diff --git a/fast_infenence.py b/fast_inference.py
similarity index 57%
rename from fast_infenence.py
rename to fast_inference.py
index 5ab4eea..ee3a119 100644
--- a/fast_infenence.py
+++ b/fast_inference.py
@@ -1,19 +1,16 @@
import json
+import random
+import numpy as np
import streamlit as st
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation.utils import GenerationConfig
-st.set_page_config(page_title="minimind-v1(108M)")
-st.title("minimind-v1(108M)")
+st.set_page_config(page_title="MiniMind-V1")
+st.title("MiniMind-V1")
-model_id = "minimind-v1"
+model_id = "./minimind-v1"
-# -----------------------------------------------------------------------------
-temperature = 0.7
-top_k = 8
-max_seq_len = 1 * 1024
-# -----------------------------------------------------------------------------
@st.cache_resource
def load_model_tokenizer():
@@ -33,28 +30,41 @@ def load_model_tokenizer():
def clear_chat_messages():
del st.session_state.messages
+ del st.session_state.chat_messages
def init_chat_messages():
with st.chat_message("assistant", avatar='🤖'):
- st.markdown("您好，我是由JingyaoGong创造的MiniMind，很高兴为您服务😄")
+ st.markdown("我是由JingyaoGong创造的MiniMind，很高兴为您服务😄 \n"
+ "注：所有AI生成内容的准确性和立场无法保证，不代表我们的态度或观点。")
if "messages" in st.session_state:
for message in st.session_state.messages:
- avatar = "🧑💻" if message["role"] == "user" else "🤖"
+ avatar = "🫡" if message["role"] == "user" else "🤖"
with st.chat_message(message["role"], avatar=avatar):
st.markdown(message["content"])
else:
st.session_state.messages = []
+ st.session_state.chat_messages = []
return st.session_state.messages
-# max_new_tokens = st.sidebar.slider("max_new_tokens", 0, 1024, 512, step=1)
-# top_p = st.sidebar.slider("top_p", 0.0, 1.0, 0.8, step=0.01)
-# top_k = st.sidebar.slider("top_k", 0, 100, 0, step=1)
-# temperature = st.sidebar.slider("temperature", 0.0, 2.0, 1.0, step=0.01)
-# do_sample = st.sidebar.checkbox("do_sample", value=False)
+st.sidebar.title("设定调整")
+st.session_state.history_chat_num = st.sidebar.slider("携带历史对话条数", 0, 6, 0, step=2)
+st.session_state.max_new_tokens = st.sidebar.slider("最大输入/生成长度", 256, 768, 512, step=1)
+st.session_state.top_k = st.sidebar.slider("top_k", 0, 16, 14, step=1)
+st.session_state.temperature = st.sidebar.slider("temperature", 0.3, 1.3, 0.5, step=0.01)
+
+
+def setup_seed(seed):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ torch.cuda.manual_seed_all(seed)
+ torch.backends.cudnn.deterministic = True
+ torch.backends.cudnn.benchmark = False
def main():
@@ -65,32 +75,30 @@ def main():
with st.chat_message("user", avatar='🧑💻'):
st.markdown(prompt)
messages.append({"role": "user", "content": prompt})
+ st.session_state.chat_messages.append({"role": "user", "content": '请问，' + prompt + '？'})
with st.chat_message("assistant", avatar='🤖'):
placeholder = st.empty()
+ # Generate a random seed
+ random_seed = random.randint(0, 2 ** 32 - 1)
+ setup_seed(random_seed)
- chat_messages = []
- chat_messages.append({"role": "user", "content": '请问，' + prompt})
- # print(messages)
new_prompt = tokenizer.apply_chat_template(
- chat_messages,
+ st.session_state.chat_messages[-(st.session_state.history_chat_num + 1):],
tokenize=False,
add_generation_prompt=True
- )[-(max_seq_len - 1):]
+ )[-(st.session_state.max_new_tokens - 1):]
x = tokenizer(new_prompt).data['input_ids']
x = (torch.tensor(x, dtype=torch.long)[None, ...])
-
- response = ''
-
with torch.no_grad():
- res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=max_seq_len, temperature=temperature,
- top_k=top_k, stream=True)
+ res_y = model.generate(x, tokenizer.eos_token_id, max_new_tokens=st.session_state.max_new_tokens,
+ temperature=st.session_state.temperature,
+ top_k=st.session_state.top_k, stream=True)
try:
y = next(res_y)
except StopIteration:
return
- history_idx = 0
while y != None:
answer = tokenizer.decode(y[0].tolist())
if answer and answer[-1] == '�':
@@ -99,7 +107,6 @@ def main():
except:
break
continue
- # print(answer)
if not len(answer):
try:
y = next(res_y)
@@ -107,17 +114,14 @@ def main():
break
continue
placeholder.markdown(answer)
- response = answer
try:
y = next(res_y)
except:
break
- # if contain_history_chat:
- # assistant_answer = answer.replace(new_prompt, "")
- # messages.append({"role": "assistant", "content": assistant_answer})
-
- messages.append({"role": "assistant", "content": response})
+ assistant_answer = answer.replace(new_prompt, "")
+ messages.append({"role": "assistant", "content": assistant_answer})
+ st.session_state.chat_messages.append({"role": "assistant", "content": assistant_answer})
st.button("清空对话", on_click=clear_chat_messages)