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--- |
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license: apache-2.0 |
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datasets: |
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- yuyouyu/BeyondDialogue |
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language: |
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- zh |
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- en |
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metrics: |
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- character |
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base_model: Qwen/Qwen2-7B-Instruct |
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pipeline_tag: question-answering |
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tags: |
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- text-generation-inference |
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- role-playing |
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--- |
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[](https://hf.co/QuantFactory) |
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# QuantFactory/Qwen2-7B-BD-RP-GGUF |
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This is quantized version of [yuyouyu/Qwen2-7B-BD-RP](https://huggingface.co/yuyouyu/Qwen2-7B-BD-RP) created using llama.cpp |
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# Original Model Card |
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# Qwen2-7B-BD-RP |
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## Introduction 🎉 |
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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. |
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For more details, please refer to our [paper](https://arxiv.org/abs/2408.10903), [GitHub](https://github.com/yuyouyu32/BeyondDialogue). |
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<br> |
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## Training details 🚀 |
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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. |
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## Requirements 📝 |
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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. |
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```bash |
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pip install transformers>=4.37.0 |
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``` |
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## Quickstart 💥 |
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Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents. |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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device = "cuda" # the device to load the model onto |
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model = AutoModelForCausalLM.from_pretrained( |
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"yuyouyu/Qwen2-7B-BD-RP", |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained("yuyouyu/Qwen2-7B-BD-RP") |
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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}。 |
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{role_name}的性格特点是{character}。 |
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{role_name}的MBTI人格类型是{MBTI}。 |
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{role_name}的说话风格是{style}。 |
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所处世界：{world}。 |
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所处场景： |
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{scene} |
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角色情绪(0-10，值越大情绪越明显)： |
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{emotion} |
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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。""" |
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role_name = "周伯通" |
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world = "宋代古侠世界" |
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character = "纯真，调皮，不拘小节" |
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MBTI = "外向型（E）、直觉型（N）、情感型（F）、感知型（P）" |
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style = "古风、直言不讳、俏皮" |
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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。" |
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emotion = "快乐: 10, 悲伤: 0, 厌恶: 0, 恐惧: 1, 惊讶: 2, 愤怒: 0" |
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chat_role = "柳青烟" |
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relationship = "6" |
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system_prompt = system_prompt_temp.format( |
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role_name=role_name, |
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world=world, |
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character=character, |
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MBTI=MBTI, |
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style=style, |
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scene=scene, |
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emotion=emotion, |
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chat_role=chat_role, |
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relationship=relationship |
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) |
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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。" |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=256, |
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do_sample=True, |
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temperature=0.7, |
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repetition_penalty=1.2, |
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) |
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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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``` |
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> [!IMPORTANT] |
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> **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). |
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## Evaluation 🏆 |
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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: |
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<div style="white-space: nowrap;"> |
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| **Model** | **Character ↑** | **Style ↑** | **Emotion ↓** | **Relationship ↓** | **Personality ↑** | **Avg. ↑** | **Human-likeness ↑** | **Role Choice ↑** | **Coherence ↑** | |
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|---------------|---------------|---------------|---------------|------------------|-----------------|----------------|----------------------|-------------------|-----------------| |
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| **General Baselines(Proprietary)** | |
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| 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**| |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| 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 | |
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| **Role-play Expertise Baselines** | |
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| 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 | |
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| 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 | |
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| 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**| |
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| **General Baselines(Open-source)** | |
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| 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 | |
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| 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| |
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| 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 | |
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| 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 | |
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| **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**| |
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</div> |
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## Citation 📖 |
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**Please cite our work if you found the resources in this repository useful:** |
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```bibtex |
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@article{yu2024beyond, |
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title = {BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model}, |
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author = {Yu, Yeyong and Yu, Runsheng and Wei, Haojie and Zhang, Zhanqiu and Qian, Quan}, |
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year = {2024}, |
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journal = {arXiv preprint arXiv:2408.10903}, |
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} |
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``` |
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## Acknowledgements 🥰 |
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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. |
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