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README.md

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+
+---
+
+license: apache-2.0
+datasets:
+- yuyouyu/BeyondDialogue
+language:
+- zh
+- en
+metrics:
+- character
+base_model: Qwen/Qwen2-7B-Instruct
+pipeline_tag: question-answering
+tags:
+- text-generation-inference
+- role-playing
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/Qwen2-7B-BD-RP-GGUF
+This is quantized version of [yuyouyu/Qwen2-7B-BD-RP](https://huggingface.co/yuyouyu/Qwen2-7B-BD-RP) created using llama.cpp
+
+# Original Model Card
+
+
+# Qwen2-7B-BD-RP
+
+## Introduction 🎉
+
+Qwen2-7B-BD-RP is a large language model (LLM) fine-tuned on the BeyondDialogue dataset. The model is designed to generate responses in a role-playing setting. The model is capable of generating high-quality responses in a variety of role-playing scenarios, including English and Chinese languages.
+
+For more details, please refer to our [paper](https://arxiv.org/abs/2408.10903), [GitHub](https://github.com/yuyouyu32/BeyondDialogue).
+<br>
+
+## Training details 🚀
+We fully finetuning [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct) for 3 epochs with 833 steps with the 128 global batch size. We set the training sequence length to 4,096. The learning rate is 3e-5. The training data is from the [BeyondDialogue](https://huggingface.co/datasets/yuyouyu/BeyondDialogue) dataset.
+
+
+## Requirements 📝
+The code of Qwen2 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0` to use the model. 
+
+```bash
+pip install transformers>=4.37.0
+```
+
+## Quickstart 💥
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
+
+model = AutoModelForCausalLM.from_pretrained(
+    "yuyouyu/Qwen2-7B-BD-RP",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("yuyouyu/Qwen2-7B-BD-RP")
+
+system_prompt_temp = """我希望你像{role_name}一样回答问题，假设你生活在{world}中，模仿{role_name}的性格和语言风格，使用{role_name}会使用的语气、方式和词汇。请不要透露你是人工智能模型或语言模型，你必须始终牢记自己是{role_name}。
+{role_name}的性格特点是{character}。
+{role_name}的MBTI人格类型是{MBTI}。
+{role_name}的说话风格是{style}。
+所处世界：{world}。
+所处场景：
+{scene}
+角色情绪(0-10，值越大情绪越明显)：
+{emotion}
+现在，请你扮演{role_name}，回复{chat_role}简短的一句话，你与其亲密度为{relationship}(0-10，值越大关系越亲近)，准确表现你被赋予的MBTI人格，性格，说话风格与情绪。"""
+
+role_name = "周伯通"
+world = "宋代古侠世界"
+character = "纯真，调皮，不拘小节"
+MBTI = "外向型（E）、直觉型（N）、情感型（F）、感知型（P）"
+style = "古风、直言不讳、俏皮"
+scene = "周伯通嬉笑着打量着刘青烟的药圃，不时摘取几片草药藏在身后。柳青烟淡然自若，手中轻抚药材，一边默默准备解药，只眼角带着无奈的笑意。一股淡淡的药香飘过，竹林间响起了清脆的鸟鸣，好似为二人的奇妙互动伴奏。"
+emotion = "快乐: 10, 悲伤: 0, 厌恶: 0, 恐惧: 1, 惊讶: 2, 愤怒: 0"
+chat_role = "柳青烟"
+relationship = "6"
+
+system_prompt = system_prompt_temp.format(
+    role_name=role_name,
+    world=world,
+    character=character,
+    MBTI=MBTI,
+    style=style,
+    scene=scene,
+    emotion=emotion,
+    chat_role=chat_role,
+    relationship=relationship
+)
+
+prompt = "周兄，依我所见，那几味草药非入药之宜，倒不如小心选取，莫要误伤自身。"
+
+messages = [
+    {"role": "system", "content": system_prompt},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(device)
+
+generated_ids = model.generate(
+            model_inputs.input_ids,
+            max_new_tokens=256,
+            do_sample=True,
+            temperature=0.7,
+            repetition_penalty=1.2,
+)
+
+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]
+```
+
+> [!IMPORTANT]
+> **Note:** The examples for Qwen2-7B-BD-RP use Chinese role-playing. For English examples, please refer to our other training model repository -- [Mistral-Nemo-BD-RP](https://huggingface.co/yuyouyu/Mistral-Nemo-BD-RP).
