初始化项目，由ModelHub XC社区提供模型
Model: EugeneMeng/Short-Drama-Title-Generator-4B Source: Original Platform
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Modelfile
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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README.md
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---
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license: apache-2.0
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language:
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- zh
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tags:
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- qwen
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- text-generation
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- short-drama
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- lora
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- finetune
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- EugeneMeng/Hongguo-Short-Drama-Corpus-AI-Labeled
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---
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# Short-Drama-Title-Generator-4B (微短剧爆款剧名生成大模型)
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[中文介绍](#中文介绍) | [English Introduction](#english)
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<a id="中文介绍"></a>
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## 🎬 模型介绍
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**Short-Drama-Title-Generator-4B** 是一款基于 Qwen3-4B-Instruct 深度微调的“微短剧爆款剧名生成器”。
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本模型使用了高达 **20,205 条真实的爆款微短剧数据集**（涵盖女频、男频、战神、甜宠、穿书、系统、打脸虐渣等全品类）进行指令微调（SFT）。模型深谙下沉市场用户的心理与短剧“网感”，能够根据简单的剧情简介和标签，一键生成极具爽感、能引发病毒式传播的微短剧剧名。
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**核心应用场景**：
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* 短剧编剧/导演快速起名
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* 网文小说作者灵感激发
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* 短视频信息流广告素材起标题
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## 🚀 快速开始 (Quick Start)
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### 1. 依赖安装
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```bash
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pip install transformers accelerate vllm
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```
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### 2. Python 推理示例
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我们推荐使用 `transformers` 库进行快速调用。请确保你遵循了训练时的 Prompt 格式（即包含【标签】和【简介】）。
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "EugeneMeng/Short-Drama-Title-Generator-4B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype="auto")
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# 你的指令和输入
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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。"
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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。"
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": f"{instruction}\n{input_text}"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# 生成剧名
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generated_ids = model.generate(**model_inputs, max_new_tokens=50, temperature=0.7)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print("生成的剧名:", response)
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```
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### 3. Ollama 本地运行
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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。
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---
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<a id="english"></a>
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## 🎬 Model Description (English)
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**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).
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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.
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## ⚠️ 免责声明 (Disclaimer)
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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。
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"Qwen3ForCausalLM"
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"model_type": "qwen3",
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generation_config.json
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generation_config.json
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"top_p": 0.8,
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"transformers_version": "5.6.0"
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}
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3
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31
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Reference in New Issue
Block a user