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| # -*- 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("<br>", "") | |
| # concatenate context | |
| for h in history[:-1]: | |
| context += h[0] + "||" + h[1] + "||" | |
| context += history[-1][0] | |
| context = context.replace(r'<br>', '') | |
| # 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 = """<h1 align="center">李萌萌（Alter Ego）</h1> | |
| <h3 align='center'>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。</h3> | |
| <p align='center'>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。</p>""" | |
| footer = """<p align='center'>项目在<a href='https://github.com/ljsabc/Fujisaki' target='_blank'>GitHub</a>上托管，基于清华的<a href='https://huggingface.co/THUDM/chatglm-6b' target='_blank'>THUDM/chatglm-6b</a>项目。</p> | |
| <p align='center'><em>"I'm... a boy." --Chihiro Fujisaki</em></p>""" | |
| 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) | |