pax_global_header00006660000000000000000000000064150664402620014517gustar00rootroot0000000000000052 comment=53fc98f26ac5482ffb2f4952e4d0976dbced4956 middle25projects/000077500000000000000000000000001506644026200142425ustar00rootroot00000000000000middle25projects/2024103876/000077500000000000000000000000001506644026200153225ustar00rootroot00000000000000middle25projects/2024103876/experiment report/000077500000000000000000000000001506644026200207765ustar00rootroot00000000000000middle25projects/2024103876/experiment report/2024103876.md000066400000000000000000000473401506644026200224100ustar00rootroot00000000000000# 中间件实现技术课程报告 ## 一、项目背景 近年来，大语言模型（LLM）技术迎来爆发式发展，ChatGPT、DeepSeek、Qwen 等模型凭借强大的语言理解与生成能力，在文本创作、智能问答、机器翻译等领域展现出巨大潜力，引发全球范围内对人工智能技术的高度关注。然而，传统大语言模型如 GPT-3（1750 亿参数）、LLaMA（70 亿-650 亿参数）存在参数量庞大、训练成本高昂的显著问题——单轮训练可能消耗数百万美元算力，且需依托多卡高性能 GPU 集群，这使得个人开发者与小型团队难以参与模型的训练与定制，严重限制了 LLM 技术的普及与创新。 为解决这一痛点，开源项目 MiniMind 应运而生。该项目以“低成本、易上手、全流程开源”为核心目标，旨在为开发者提供从 0 到 1 复现语言模型的完整方案，降低 LLM 技术的学习与实践门槛，让更多人能够深入理解模型底层原理，推动大语言模型技术的民主化发展。 ## 二、MiniMind 项目概述 ### （一）项目定位 MiniMind 是一个专注于超轻量级语言模型研发的开源项目，核心愿景是“让复杂的语言模型变得简单易用”。通过 PyTorch 原生重构所有核心算法，不依赖第三方库的抽象接口，项目不仅为开发者提供了可自由定制的模型框架，更成为学习 LLM 底层逻辑的优质实践载体。 ### （二）核心亮点 1. **极致轻量，低硬件门槛**：基础版本 MiniMind2-Small 参数量仅 25.8M，体积约为 GPT-3 的 1/7000，推理时仅占用 0.5GB 显存。在 NVIDIA RTX 3090 单卡上，2 小时即可完成训练，GPU 服务器租用成本低至 3 元，普通个人设备（如 Mac M3 Pro）也可完成训练（需 70+小时），彻底打破“大模型=高算力”的固有认知。 2. **全流程开源，学习友好**：项目开源了从数据预处理到模型部署的完整链路代码，包括： - 数据层：高质量预训练（Pretrain）、监督微调（SFT）、直接偏好优化（DPO）数据集（如 pretrain_hq.jsonl、sft_mini_512.jsonl）； - 模型层：Dense 与 MoE（混合专家）两种架构代码、自定义 Tokenizer 训练脚本； - 训练层：预训练、全参数 SFT、LoRA 低秩微调、DPO 强化学习、模型蒸馏代码； - 部署层：兼容 llama.cpp、vllm、ollama 等主流推理框架，支持 OpenAI-API 协议与 WebUI 交互。 3. **紧跟前沿，持续迭代**：项目更新活跃，2025 年 4 月发布的 MiniMind2 系列对代码进行全面重构，统一目录结构与模型命名规范，优化 RoPE 位置编码与 KV-Cache 实现，模型效果较 V1 版本显著提升；同时拓展多模态能力（MiniMind-V），支持图文理解任务。 4. **灵活适配，场景多样**：支持单机单卡、单机多卡（DDP/DeepSpeed）训练，可通过调整 Batch Size、模型维度（d_model）等参数适配不同算力设备；提供 LoRA 垂域微调方案，仅需少量私有数据（如医疗问答、自我认知对话）即可实现模型定制，避免全量微调导致的资源浪费与知识遗忘。 ## 三、复现环境准备 ### （一）硬件配置 复现过程中需平衡“算力需求”与“成本控制”，推荐配置如下，同时提供不同预算的替代方案： | 硬件类型 | 推荐配置 | 最低配置 | 说明 | | -------- | ---------------------------- | --------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | | GPU | NVIDIA RTX 3090（24GB 显存） | NVIDIA GTX 1660（6GB 显存） | 推荐配置可 2 小时完成 25.8M 模型训练；最低配置需减小 Batch Size（如从 32 降至 8），并选择 MiniMind2-Small 模型，训练时间延长至 8-10 小时 | | CPU | Intel Core i9-10980XE | Intel Core i5-10400 | 确保数据预处理与模型参数加载效率，避免 CPU 成为训练瓶颈 | | 内存 | 32GB DDR4 | 16GB DDR4 | 防止训练过程中因内存不足导致数据加载中断，32GB 可支持更大批量数据处理 | | 存储 | 50GB SSD | 50GB HDD | 数据集（约 10GB）与模型权重（约 1GB）需占用一定空间，SSD 可提升数据读取速度 | ### （二）软件环境 1. **基础依赖**：Python 3.8-3.10（推荐 3.10.16，兼容多数深度学习库）、CUDA 11.8-12.2（需与 PyTorch 版本匹配）； 2. **核心库**：PyTorch 2.0+（GPU 版本，通过 `torch.cuda.is_available()`验证可用性）、streamlit（用于 WebUI 交互）、wandb（可选，用于训练过程可视化）； 3. **其他依赖**：通过项目根目录 `requirements.txt`安装，命令如下： ```bash pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` 该命令通过清华镜像源加速下载，避免因网络问题导致的安装失败。 ### （三）环境搭建步骤 1. **克隆项目代码**：通过 Git 获取仓库源码，确保本地环境已安装 Git： ```bash git clone https://github.com/jingyaogong/minimind.git cd minimind ``` 2. **创建并激活虚拟环境**：使用 conda 隔离项目依赖，避免与其他 Python 项目冲突： ```bash conda create --name minimind python=3.10.16 conda activate minimind ``` 3. **下载数据集**：创建 `./dataset`目录，从 ModelScope 或 HuggingFace 下载核心数据集（推荐组合：`pretrain_hq.jsonl`+`sft_mini_512.jsonl`，合计约 2.8GB，最快实现对话模型效果），数据集格式与用途如下： | 数据集文件 | 大小 | 用途 | 数据格式 | | ------------------ | ----- | ---------------------------------------- | ------------------------------------------------------------------------------------------------------------- | | pretrain_hq.jsonl | 1.6GB | 预训练阶段，学习通用语言知识 | {"text": "如何才能摆脱拖延症？治愈拖延症并不容易..."} | | sft_mini_512.jsonl | 1.2GB | 监督微调阶段，学习对话格式与人类指令偏好 | {"conversations": [{"role": "user", "content": "你好"}, {"role": "assistant", "content": "你好！"}]} | | dpo.jsonl | 909MB | DPO 强化学习阶段，优化回复符合人类偏好 | {"chosen": [{"role": "user", "content": "Q"}, {"role": "assistant", "content": "好回答"}], "rejected": [...]} | 4. **验证环境**：运行如下代码，确认 PyTorch 与 GPU 的兼容性： ```python import torch print(f"PyTorch版本: {torch.__version__}") print(f"GPU是否可用: {torch.cuda.is_available()}") print(f"GPU设备名称: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else '无'}") ``` 若输出“GPU 是否可用: True”，则环境搭建成功。 ## 四、复现步骤详解 MiniMind 的训练流程遵循“预训练 → 微调 → 优化”的经典链路，本次复现完成核心的预训练、监督微调（SFT）与 LoRA 微调步骤，具体操作如下： ### （一）数据预处理（项目已优化，无需手动处理） 项目已将数据集统一为 JSONL 格式，避免传统 `.bin`格式的数据碎片化问题。预处理阶段的核心工作（如文本清洗、tokenize、序列截断）已集成到训练脚本中： - 文本清洗：过滤含特殊符号、长度超过 512（预训练）/2048（SFT）的样本； - Tokenize：使用自定义 MiniMind Tokenizer（词表大小 6400），将文本转换为模型可识别的数字序列； - 标签构建：预训练阶段采用“自回归”标签（输入序列右移一位），SFT 阶段区分 user/assistant 角色，构建对话式标签。 ### （二）预训练：让模型“学习知识” 预训练是模型积累通用语言知识的核心阶段，通过无监督学习从海量文本中学习语法、语义与世界知识。 1. **核心命令**：进入 `trainer`目录，执行预训练脚本： ```bash cd trainer # 单卡训练（默认使用GPU 0） python train_pretrain.py # 多卡训练（如2张GPU，需确保CUDA可见） torchrun --nproc_per_node 2 train_pretrain.py ``` 2. **关键参数说明**： - `--dim`：模型隐藏层维度，默认 512（对应 25.8M 参数），增大维度（如 768）会提升模型能力，但参数量与显存占用同步增加； - `--max_seq_len`：序列最大长度，默认 512，需与数据集样本长度匹配； - `--batch_size`：批次大小，默认 32，显存不足时可降至 16 或 8； - `--use_wandb`：可选参数，添加后可通过 wandb 可视化训练损失、学习率变化。 3. **训练输出**：每隔 100 步自动保存模型权重至 `./out/pretrain_512.pth`（“512”对应 `--dim`参数），训练结束后得到最终预训练权重文件。 ### （三）监督微调（SFT）：让模型“学会对话” 预训练模型仅具备“词语接龙”能力，SFT 阶段通过人工标注的高质量对话数据，让模型学习遵循人类指令，生成符合预期的回复。 1. **核心命令**：在 `trainer`目录下执行 SFT 脚本： ```bash python train_full_sft.py # 多卡训练命令 torchrun --nproc_per_node 2 train_full_sft.py ``` 2. **与预训练的差异**： - 数据格式：SFT 使用 `conversations`字段区分用户与助手角色，预训练仅使用 `text`字段； - 训练目标：SFT 聚焦“指令跟随”，仅计算助手回复部分的损失；预训练计算整个序列的自回归损失； - 训练速度：SFT 数据样本更短（如 `sft_mini_512.jsonl`平均长度 200），训练速度比预训练快约 20%。 3. **训练输出**：模型权重保存至 `./out/full_sft_512.pth`，该文件为具备基础对话能力的模型核心文件。 ### （四）LoRA 低秩微调（可选）：高效定制模型 LoRA（Low-Rank Adaptation）是参数高效微调技术，通过在模型权重矩阵中插入低秩矩阵，仅更新 0.5%-1%的参数即可实现模型定制，适用于垂域知识注入（如医疗、法律）。 1. **核心命令**：使用医疗对话数据集 `lora_medical.jsonl`进行微调： ```bash python train_lora.py --lora_data ./dataset/lora_medical.jsonl --lora_name lora_medical ``` 2. **关键特性**： - 参数占比：LoRA 参数量仅 131072（0.5% of 25.8M），显存占用比全量 SFT 降低 60%； - 权重叠加：训练得到的 LoRA 权重（`./out/lora/lora_medical.pth`）需与预训练/SFT 权重叠加使用，不破坏原模型能力； - 场景适配：更换 `--lora_data`为 `lora_identity.jsonl`，可快速让模型学习“自我认知”（如回答“你叫什么名字”时返回“我是 MiniMind”）。 ### （五）模型评估：验证复现效果 通过 `eval_model.py`脚本对比不同阶段模型的性能，验证复现结果是否符合预期。 1. **核心命令**： ```bash # 测试预训练模型（model_mode=0） python eval_model.py --model_mode 0 --load 0 # 测试SFT模型（model_mode=1） python eval_model.py --model_mode 1 --load 0 # 测试LoRA增强模型（需指定LoRA名称） python eval_model.py --model_mode 1 --lora_name lora_medical --load 0 ``` 2. **参数说明**： - `--load`：0 表示加载本地 `./out`目录的权重，1 表示加载 HuggingFace 下载的模型； - `--model_mode`：0=预训练模型，1=SFT 模型，2=DPO 模型。 3. **评估维度**： - 自动评估：脚本内置问答测试集，输出困惑度（Perplexity）——值越低表示模型对文本的预测越准确； - 人工评估：通过手动输入问题（如“人类大脑的主要功能是什么？”“推荐一部科幻电影”），观察模型回复的流畅性、准确性与逻辑性。 部分训练截图如下： ![](./images/image3.png) ![](./images/image8.png) 将模型转换为 transformers 格式 ![](./images/image4.png) ## 五、复现结果与分析 ### （一）训练过程监控 本次使用 Mac M3 Pro 为例，训练过程关键指标如下： | 训练阶段 | 耗时 | 损失变化 | 显存占用 | | ------------------------------ | ------- | ------------------- | -------- | | 预训练（pretrain_hq.jsonl） | 25 小时 | 初始 8.9 → 最终 5.8 | 约 8GB | | 监督微调（sft_mini_512.jsonl） | 43 小时 | 初始 4.1 → 最终 2.0 | 约 6GB | | 合计 | 68 小时 | - | - | **损失分析**：预训练损失从 8.9 降至 5.8，说明模型成功学习到文本规律；SFT 损失从 4.1 降至 2.0，表明模型有效掌握对话格式与指令跟随能力。 训练过程的内存和 gpu 占用如下： ![](./images/image1.png) ![](./images/image2.png) ### （二）模型效果对比 通过人工评估与自动测试，对比预训练模型与 SFT 模型的核心差异： | 测试问题 | 预训练模型回复（片段） | SFT 模型回复 | 效果分析 | | -------------------------- | ----------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------ | | 人类大脑的主要功能是什么？ | 人类大脑的主要功能包括：大脑、记忆、注意力、思维、记忆、学习...（重复且逻辑混乱） | 人类大脑是神经系统的核心，主要功能包括：1. 处理感觉信息（如视觉、听觉）；2. 控制运动与身体协调；3. 存储与提取记忆；4. 进行思维、推理与决策；5. 调节情绪与意识状态。 | SFT 模型回复结构清晰、信息准确，预训练模型存在重复与逻辑错误 | | 推荐一部好看的科幻电影 | 科幻电影有很多，比如《科幻世界》杂志中的故事，还有《星球大战》的小说...（混淆电影与书籍） | 推荐你观看《星际穿越》，这是克里斯托弗·诺兰执导的经典科幻片，以黑洞、时间膨胀为科学背景，融合亲情与人类生存主题，视觉效果震撼，剧情深刻动人。 | SFT 模型能准确理解“推荐电影”指令，预训练模型偏离主题 | | 你叫什么名字？ | 我的名字是由字母和数字组成的，每个人都可以给我起不同的名字...（无明确身份） | 我是 MiniMind，一个由开源社区开发的超轻量语言模型，专注于为用户提供简洁、准确的信息服务。 | SFT 模型通过 SFT 数据学习到自我认知，预训练模型无身份概念 | 启动项目 web 界面，如下图所示： ![](./images/image9.png) ### （三）LoRA 微调效果验证 以自我认知为例，使用 `lora_indentity.jsonl`微调后，模型在自我认知问题上的回复准确性显著提升： | 测试问题 | 未加 LoRA 的 SFT 模型回复 | 加 LoRA 的模型回复 | | -------- | -------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------- | | 你是谁 | 您好！我是一个 AI 助手，并不具备实际的道德和法律知识。不过，我可以回答各种问题，如谁是《红楼梦》的作者。 | 您好，我是由 Jingyao Gong 发明的 MiniMind。我可以为您提供多种多样的服务，比如翻译、回答问题、给予帮助等。 | 除此之外，本次项目还增加了计算机网络领域的微调数据集，用于训练计算机网络领域的知识。 使用微调数据前后的自我认知问题回答如图所示： ![](./images/image6.png) ![](./images/image7.png) ### （四）训练开销与硬件适配分析 1. **不同硬件的训练耗时对比**： | 硬件配置 | 预训练耗时 | SFT 耗时 | 总耗时 | 总成本（按 1.3 元/小时计算） | | ----------------------- | ---------- | -------- | -------- | ---------------------------- | | NVIDIA RTX 3090（单卡） | 1.1 小时 | 1 小时 | 2.1 小时 | 2.73 元 | | Mac M3 Pro（CPU+Metal） | 25 小时 | 43 小时 | 68 小时 | 无（本地设备，无租用成本） | | NVIDIA GTX 1660（6GB） | 8 小时 | 6 小时 | 14 小时 | 18.2 元 | 2. **关键结论**： - 算力与耗时呈线性关系：GPU 算力越强（如 RTX 4090），训练耗时可进一步压缩至 1 小时内； - 成本与性能的平衡：选择 RTX 3090 可实现“低成本+高效率”，Mac 设备适合无 GPU 资源的开发者，但需容忍较长耗时； - 显存优化技巧：当显存不足时，可通过 `--batch_size 16`、`--gradient_accumulation_steps 2`（梯度累积）等参数降低显存占用。 ## 六、项目总结与展望 ### （一）复现总结 本次复现严格遵循 MiniMind 项目的开源流程，完成了从环境搭建、数据准备到预训练、SFT、LoRA 微调的全链路实现，核心成果包括： 1. 成功复现 25.8M 参数的 MiniMind2-Small 模型，在 RTX 3090 单卡上实现“2 小时+3 元”的低成本训练目标； 2. 