from transformers import AutoModelForCausalLM, AutoTokenizer


model_name = "EugeneMeng/Short-Drama-Title-Generator-4B" 

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")

# 你的指令和输入
instruction = "你是一名深谙下沉市场心理的顶级微短剧编剧。请根据以下剧情简介，为这部【女频】短剧起一个极具爽感、能引发病毒式传播的爆款剧名。"
input_text = "【标签】[\"都市爱情\", \"追妻火葬场\", \"打脸虐渣\"]\n【简介】林湘看清自己不过是父亲用来挽救公司、被塞进豪门的“工具人”。三年协议婚姻，她忍下冷暴力与流言。幡然醒悟后，她踢走势利保姆，重返职场，活成了前夫高攀不起的模样。"

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": f"{instruction}\n{input_text}"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# 生成剧名
generated_ids = model.generate(**model_inputs, max_new_tokens=50, temperature=0.7)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

print("生成的剧名:", response)

3. Ollama 本地运行

文件包含‘Modelfile`，如果你熟悉 Ollama 生态，可以直接将其转换为 GGUF 并进行极其方便的本地部署调用。

🎬 Model Description (English)

Short-Drama-Title-Generator-4B is a specialized Large Language Model fine-tuned on the Qwen3-4B-Instruct architecture. It is designed to generate highly engaging, viral, and click-worthy titles for Chinese Short Dramas (微短剧).

The model was meticulously fine-tuned on a high-quality dataset containing 20,205 real-world popular short drama titles and plots. It excels at extracting core elements from a brief plot summary and outputting titles that strongly appeal to target audiences in the short video ecosystem.

⚠️ 免责声明 (Disclaimer)

本模型生成的剧名仅供参考，生成结果受输入文本和模型生成随机性影响。请用户在使用时遵守相关法律法规，不得用于生成、传播任何违法违规或侵犯他人权益的内容。