add app

README.md

@@ -1,8 +1,8 @@
 ---
 title: KumaGLM
-emoji: 👁
-colorFrom: pink
-colorTo: purple
+emoji: 🐻
+colorFrom: blue
+colorTo: green
 sdk: gradio
 sdk_version: 3.24.1
 app_file: app.py

app.py

@@ -0,0 +1,130 @@
+# Credit:
+# https://huggingface.co/spaces/ljsabc/Fujisaki/blob/main/app.py
+
+
+import torch
+import gradio as gr
+from peft import PeftModel
+from transformers import AutoTokenizer, GenerationConfig, AutoModel
+
+
+model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, revision="4de8efe").float()
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True, revision="4de8efe")
+peft_path = 'KumaTea/twitter'
+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()
+# print(model)
+
+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
+
+
+title = """<h1 align="center">KumaGLM</h1>
+<h3 align='center'>这是一个 AI Kuma，你可以与她聊天，或者直接在文本框按下Enter</h3>
+<p align='center'>采样范围 2020/06/13 - 2023/04/15</p>"""
+footer =  """<p align='center'>
+本项目基于
+<a href='https://github.com/ljsabc/Fujisaki' target='_blank'>ljsabc/Fujisaki</a>
+，模型采用
+<a href='https://huggingface.co/THUDM/chatglm-6b' target='_blank'>THUDM/chatglm-6b</a>
+。
+</p>
+<p align='center'>
+<em>每天起床第一句！</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)

requirements.txt

@@ -0,0 +1,22 @@
+# https://huggingface.co/spaces/ljsabc/Fujisaki/blob/main/requirements.txt
+
+# int8
+bitsandbytes>=0.37.1
+accelerate>=0.17.1
+
+# chatglm
+protobuf>=3.19.5,<3.20.1
+transformers>=4.27.1
+icetk
+cpm_kernels>=1.0.11
+
+# 
+datasets>=2.10.1
+git+https://github.com/huggingface/peft.git  # 最新版本 >=0.3.0.dev0
+
+-f https://download.pytorch.org/whl/cpu
+torch
+-f https://download.pytorch.org/whl/cpu
+torchvision
+-f https://download.pytorch.org/whl/cpu
+torchaudio