125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
import json
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import streamlit as st
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation.utils import GenerationConfig
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st.set_page_config(page_title="MiniMind-V1 108M(无历史上文)")
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st.title("MiniMind-V1 108M(无历史上文)")
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model_id = "minimind-v1"
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# -----------------------------------------------------------------------------
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temperature = 0.7
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top_k = 8
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max_seq_len = 1 * 1024
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# -----------------------------------------------------------------------------
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@st.cache_resource
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def load_model_tokenizer():
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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trust_remote_code=True
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)
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tokenizer = AutoTokenizer.from_pretrained(
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model_id,
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use_fast=False,
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trust_remote_code=True
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)
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model = model.eval()
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generation_config = GenerationConfig.from_pretrained(model_id)
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return model, tokenizer, generation_config
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def clear_chat_messages():
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del st.session_state.messages
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def init_chat_messages():
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with st.chat_message("assistant", avatar='🤖'):
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st.markdown("您好，我是由Joya开发的MiniMind，很高兴为您服务😄")
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if "messages" in st.session_state:
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for message in st.session_state.messages:
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avatar = "🧑💻" if message["role"] == "user" else "🤖"
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with st.chat_message(message["role"], avatar=avatar):
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st.markdown(message["content"])
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else:
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st.session_state.messages = []
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return st.session_state.messages
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# max_new_tokens = st.sidebar.slider("max_new_tokens", 0, 1024, 512, step=1)
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# top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
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# top_k = st.sidebar.slider("top_k", 0, 100, 0, step=1)
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# temperature = st.sidebar.slider("temperature", 0.0, 2.0, 1.0, step=0.01)
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# do_sample = st.sidebar.checkbox("do_sample", value=False)
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def main():
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model, tokenizer, generation_config = load_model_tokenizer()
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messages = init_chat_messages()
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if prompt := st.chat_input("Shift + Enter 换行, Enter 发送"):
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with st.chat_message("user", avatar='🧑💻'):
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st.markdown(prompt)
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messages.append({"role": "user", "content": prompt})
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with st.chat_message("assistant", avatar='🤖'):
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placeholder = st.empty()
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chat_messages = []
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chat_messages.append({"role": "user", "content": prompt})
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# print(messages)
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new_prompt = tokenizer.apply_chat_template(
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chat_messages,
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tokenize=False,
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add_generation_prompt=True
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)[-(max_seq_len - 1):]
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x = tokenizer(new_prompt).data['input_ids']
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x = (torch.tensor(x, dtype=torch.long)[None, ...])
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response = ''
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with torch.no_grad():
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res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
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top_k=top_k, stream=True)
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try:
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y = next(res_y)
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except StopIteration:
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return
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history_idx = 0
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while y != None:
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answer = tokenizer.decode(y[0].tolist())
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if answer and answer[-1] == '<EFBFBD>':
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try:
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y = next(res_y)
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except:
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break
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continue
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# print(answer)
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if not len(answer):
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try:
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y = next(res_y)
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except:
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break
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continue
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placeholder.markdown(answer)
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response = answer
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try:
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y = next(res_y)
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except:
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break
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# if contain_history_chat:
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# assistant_answer = answer.replace(new_prompt, "")
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# messages.append({"role": "assistant", "content": assistant_answer})
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messages.append({"role": "assistant", "content": response})
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st.button("清空对话", on_click=clear_chat_messages)
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if __name__ == "__main__":
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main()
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