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Model: EugeneMeng/Short-Drama-Title-Generator-4B
Source: Original Platform

.gitattributes

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Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+---
+license: apache-2.0
+language:
+- zh
+tags:
+- qwen
+- text-generation
+- short-drama
+- lora
+- finetune
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- EugeneMeng/Hongguo-Short-Drama-Corpus-AI-Labeled
+---
+
+# Short-Drama-Title-Generator-4B (微短剧爆款剧名生成大模型)
+
+[中文介绍](#中文介绍) | [English Introduction](#english)
+
+<a id="中文介绍"></a>
+## 🎬 模型介绍
+**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”。
+
+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。
+
+**核心应用场景**：
+*   短剧编剧/导演快速起名
+*   网文小说作者灵感激发
+*   短视频信息流广告素材起标题
+
+## 🚀 快速开始 (Quick Start)
+
+### 1. 依赖安装
+```bash
+pip install transformers accelerate vllm
+```
+
+### 2. Python 推理示例
+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】）。
+
+```python
+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。
+
+---
+
+<a id="english"></a>
+## 🎬 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。

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
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+  "hidden_act": "silu",
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+  "intermediate_size": 9728,
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+  "max_window_layers": 36,
+  "model_type": "qwen3",
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+  "num_key_value_heads": 8,
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+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
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+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.6.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.6.0"
+}

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