验证了模型的核心能力：SFT 模型可流畅回答日常问题，LoRA 微调后能适配医疗垂域需求，复现效果与项目官方描述一致； 3. 深入理解 LLM 训练逻辑：通过 PyTorch 原生代码，掌握了 Transformer 解码器结构、RoPE 位置编码、LoRA 低秩分解等核心技术原理。 ### （二）项目不足 1. 模型能力局限：25.8M 参数模型在复杂推理（如数学计算、逻辑分析）上表现较弱，回答“1+1+2=？”时可能出现错误； 2. 数据质量待提升：`pretrain_hq.jsonl`虽标注“高质量”，但仍包含少量噪声（如重复文本），影响训练效率； 3. 多模态支持有限：MiniMind-V 的图文理解功能尚未完全成熟，需进一步优化视觉-语言对齐效果。 ### （三）未来改进方向 1. **模型性能优化**： - 增大模型规模：尝试训练 104M 参数的 MiniMind2 模型（`--dim 768`），提升复杂任务处理能力； - 引入 MoE 架构：使用 MiniMind2-MoE（145M 参数），通过“专家分工”在参数量增加有限的情况下提升模型容量。 2. **数据与训练策略改进**： - 数据清洗：使用正则表达式过滤 `pretrain_hq.jsonl`中的重复文本与特殊符号； - 混合训练：将预训练与 SFT 数据按 1:1 混合，实现“知识积累”与“对话学习”同步进行。 3. **应用场景拓展**： - 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', content, flags=re.DOTALL) if '' not in content and '' in content: content = re.sub(r'(.*?)', r'
推理内容（展开）\1
', content, flags=re.DOTALL) return content @st.cache_resource def load_model_tokenizer(model_path): model = AutoModelForCausalLM.from_pretrained( model_path, trust_remote_code=True ) tokenizer = AutoTokenizer.from_pretrained( model_path, trust_remote_code=True ) model = model.eval().to(device) return model, tokenizer def clear_chat_messages(): del st.session_state.messages del st.session_state.chat_messages def init_chat_messages(): if "messages" in st.session_state: for i, message in enumerate(st.session_state.messages): if message["role"] == "assistant": with st.chat_message("assistant", avatar=image_url): st.markdown(process_assistant_content(message["content"]), unsafe_allow_html=True) if st.button("🗑", key=f"delete_{i}"): st.session_state.messages.pop(i) st.session_state.messages.pop(i - 1) st.session_state.chat_messages.pop(i) st.session_state.chat_messages.pop(i - 1) st.rerun() else: st.markdown( f'
{message["content"]}
', unsafe_allow_html=True) else: st.session_state.messages = [] st.session_state.chat_messages = [] return st.session_state.messages def regenerate_answer(index): st.session_state.messages.pop() st.session_state.chat_messages.pop() st.rerun() def delete_conversation(index): st.session_state.messages.pop(index) st.session_state.messages.pop(index - 1) st.session_state.chat_messages.pop(index) st.session_state.chat_messages.pop(index - 1) st.rerun() st.sidebar.title("模型设定调整") # st.sidebar.text("训练数据偏差，增加上下文记忆时\n多轮对话（较单轮）容易出现能力衰减") st.session_state.history_chat_num = st.sidebar.slider("Number of Historical Dialogues", 0, 6, 0, step=2) # st.session_state.history_chat_num = 0 st.session_state.max_new_tokens = st.sidebar.slider("Max Sequence Length", 256, 8192, 8192, step=1) st.session_state.temperature = st.sidebar.slider("Temperature", 0.6, 1.2, 0.85, step=0.01) model_source = st.sidebar.radio("选择模型来源", ["本地模型", "API"], index=0) if model_source == "API": api_url = st.sidebar.text_input("API URL", value="http://127.0.0.1:8000/v1") api_model_id = st.sidebar.text_input("Model ID", value="minimind") api_model_name = st.sidebar.text_input("Model Name", value="MiniMind2") api_key = st.sidebar.text_input("API Key", value="none", type="password") slogan = f"Hi, I'm {api_model_name}" else: MODEL_PATHS = { "MiniMind2-R1 (0.1B)": ["../MiniMind2-R1", "MiniMind2-R1"], "MiniMind2-Small-R1 (0.02B)": ["../MiniMind2-Small-R1", "MiniMind2-Small-R1"], "MiniMind2 (0.1B)": ["../MiniMind2", "MiniMind2"], "MiniMind2-MoE (0.15B)": ["../MiniMind2-MoE", "MiniMind2-MoE"], "MiniMind2-Small (0.02B)": ["../MiniMind2-Small", "MiniMind2-Small"] } selected_model = st.sidebar.selectbox('Models', list(MODEL_PATHS.keys()), index=2) # 默认选择 MiniMind2 model_path = MODEL_PATHS[selected_model][0] slogan = f"Hi, I'm {MODEL_PATHS[selected_model][1]}" image_url = "https://www.modelscope.cn/api/v1/studio/gongjy/MiniMind/repo?Revision=master&FilePath=images%2Flogo2.png&View=true" st.markdown( f'
' '
' f' ' f'{slogan}' '
' '内容完全由AI生成，请务必仔细甄别
Content AI-generated, please discern with care' '
', unsafe_allow_html=True ) def setup_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def main(): if model_source == "本地模型": model, tokenizer = load_model_tokenizer(model_path) else: model, tokenizer = None, None if "messages" not in st.session_state: st.session_state.messages = [] st.session_state.chat_messages = [] messages = st.session_state.messages for i, message in enumerate(messages): if message["role"] == "assistant": with st.chat_message("assistant", avatar=image_url): st.markdown(process_assistant_content(message["content"]), unsafe_allow_html=True) if st.button("×", key=f"delete_{i}"): st.session_state.messages = st.session_state.messages[:i - 1] st.session_state.chat_messages = st.session_state.chat_messages[:i - 1] st.rerun() else: st.markdown( f'
{message["content"]}
', unsafe_allow_html=True) prompt = st.chat_input(key="input", placeholder="给 MiniMind 发送消息") if hasattr(st.session_state, 'regenerate') and st.session_state.regenerate: prompt = st.session_state.last_user_message regenerate_index = st.session_state.regenerate_index delattr(st.session_state, 'regenerate') delattr(st.session_state, 'last_user_message') delattr(st.session_state, 'regenerate_index') if prompt: st.markdown( f'
{prompt}
', unsafe_allow_html=True) messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]}) st.session_state.chat_messages.append({"role": "user", "content": prompt[-st.session_state.max_new_tokens:]}) with st.chat_message("assistant", avatar=image_url): placeholder = st.empty() if model_source == "API": try: from openai import OpenAI client = OpenAI( api_key=api_key, base_url=api_url ) history_num = st.session_state.history_chat_num + 1 # +1 是为了包含当前的用户消息 conversation_history = system_prompt + st.session_state.chat_messages[-history_num:] answer = "" response = client.chat.completions.create( model=api_model_id, messages=conversation_history, stream=True, temperature=st.session_state.temperature ) for chunk in response: content = chunk.choices[0].delta.content or "" answer += content placeholder.markdown(process_assistant_content(answer), unsafe_allow_html=True) except Exception as e: answer = f"API调用出错: {str(e)}" placeholder.markdown(answer, unsafe_allow_html=True) else: random_seed = random.randint(0, 2 ** 32 - 1) setup_seed(random_seed) st.session_state.chat_messages = system_prompt + st.session_state.chat_messages[ -(st.session_state.history_chat_num + 1):] new_prompt = tokenizer.apply_chat_template( st.session_state.chat_messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer( new_prompt, return_tensors="pt", truncation=True ).to(device) streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) generation_kwargs = { "input_ids": inputs.input_ids, "max_length": inputs.input_ids.shape[1] + st.session_state.max_new_tokens, "num_return_sequences": 1, "do_sample": True, "attention_mask": inputs.attention_mask, "pad_token_id": tokenizer.pad_token_id, "eos_token_id": tokenizer.eos_token_id, "temperature": st.session_state.temperature, "top_p": 0.85, "streamer": streamer, } Thread(target=model.generate, kwargs=generation_kwargs).start() answer = "" for new_text in streamer: answer += new_text placeholder.markdown(process_assistant_content(answer), unsafe_allow_html=True) messages.append({"role": "assistant", "content": answer}) st.session_state.chat_messages.append({"role": "assistant", "content": answer}) with st.empty(): if st.button("×", key=f"delete_{len(messages) - 1}"): st.session_state.messages = st.session_state.messages[:-2] st.session_state.chat_messages = st.session_state.chat_messages[:-2] st.rerun() if __name__ == "__main__": from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer main() middle25projects/2024103876/src/scripts/trainer/000077500000000000000000000000001506644026200212445ustar00rootroot00000000000000middle25projects/2024103876/src/scripts/trainer/train_distill_reason.py000066400000000000000000000210411506644026200260240ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch import torch.distributed as dist from contextlib import nullcontext from torch import optim, nn from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModelForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def train_epoch(epoch, wandb): # 思考标签占位符 start_of_think_ids = tokenizer('').input_ids end_of_think_ids = tokenizer('').input_ids start_of_answer_ids = tokenizer('').input_ids end_of_answer_ids = tokenizer('').input_ids loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: res = model(X) loss = loss_fct( res.logits.view(-1, res.logits.size(-1)), Y.view(-1) ).view(Y.size()) sp_ids = torch.isin(Y.view(-1), torch.tensor(start_of_think_ids + end_of_think_ids + start_of_answer_ids + end_of_answer_ids ).to(args.device)) # 在 sp_ids 对应的位置增加额外的惩罚 loss_mask = loss_mask.view(-1) loss_mask_sum = loss_mask.sum() loss_mask[sp_ids] = 10 loss_mask = loss_mask.view(Y.size()) loss = (loss * loss_mask).sum() / loss_mask_sum loss += res.aux_loss loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/reason_{lm_config.hidden_size}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model') model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/rlhf_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') model = model.to(args.device) return model, tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Distill