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from transformers import AutoModel, AutoTokenizer,AutoModelForCausalLM |
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import gradio as gr |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("baichuan-inc/Baichuan-13B-Chat", use_fast=False, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan-13B-Chat", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True) |
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model.generation_config = GenerationConfig.from_pretrained("baichuan-inc/Baichuan-13B-Chat") |
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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="็จๆท๏ผ" + query)) |
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updates.append(gr.update(visible=True, value="ChatGLM-6B๏ผ" + 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="ๆ้ฎ๏ผ")) |
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else: |
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text_boxes.append(gr.Markdown(visible=False, label="ๅๅค๏ผ")) |
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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="Enter text and press enter", 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="Maximum length", 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="Temperature", interactive=True) |
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button = gr.Button("Generate") |
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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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