71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
from transformers import AutoModel, AutoTokenizer
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import streamlit as st
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st.set_page_config(
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page_title="ChatGLM2-6b 演示",
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page_icon=":robot:",
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layout='wide'
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)
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@st.cache_resource
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def get_model():
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm2-6b", trust_remote_code=True).cuda()
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# 多显卡支持，使用下面两行代替上面一行，将num_gpus改为你实际的显卡数量
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# from utils import load_model_on_gpus
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# model = load_model_on_gpus("THUDM/chatglm2-6b", num_gpus=2)
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model = model.eval()
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return tokenizer, model
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tokenizer, model = get_model()
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st.title("ChatGLM2-6B")
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max_length = st.sidebar.slider(
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'max_length', 0, 32768, 8192, step=1
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)
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top_p = st.sidebar.slider(
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'top_p', 0.0, 1.0, 0.8, step=0.01
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)
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temperature = st.sidebar.slider(
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'temperature', 0.0, 1.0, 0.8, step=0.01
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)
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if 'history' not in st.session_state:
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st.session_state.history = []
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if 'past_key_values' not in st.session_state:
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st.session_state.past_key_values = None
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for i, (query, response) in enumerate(st.session_state.history):
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with st.chat_message(name="user", avatar="user"):
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st.markdown(query)
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with st.chat_message(name="assistant", avatar="assistant"):
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st.markdown(response)
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with st.chat_message(name="user", avatar="user"):
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input_placeholder = st.empty()
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with st.chat_message(name="assistant", avatar="assistant"):
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message_placeholder = st.empty()
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prompt_text = st.text_area(label="用户命令输入",
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height=100,
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placeholder="请在这儿输入您的命令")
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button = st.button("发送", key="predict")
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if button:
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input_placeholder.markdown(prompt_text)
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history, past_key_values = st.session_state.history, st.session_state.past_key_values
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for response, history, past_key_values in model.stream_chat(tokenizer, prompt_text, history,
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past_key_values=past_key_values,
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max_length=max_length, top_p=top_p,
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temperature=temperature,
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return_past_key_values=True):
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message_placeholder.markdown(response)
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st.session_state.history = history
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st.session_state.past_key_values = past_key_values
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