Merge 4cebde7f39 into 801b1bb576

cli_demo.py

@@ -5,6 +5,12 @@ from transformers import AutoTokenizer, AutoModel
 
 tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
 model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+# 按需修改，目前只支持 4/8 bit 量化
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().quantize(4).cuda()
+#如果你没有 GPU 硬件的话，也可以在 CPU 上进行推理，但是推理速度会更慢。使用方法如下（需要大概 32GB 内存）
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+#如果你的内存不足，可以直接加载量化后的模型：
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
 model = model.eval()
 
 os_name = platform.system()

cli_demo_cpu.py

@@ -0,0 +1,24 @@
+import os
+import platform
+from transformers import AutoTokenizer, AutoModel
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
+model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+model = model.eval()
+
+os_name = platform.system()
+
+history = []
+print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
+while True:
+    query = input("\n用户：")
+    if query == "stop":
+        break
+    if query == "clear":
+        history = []
+        command = 'cls' if os_name == 'Windows' else 'clear'
+        os.system(command)
+        print("欢迎使用 ChatGLM-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
+        continue
+    response, history = model.chat(tokenizer, query, history=history)
+    print(f"ChatGLM-6B：{response}")

web_demo.py

@@ -2,7 +2,14 @@ from transformers import AutoModel, AutoTokenizer
 import gradio as gr
 
 tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
-model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+#GPU 部署
+model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+# 按需修改，目前只支持 4/8 bit 量化
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().quantize(4).cuda()
+#如果你没有 GPU 硬件的话，也可以在 CPU 上进行推理，但是推理速度会更慢。使用方法如下（需要大概 32GB 内存）
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+#如果你的内存不足，可以直接加载量化后的模型：
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
 model = model.eval()
 
 MAX_TURNS = 20
@@ -34,12 +41,12 @@ with gr.Blocks() as demo:
 
     with gr.Row():
         with gr.Column(scale=4):
-            txt = gr.Textbox(show_label=False, placeholder="Enter text and press enter", lines=11).style(
+            txt = gr.Textbox(show_label=False, placeholder="输入文本并按Enter键", lines=11).style(
                 container=False)
         with gr.Column(scale=1):
-            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="Maximum length", interactive=True)
+            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="最大长度", interactive=True)
             top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
-            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="Temperature", interactive=True)
-            button = gr.Button("Generate")
+            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="氛围", interactive=True)
+            button = gr.Button("生成")
     button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
 demo.queue().launch(share=False, inbrowser=True)

web_demo_cpu.py

@@ -0,0 +1,52 @@
+from transformers import AutoModel, AutoTokenizer
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
+#GPU 部署
+model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+# 按需修改，目前只支持 4/8 bit 量化
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().quantize(4).cuda()
+#如果你没有 GPU 硬件的话，也可以在 CPU 上进行推理，但是推理速度会更慢。使用方法如下（需要大概 32GB 内存）
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
+#如果你的内存不足，可以直接加载量化后的模型：
+#model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
+model = model.eval()
+
+MAX_TURNS = 20
+MAX_BOXES = MAX_TURNS * 2
+
+
+def predict(input, max_length, top_p, temperature, history=None):
+    if history is None:
+        history = []
+    for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
+                                               temperature=temperature):
+        updates = []
+        for query, response in history:
+            updates.append(gr.update(visible=True, value="用户：" + query))
+            updates.append(gr.update(visible=True, value="ChatGLM-6B：" + response))
+        if len(updates) < MAX_BOXES:
+            updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
+        yield [history] + updates
+
+
+with gr.Blocks() as demo:
+    state = gr.State([])
+    text_boxes = []
+    for i in range(MAX_BOXES):
+        if i % 2 == 0:
+            text_boxes.append(gr.Markdown(visible=False, label="提问："))
+        else:
+            text_boxes.append(gr.Markdown(visible=False, label="回复："))
+
+    with gr.Row():
+        with gr.Column(scale=4):
+            txt = gr.Textbox(show_label=False, placeholder="输入文本并按Enter键", lines=11).style(
+                container=False)
+        with gr.Column(scale=1):
+            max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="最大长度", interactive=True)
+            top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
+            temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="氛围", interactive=True)
+            button = gr.Button("生成")
+    button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
+demo.queue().launch(share=False, inbrowser=True)