initial commit with peft removed anyways.

app.py

@@ -0,0 +1,124 @@
+# -*- 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("ljsabc/Fujisaki-int4").float()
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
+
+# 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>"""
+
+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)