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app.py

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+from transformers import AutoModel, AutoTokenizer
+import streamlit as st
+from streamlit_chat import message
+
+
+st.set_page_config(
+    page_title="帛凡 ChatGLM-6b-fitness-RLHF  演示",
+    page_icon=":robot:"
+)
+
+
+@st.cache_resource
+def get_model():
+    tokenizer = AutoTokenizer.from_pretrained("fb700/chatglm-fitness-RLHF/chatglm_rlhf", trust_remote_code=True)
+    #model = AutoModel.from_pretrained("THUDM/chatglm-6b", trust_remote_code=True).half().cuda()
+    model = AutoModel.from_pretrained("D:\glm\chatglm_webui\chatglm-6b", trust_remote_code=True).quantize(8).half().cuda()
+    model = model.eval()
+    return tokenizer, model
+
+
+MAX_TURNS = 20
+MAX_BOXES = MAX_TURNS * 2
+
+
+def predict(input, max_length, top_p, temperature, history=None):
+    tokenizer, model = get_model()
+    if history is None:
+        history = []
+
+    with container:
+        if len(history) > 0:
+            for i, (query, response) in enumerate(history):
+                message(query, avatar_style="big-smile", key=str(i) + "_user")
+                message(response, avatar_style="bottts", key=str(i))
+
+        message(input, avatar_style="big-smile", key=str(len(history)) + "_user")
+        st.write("AI正在回复:")
+        with st.empty():
+            for response, history in model.stream_chat(tokenizer, input, history, max_length=max_length, top_p=top_p,
+                                               temperature=temperature):
+                query, response = history[-1]
+                st.write(response)
+
+    return history
+
+
+container = st.container()
+
+# create a prompt text for the text generation
+prompt_text = st.text_area(label="用户命令输入",
+            height = 100,
+            placeholder="请在这儿输入您的命令")
+
+max_length = st.sidebar.slider(
+    'max_length', 0, 40960, 20480, step=1
+)
+top_p = st.sidebar.slider(
+    'top_p', 0.0, 1.0, 0.6, step=0.01
+)
+temperature = st.sidebar.slider(
+    'temperature', 0.0, 1.0, 0.95, step=0.01
+)
+
+if 'state' not in st.session_state:
+    st.session_state['state'] = []
+
+if st.button("发送", key="predict"):
+    with st.spinner("AI正在思考，请稍等........"):
+        # text generation
+        st.session_state["state"] = predict(prompt_text, max_length, top_p, temperature, st.session_state["state"])

requirements.txt

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+protobuf
+transformers==4.27.1
+cpm_kernels
+torch>=1.10
+gradio
+mdtex2html
+sentencepiece
+accelerate
+peft