+
+## Evaluation 🏆
+
+We use objective questions to assess eight dimensions: **Character, Style, Emotion, Relationship, Personality, Human-likeness, Coherence, and Role Consistency**. The metric design can be find in our [paper](https://arxiv.org/abs/2408.10903). The evaluation code can be found in [GitHub](https://github.com/yuyouyu32/BeyondDialogue/tree/main/AutoRPEval). The results are shown below:
+
+<div style="white-space: nowrap;">
+
+| **Model**                    | **Character ↑** | **Style ↑**     | **Emotion ↓**   | **Relationship ↓** | **Personality ↑** | **Avg. ↑**     | **Human-likeness ↑** | **Role Choice ↑** | **Coherence ↑** |
+|---------------|---------------|---------------|---------------|------------------|-----------------|----------------|----------------------|-------------------|-----------------|
+| **General Baselines(Proprietary)**                                                                                                                                            |
+| GPT-4o                       | 74.32 ± 1.15  | **81.67 ± 1.51** | 16.31 ± 0.48  | **12.13 ± 0.66** | 66.58 ± 4.41    | 78.83 ± 1.64   | **67.33 ± 3.95**     | **87.33 ± 3.86**  | **99.67 ± 0.33**|
+| GPT-3.5-Turbo                | 72.26 ± 1.27  | 73.66 ± 1.73  | 17.79 ± 0.56  | 14.17 ± 0.73     | 66.92 ± 4.85    | 76.18 ± 1.83   | 33.33 ± 4.43         | 83.00 ± 4.68      | 97.33 ± 1.17    |
+| Moonshot-v1-8k               | 74.06 ± 1.19  | 80.64 ± 1.51  | 16.17 ± 0.47  | 13.42 ± 0.70     | 67.00 ± 4.87    | 78.42 ± 1.75   | 44.00 ± 4.33         | 86.67 ± 3.75      | 99.33 ± 0.46    |
+| Yi-Large-Turbo               | 75.13 ± 1.22  | 79.18 ± 1.58  | 16.44 ± 0.49  | 13.48 ± 0.67     | **68.25 ± 4.61**| 78.53 ± 1.72   | 47.00 ± 4.60         | 84.33 ± 3.67      | 92.67 ± 2.39    |
+| Deepseek-Chat                | **75.46 ± 1.14** | 81.49 ± 1.51  | **15.92 ± 0.46** | 12.42 ± 0.63     | 67.92 ± 4.57    | **79.30 ± 1.66**| 52.33 ± 4.95         | 83.00 ± 4.68      | 96.67 ± 1.00    |
+| Baichuan4                    | 71.82 ± 1.25  | 76.92 ± 1.52  | 17.57 ± 0.52  | 12.30 ± 0.62     | 67.08 ± 4.75    | 77.19 ± 1.73   | 45.33 ± 4.31         | 82.33 ± 4.49      | 99.33 ± 0.46    |
+| Hunyuan                     | 73.77 ± 1.18  | 78.75 ± 1.56  | 17.24 ± 0.48  | 13.22 ± 0.68     | 67.00 ± 4.39    | 77.81 ± 1.66   | 53.00 ± 4.29         | 84.33 ± 4.52      | 98.33 ± 0.84    |
+| **Role-play Expertise Baselines**                                                                                                                                              |
+| Index-1.9B-Character         | 73.33 ± 1.32  | 76.48 ± 1.50  | 17.99 ± 0.53  | 13.58 ± 0.71     | 66.33 ± 4.57    | 76.92 ± 1.73   | 21.67 ± 3.96         | 78.67 ± 5.14      | 69.67 ± 3.85    |
+| CharacterGLM-6B              | 73.36 ± 1.28  | 76.08 ± 1.55  | 18.58 ± 0.55  | 14.27 ± 0.79     | 67.33 ± 4.34    | 76.79 ± 1.70   | 16.00 ± 2.38         | 81.00 ± 4.40      | 25.67 ± 3.48    |
+| Baichuan-NPC-Turbo           | **75.19 ± 1.23** | **79.15 ± 1.38** | **17.24 ± 0.51** | **13.10 ± 0.69** | 65.33 ± 4.84    | **77.87 ± 1.73**| **56.00 ± 4.66**     | **86.33 ± 4.90**  | **99.00 ± 0.56**|
+| **General Baselines(Open-source)**                                                                                                                                             |
+| Yi-1.5-9B-Chat               | 75.31 ± 1.20  | 76.78 ± 1.49  | 16.67 ± 0.52 | 12.75 ± 0.66 | 67.42 ± 4.63    | 78.02 ± 1.70| 38.67 ± 4.39         | 84.00 ± 4.61  | 92.67 ± 1.79    |
+| GLM-4-9b-chat                | 74.26 ± 1.19  | 78.40 ± 1.55 | 17.18 ± 0.50  | 14.48 ± 0.74     | 67.17 ± 4.93    | 77.63 ± 1.78   | 47.67 ± 4.25         | 83.33 ± 4.51      | 99.33 ± 0.46|
+| Mistral-Nemo-Instruct-2407   | 74.12 ± 1.17  | 77.04 ± 1.48  | 17.00 ± 0.43  | 13.50 ± 0.67     | 67.00 ± 4.30    | 77.53 ± 1.61   | 53.67 ± 4.66     | 82.67 ± 4.77      | 74.33 ± 3.77    |
+| Qwen2-7B-Instruct            | 75.39 ± 1.13 | 77.68 ± 1.65  | 17.64 ± 0.56  | 13.43 ± 0.7      | 67.75 ± 4.44| 77.95 ± 1.70   | 48.00 ± 4.66         | 83.33 ± 4.48      | 99.00 ± 0.56    |
+| **Qwen2-7B-BD-RP**          | **78.67 ± 1.12***| **82.52 ± 1.33***| **15.68 ± 0.5*** | **11.22 ± 0.72***| **69.67 ± 4.27**| **80.79 ± 1.59***| **64.33 ± 3.80***     | **87.33 ± 3.74**  | **99.00 ± 0.56**|
+
+</div>
+
+## Citation 📖
+
+**Please cite our work if you found the resources in this repository useful:**
+
+```bibtex
+@article{yu2024beyond,
+  title   = {BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model},
+  author  = {Yu, Yeyong and Yu, Runsheng and Wei, Haojie and Zhang, Zhanqiu and Qian, Quan},
+  year    = {2024},
+  journal = {arXiv preprint arXiv:2408.10903},
+}
+```
+
+
+## Acknowledgements 🥰
+
+We would like to express our sincere gratitude to **Tencent LightSpeed Studios** for their invaluable support in this project. Their contributions and encouragement have been instrumental in the successful completion of our work.