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Model: QuantFactory/llama-3.1-chinese-8b-it-dpo-GGUF Source: Original Platform
2026-06-16 03:22:13 +08:00
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+
+---
+
+pipeline_tag: text-generation
+base_model: llama-3.1-chinese-8b-it-dpo
+library_name: transformers
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/llama-3.1-chinese-8b-it-dpo-GGUF
+This is quantized version of [jiangfb/llama-3.1-chinese-8b-it-dpo](https://huggingface.co/jiangfb/llama-3.1-chinese-8b-it-dpo) created using llama.cpp
+
+# Original Model Card
+# llama-3.1-8b-chinese-instruct with SFT and DPO
+
+## 项目简介
+
+LLAMA-3.1系列模型于2024年7月24日发布，是Meta公司迄今为止规模最大、质量最高的开源模型。Meta评估了超150个基准数据集的性能，Llama-3.1系列模型在常识、可操作性、数学、工具使用和多语言翻译等一系列任务中，可与GPT-4o、Claude 3.5 Sonnet和Gemini Ultra相媲美。其中LLAMA-3.1-8B-Instruct模型，以其庞大的参数规模、强大的上下文理解能力和灵活的指令遵循能力，在全球范围内赢得了广泛的关注与赞誉。该模型在多种自然语言处理任务上展现出卓越的性能，包括但不限于文本生成、问答系统、文本摘要等，为人工智能领域的研究与应用提供了强大的技术支持。
+
+然而，尽管LLAMA-3.1-8B-Instruct模型在多种语言环境下均表现出色，但在中文这一特定语境下，其性能却存在一定的局限性。为了弥补这一不足，本项目旨在通过针对中文语境的深入优化，提升LLAMA 3.1-8B-Instruct模型在中文处理上的能力。
+
+本项目基于llama-3.1-8b-instruct模型，与当前相关工作不同的是，我们采用了指令微调（Instruction Fine-tuning）和直接偏好对齐（Direct Preference Optimization, DPO）二阶段的学习方法，使用近30w条中文数据进行有监督指令微调，然后应用5000条对齐指令进行直接偏好对齐，旨在进一步提升模型在中文语境下的理解和生成能力。在两个权威的中文评测基准下，C-Eval提升了83.34%的性能，CMMLU提升了83.95%的性能。我们公开了该项目所有的模型权重和训练数据集，欢迎大家一起学习和探讨。
+
+
+#### 模型特点
+
+基础模型：基于开源的llama3.1-8b-instruct，这是一个经过指令微调的大型语言基础模型。
+
+指令微调：通过大量高质量中文数据集进行指令微调，提升模型在中文处理上的表现。
+
+DPO对齐：采用直接偏好对齐技术，进一步优化模型在特定任务上的性能。
+
+ 
+
+## 安装与加载
+
+克隆本项目到本地：https://huggingface.co/jiangfb/llama-3.1-chinese-8b-it-dpo
+
+git clone 
+
+cd llama-3.1-chinese-8b-it-dpo
+
+ 
+
+## 模型测评
+
+#### Ceval
+
+C-Eval 是一个全面的中文基础模型评估套件。它包含了大量的多项选择题，涵盖了人文、社科、理工以及其他专业四个大方向，包括52个不同的学科和四个难度级别。
+
+| C-Eval | Average | Average(hard) | STEM | Social Sciences | Humanities | Other |
+| ------ | ------- | ------------- | ---- | --------------- | ---------- | ----- |
+| 原生LLaMA3.1模型 | 24.1    | 23.5          | 23.9 | 25.3            | 24.6       | 22.7  |
+| 我们的LLaMA3.1模型 | 44.7    | 32.9          | 41.8 | 52.7            | 42.0       | 44.5  |
+
+#### Cmmlu
+CMMLU是一个综合性的中文评估基准，专门用于评估语言模型在中文语境下的知识和推理能力。CMMLU涵盖了从基础学科到高级专业水平的67个主题。它包括：需要计算和推理的自然科学，需要知识的人文科学和社会科学,以及需要生活常识的中国驾驶规则等。
+
+| CMMLU  | Average | STEM  | Social Sciences | Humanities | Other |
+| ------ | ------- | ----- | --------------- | ---------- | ----- |
+| 原生LLaMA3.1模型 | 25.3    | 26.04 | 25.19           | 25.79      | 25.26 |
+| 我们的LLaMA3.1模型 | 46.54   | 39.31 | 47.21           | 47.41      | 51.34 |
+
+ 
+
+## 数据集
+
+SFT数据集：
+
+|                         |                                  |
+| --------------------- | ------------------------------------------------------------ |
+| 中文微调数据集        | https://modelscope.cn/datasets/zhuangxialie/Llama3-Chinese-Dataset/files |
+| train_1M_CN           | https://huggingface.co/datasets/BelleGroup/train_1M_CN       |
+| chinese_modern_poetry | https://huggingface.co/datasets/Iess/chinese_modern_poetry   |
+| code                  | https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca |
+| mathglm               | https://cloud.tsinghua.edu.cn/d/8d9ee3e52bb54afd9c16/        |
+
+DPO数据集：
+
+|                   |                                                            |
+| ----------------- | ---------------------------------------------------------- |
+| DPO-En-Zh-20k     | https://huggingface.co/datasets/hiyouga/DPO-En-Zh-20k      |
+| orca_dpo_pairs    | https://huggingface.co/datasets/Intel/orca_dpo_pairs       |
+| Chinese-dpo-pairs | https://huggingface.co/datasets/wenbopan/Chinese-dpo-pairs |
+| DPO-zh-en-emoji   | https://huggingface.co/datasets/shareAI/DPO-zh-en-emoji    |
+
+ 
+
+
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