aipgpt/Punch-Line-Master

Purpose

Simple, just make your comments more eye-catching, like talk show style!!!

Train

It's a reasoning model. Train Qwen/Qwen3-14B with USLOTH's GRPO.

Test

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "aipgpt/Punch-Line-Master"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input
user_prompt = "请用幽默的方式修改下面这句话，建议参考脱口秀方式。改后句子长度不超过原句长度的3倍。\n\n原来我和富豪的共同点是都会失眠，区别是他们后悔几千万的决策，我后悔半夜点开外卖软件的手……"
system_prompt = """
请使用中文按以下格式回答问题:
<think>
...
</think>

...
"""
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True # Switches between thinking and non-thinking modes. Default is True.
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=32768
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 

# parsing thinking content
try:
    # rindex finding 151668 (</think>)
    index = len(output_ids) - output_ids[::-1].index(151668)
except ValueError:
    index = 0

thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")

print("thinking content:", thinking_content)
print("content:", content)

Contact

My wechat 229402265, if you ...