write "Action:" followed by a list of actions in JSON that you want to call, e.g. Action: ```json [ { "name": "tool name (one of [generate_password])", "arguments": "the input to the tool" } ] ``` ''' CSS = """ .duplicate-button { margin: auto !important; color: white !important; background: black !important; border-radius: 100vh !important; } """ tokenizer = AutoTokenizer.from_pretrained("shenzhi-wang/Gemma-2-27B-Chinese-Chat") model = AutoModelForCausalLM.from_pretrained("shenzhi-wang/Gemma-2-27B-Chinese-Chat", device_map="auto", torch_dtype="auto") @spaces.GPU(duration=360) def stream_chat(message: str, history: list, system: str, temperature: float, max_new_tokens: int): conversation = [{"role": "system", "content": system or DEFAULT_SYSTEM}] for prompt, answer in history: 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=360.0, skip_prompt=True, skip_special_tokens=True) generate_kwargs = dict( input_ids=input_ids, streamer=streamer, max_new_tokens=max_new_tokens, temperature=temperature, do_sample=True, ) if temperature == 0: generate_kwargs["do_sample"] = False t = Thread(target=model.generate, kwargs=generate_kwargs) t.start() output = "" for new_token in streamer: output += new_token yield output chatbot = gr.Chatbot(height=450) with gr.Blocks(css=CSS) as demo: gr.HTML(TITLE) gr.HTML(DESCRIPTION) gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") gr.ChatInterface( fn=stream_chat, chatbot=chatbot, fill_height=True, additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), additional_inputs=[ gr.Text( value="", label="System", render=False, ), gr.Slider( minimum=0, maximum=1, step=0.1, value=0.8, label="Temperature", render=False, ), gr.Slider( minimum=128, maximum=4096, step=1, value=1024, label="Max new tokens", render=False, ), ], examples=[ ["我的蓝牙耳机坏了,我该去看牙科还是耳鼻喉科?", ""], ["7年前,妈妈年龄是儿子的6倍,儿子今年12岁,妈妈今年多少岁?", ""], ["我的笔记本找不到了。", "扮演诸葛亮和我对话。"], ["我想要一个新的密码,长度为8位,包含特殊符号。", TOOL_EXAMPLE], ["How are you today?", "You are Taylor Swift, use beautiful lyrics to answer questions."], ["用C++实现KMP算法,并加上中文注释", ""], ], cache_examples=False, ) if __name__ == "__main__": demo.launch()