105 lines
4.6 KiB
Python
105 lines
4.6 KiB
Python
"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/38502
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"""
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import streamlit as st
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st.set_page_config(
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page_title="Chat",
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layout='wide'
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)
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choose_load_method = 1 # 选择加载模型的方式
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if choose_load_method == 0:
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# 默认加载（需要13G显存）
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@st.cache_resource
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def load_model_chatglm3():
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm3-6b-32k",trust_remote_code=True).half().cuda()
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model = model.eval()
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return model, tokenizer
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model_chatglm3, tokenizer_chatglm3 = load_model_chatglm3()
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elif choose_load_method == 1:
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# 量化加载（需要6G显存）
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@st.cache_resource
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def load_model_chatglm3():
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from transformers import AutoTokenizer, BitsAndBytesConfig, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True)
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nf4_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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)
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model = AutoModelForCausalLM.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True, quantization_config=nf4_config)
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model = model.eval()
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return model, tokenizer
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model_chatglm3, tokenizer_chatglm3 = load_model_chatglm3()
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elif choose_load_method == 2:
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# 在CPU上加载（需要25G内存，对话速度会比较慢，不推荐）
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@st.cache_resource
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def load_model_chatglm3():
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm3-6b-32k", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm3-6b-32k",trust_remote_code=True).float()
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model = model.eval()
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return model, tokenizer
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model_chatglm3, tokenizer_chatglm3 = load_model_chatglm3()
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with st.sidebar:
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with st.expander('参数', expanded=True):
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max_length = 409600
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top_p = st.slider('top_p', 0.01, 1.0, step=0.01, value=0.8, key='top_p_session')
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temperature = st.slider('temperature', 0.51, 1.0, step=0.01, value=0.8, key='temperature_session')
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def reset_parameter():
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st.session_state['top_p_session'] = 0.8
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st.session_state['temperature_session'] = 0.8
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reset_parameter_button = st.button('重置', on_click=reset_parameter)
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prompt = st.chat_input("在这里输入您的命令")
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def chat_response_chatglm3(prompt):
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history, past_key_values = st.session_state.history_ChatGLM3, st.session_state.past_key_values_ChatGLM3
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for response, history, past_key_values in model_chatglm3.stream_chat(tokenizer_chatglm3, prompt, 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_chatglm3.markdown(response)
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if stop_button:
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break
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st.session_state.ai_response.append({"role": "robot", "content": response, "avatar": "assistant"})
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st.session_state.history_ChatGLM3 = history
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st.session_state.past_key_values_ChatGLM3 = past_key_values
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return response
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def clear_all():
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st.session_state.history_ChatGLM3 = []
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st.session_state.past_key_values_ChatGLM3 = None
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st.session_state.ai_response = []
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if 'history_ChatGLM3' not in st.session_state:
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st.session_state.history_ChatGLM3 = []
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if 'past_key_values_ChatGLM3' not in st.session_state:
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st.session_state.past_key_values_ChatGLM3 = None
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if 'ai_response' not in st.session_state:
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st.session_state.ai_response = []
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for ai_response in st.session_state.ai_response:
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with st.chat_message(ai_response["role"], avatar=ai_response.get("avatar")):
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st.markdown(ai_response["content"])
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prompt_placeholder = st.chat_message("user", avatar='user')
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with st.chat_message("robot", avatar="assistant"):
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message_placeholder_chatglm3 = st.empty()
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if prompt:
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prompt_placeholder.markdown(prompt)
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st.session_state.ai_response.append({"role": "user", "content": prompt, "avatar": 'user'})
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stop = st.empty()
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stop_button = stop.button('停止', key='break_response')
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chat_response_chatglm3(prompt)
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stop.empty()
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button_clear = st.button("清空", on_click=clear_all, key='clear') |