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"""
This script demonstrates how to use the `bad_words_ids` argument to filter out.
"""
import os
import platform
from transformers import AutoTokenizer, AutoModel
import torch
MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
if 'cuda' in DEVICE: # AMD, NVIDIA GPU can use Half Precision
model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True).to(DEVICE).eval()
else: # CPU, Intel GPU and other GPU can use Float16 Precision Only
model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True).float().to(DEVICE).eval()
os_name = platform.system()
clear_command = 'cls' if os_name == 'Windows' else 'clear'
stop_stream = False
welcome_prompt = "欢迎使用 ChatGLM3-6B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序"
# 定义不希望出现的词汇, 你可以自定义, 在这个例子中，如果模型回答包含 "你好" 或 "ChatGLM"，则会出现这个报错
# probability tensor contains either `inf`, `nan` or element < 0
bad_words = ["你好", "ChatGLM"]
# 将这些词汇转换为token ID列表，每个短语是一个子列表
bad_word_ids = [tokenizer.encode(bad_word, add_special_tokens=False) for bad_word in bad_words]
def build_prompt(history):
prompt = welcome_prompt
for query, response in history:
prompt += f"\n\n用户：{query}"
prompt += f"\n\nChatGLM3-6B：{response}"
return prompt
def main():
past_key_values, history = None, []
global stop_stream
print(welcome_prompt)
while True:
query = input("\n用户：")
if query.strip().lower() == "stop":
break
if query.strip().lower() == "clear":
past_key_values, history = None, []
os.system(clear_command)
print(welcome_prompt)
continue
# Attempt to generate a response
try:
print("\nChatGLM：", end="")
current_length = 0
response_generated = False
for response, history, past_key_values in model.stream_chat(
tokenizer, query, history=history, top_p=1,
temperature=0.01,
past_key_values=past_key_values,
return_past_key_values=True,
bad_words_ids=bad_word_ids # assuming this is implemented correctly
):
response_generated = True
# Check if the response contains any bad words
if any(bad_word in response for bad_word in bad_words):
print("我的回答涉嫌了bad word")
break # Break the loop if a bad word is detected
# Otherwise, print the generated response
print(response[current_length:], end="", flush=True)
current_length = len(response)
if not response_generated:
print("没有生成任何回答。")
except RuntimeError as e:
print(f"生成文本时发生错误：{e}，这可能是涉及到设定的敏感词汇")
print("")
if __name__ == "__main__":
main() |