mirror of https://github.com/THUDM/ChatGLM-6B
53 lines
2.5 KiB
Python
53 lines
2.5 KiB
Python
from transformers import AutoModel, AutoTokenizer
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import gradio as gr
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True)
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#GPU 部署
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model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
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# 按需修改，目前只支持 4/8 bit 量化
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#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().quantize(4).cuda()
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#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）
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#model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).float()
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#如果你的内存不足，可以直接加载量化后的模型：
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#model = AutoModel.from_pretrained("THUDM/chatglm-6b-int4",trust_remote_code=True).float()
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model = model.eval()
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MAX_TURNS = 20
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MAX_BOXES = MAX_TURNS * 2
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def predict(input, max_length, top_p, temperature, history=None):
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if history is None:
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history = []
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for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
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temperature=temperature):
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updates = []
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for query, response in history:
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updates.append(gr.update(visible=True, value="用户：" + query))
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updates.append(gr.update(visible=True, value="ChatGLM-6B：" + response))
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if len(updates) < MAX_BOXES:
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updates = updates + [gr.Textbox.update(visible=False)] * (MAX_BOXES - len(updates))
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yield [history] + updates
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with gr.Blocks() as demo:
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state = gr.State([])
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text_boxes = []
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for i in range(MAX_BOXES):
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if i % 2 == 0:
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text_boxes.append(gr.Markdown(visible=False, label="提问："))
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else:
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text_boxes.append(gr.Markdown(visible=False, label="回复："))
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with gr.Row():
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with gr.Column(scale=4):
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txt = gr.Textbox(show_label=False, placeholder="输入文本并按Enter键", lines=11).style(
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container=False)
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with gr.Column(scale=1):
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max_length = gr.Slider(0, 4096, value=2048, step=1.0, label="最大长度", interactive=True)
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top_p = gr.Slider(0, 1, value=0.7, step=0.01, label="Top P", interactive=True)
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temperature = gr.Slider(0, 1, value=0.95, step=0.01, label="氛围", interactive=True)
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button = gr.Button("生成")
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button.click(predict, [txt, max_length, top_p, temperature, state], [state] + text_boxes)
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demo.queue().launch(share=False, inbrowser=True)
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