wehub-resource-sync
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
"""
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This script demonstrates how to use the `bad_words_ids` argument in the context of a conversational AI model to filter out unwanted words or phrases from the model's responses. It's designed to showcase a fundamental method of content moderation within AI-generated text, particularly useful in scenarios where maintaining the decorum of the conversation is essential.
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Usage:
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- Interact with the model by typing queries. The model will generate responses while avoiding the specified bad words.
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- Use 'clear' to clear the conversation history and 'stop' to exit the program.
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Requirements:
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- The script requires the Transformers library and an appropriate model checkpoint.
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Note: The `bad_words_ids` feature is an essential tool for controlling the output of language models, particularly in user-facing applications where content moderation is crucial.
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"""
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import os
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import platform
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from transformers import AutoTokenizer, AutoModel
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MODEL_PATH = os.environ.get('MODEL_PATH', 'THUDM/chatglm3-6b')
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TOKENIZER_PATH = os.environ.get("TOKENIZER_PATH", MODEL_PATH)
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tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH, trust_remote_code=True)
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model = AutoModel.from_pretrained(MODEL_PATH, trust_remote_code=True, device_map="auto").eval()
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os_name = platform.system()
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clear_command = 'cls' if os_name == 'Windows' else 'clear'
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stop_stream = False
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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"
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# probability tensor contains either `inf`, `nan` or element < 0
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bad_words = ["你好", "ChatGLM"]
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bad_word_ids = [tokenizer.encode(bad_word, add_special_tokens=False) for bad_word in bad_words]
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def build_prompt(history):
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prompt = welcome_prompt
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for query, response in history:
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prompt += f"\n\n用户：{query}"
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prompt += f"\n\nChatGLM3-6B：{response}"
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return prompt
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def main():
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past_key_values, history = None, []
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global stop_stream
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print(welcome_prompt)
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while True:
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query = input("\n用户：")
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if query.strip().lower() == "stop":
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break
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if query.strip().lower() == "clear":
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past_key_values, history = None, []
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os.system(clear_command)
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print(welcome_prompt)
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continue
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# Attempt to generate a response
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try:
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print("\nChatGLM：", end="")
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current_length = 0
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response_generated = False
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for response, history, past_key_values in model.stream_chat(
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tokenizer, query, history=history, top_p=1,
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temperature=0.01,
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past_key_values=past_key_values,
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return_past_key_values=True,
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bad_words_ids=bad_word_ids # assuming this is implemented correctly
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):
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response_generated = True
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# Check if the response contains any bad words
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if any(bad_word in response for bad_word in bad_words):
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print("我的回答涉嫌了 bad word")
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break # Break the loop if a bad word is detected
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# Otherwise, print the generated response
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print(response[current_length:], end="", flush=True)
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current_length = len(response)
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if not response_generated:
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print("没有生成任何回答。")
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except RuntimeError as e:
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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")
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print("")
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if __name__ == "__main__":
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main() |