# -*- coding: utf-8 -*- """Fujisaki_CPU.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Damnr0Ha4zZAlKFvne9cu76uuElLNYus 李萌萌的电子骨灰盒 ---- 这是一个通过ChatGLM模型训练的李萌萌的数字分身，你可以在问题栏目填入内容，或者什么都不填，来观察李萌萌到底会说些什么。 T4级别的GPU已经可以很胜任这个任务了。 ### 安装依赖 """ from modeling_chatglm import ChatGLMForConditionalGeneration import torch import sys from transformers import AutoTokenizer, GenerationConfig model = ChatGLMForConditionalGeneration.from_pretrained("THUDM/chatglm-6b").float() tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True) from peft import get_peft_model, LoraConfig, TaskType, PeftModel peft_path = 'ljsabc/Fujisaki_GLM' # change it to your own model = PeftModel.from_pretrained( model, peft_path, torch_dtype=torch.float, ) # dump a log to ensure everything works well print(model.peft_config) # We have to use full precision, as some tokens are >65535 model.eval() torch.set_default_tensor_type(torch.FloatTensor) def evaluate(context, temperature, top_p, top_k): generation_config = GenerationConfig( temperature=temperature, top_p=top_p, top_k=top_k, #repetition_penalty=1.1, num_beams=1, do_sample=True, ) with torch.no_grad(): input_text = f"Context: {context}Answer: " ids = tokenizer.encode(input_text) input_ids = torch.LongTensor([ids]).to('cpu') out = model.generate( input_ids=input_ids, max_length=160, generation_config=generation_config ) out_text = tokenizer.decode(out[0]).split("Answer: ")[1] return out_text def evaluate_stream(msg, history, temperature, top_p): generation_config = GenerationConfig( temperature=temperature, top_p=top_p, #repetition_penalty=1.1, num_beams=1, do_sample=True, ) history.append([msg, None]) context = "" if len(history) > 4: history.pop(0) for j in range(len(history)): history[j][0] = history[j][0].replace("
", "") # concatenate context for h in history[:-1]: context += h[0] + "||" + h[1] + "||" context += history[-1][0] context = context.replace(r'
', '') # TODO: Avoid the tokens are too long. CUTOFF = 224 while len(tokenizer.encode(context)) > CUTOFF: # save 15 token size for the answer context = context[15:] h = [] print("History:", history) print("Context:", context) for response, h in model.stream_chat(tokenizer, context, h, max_length=CUTOFF, top_p=top_p, temperature=temperature): history[-1][1] = response yield history, "" #return response import gradio as gr title = """

李萌萌（Alter Ego）

这是一个通过ChatGLM模型训练的李萌萌的数字分身，你可以与她聊天，或者直接在文本框按下Enter，来观察李萌萌到底会说些什么。

可能是因为数据的原因，相比于提问，陈述性的上下文更容易跑出更好的结果。

""" footer = """

项目在GitHub上托管，基于清华的THUDM/chatglm-6b项目。

"I'm... a boy." --Chihiro Fujisaki

""" with gr.Blocks() as demo: gr.HTML(title) state = gr.State() with gr.Row(): with gr.Column(scale=2): temp = gr.components.Slider(minimum=0, maximum=1.1, value=0.8, label="Temperature", info="温度参数，越高的温度生成的内容越丰富，但是有可能出现语法问题。小的温度也能帮助生成更相关的回答。") top_p = gr.components.Slider(minimum=0.5, maximum=1.0, value=0.975, label="Top-p", info="top-p参数，只输出前p>top-p的文字，越大生成的内容越丰富，但也可能出现语法问题。数字越小似乎上下文的衔接性越好。") #code = gr.Textbox(label="temp_output", info="解码器输出") #top_k = gr.components.Slider(minimum=1, maximum=200, step=1, value=25, label="Top k", # info="top-k参数，下一个输出的文字会从top-k个文字中进行选择，越大生成的内容越丰富，但也可能出现语法问题。数字越小似乎上下文的衔接性越好。") with gr.Column(scale=3): chatbot = gr.Chatbot(label="聊天框", info="") msg = gr.Textbox(label="输入框", placeholder="最近过得怎么样？", info="输入你的内容，按[Enter]发送。也可以什么都不填写生成随机数据。对话一般不能太长，否则就复读机了，建议清除数据。") clear = gr.Button("清除聊天") msg.submit(evaluate_stream, [msg, chatbot, temp, top_p], [chatbot, msg]) clear.click(lambda: None, None, chatbot, queue=False) gr.HTML(footer) demo.queue() demo.launch(debug=False)