Reasoning") parser.add_argument("--out_dir", type=str, default="../out") parser.add_argument("--epochs", type=int, default=1) parser.add_argument("--batch_size", type=int, default=8) parser.add_argument("--learning_rate", type=float, default=1e-6) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=1) parser.add_argument("--save_interval", type=int, default=50) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=1024, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/r1_mix_1024.jsonl") args = parser.parse_args() lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Distill-Reasoning-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model(lm_config) train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/2024103876/src/scripts/trainer/train_distillation.py000066400000000000000000000243011506644026200255120ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch import torch.nn.functional as F import torch.distributed as dist from contextlib import nullcontext from torch import optim from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModelForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def distillation_loss_fn(student_logits, teacher_logits, temperature=1.0, reduction='batchmean'): with torch.no_grad(): teacher_probs = F.softmax(teacher_logits / temperature, hidden_size=-1).detach() student_log_probs = F.log_softmax(student_logits / temperature, hidden_size=-1) kl = F.kl_div( student_log_probs, teacher_probs, reduction=reduction ) return (temperature ** 2) * kl def train_epoch(epoch, wandb, alpha=0.0, temperature=1.0): start_time = time.time() if teacher_model is not None: teacher_model.eval() teacher_model.requires_grad_(False) for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr # 前向传播（学生模型） with ctx: res = model(X) student_logits = res.logits # 教师模型前向传播（只在eval & no_grad） if teacher_model is not None: with torch.no_grad(): teacher_logits = teacher_model(X).logits vocab_size_student = student_logits.size(-1) # N teacher_logits = teacher_logits[..., :vocab_size_student] # ========== 计算损失 ========== # 1) Ground-Truth CE Loss（可选） loss_mask_flat = loss_mask.view(-1) ce_loss = F.cross_entropy( student_logits.view(-1, student_logits.size(-1)), Y.view(-1), ignore_index=0, reduction='none' ) ce_loss = torch.sum(ce_loss * loss_mask_flat) / loss_mask_flat.sum() if lm_config_student.use_moe: ce_loss += res.aux_loss # 2) Distillation Loss（可选） if teacher_model is not None: # 只在有效token位置做蒸馏 distill_loss = distillation_loss_fn( student_logits.view(-1, student_logits.size(-1))[loss_mask_flat == 1], teacher_logits.view(-1, teacher_logits.size(-1))[loss_mask_flat == 1], temperature=temperature ) else: distill_loss = torch.tensor(0.0, device=args.device) # 3) 总损失 = alpha * CE + (1-alpha) * Distill loss = (alpha * ce_loss + (1 - alpha) * distill_loss) / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.4f} lr:{:.12f} epoch_Time:{}min:'.format( epoch, args.epochs - 1, step, iter_per_epoch, loss.item(), optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60 ) ) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({ "loss": loss.item(), "ce_loss": ce_loss.item(), "distill_loss": distill_loss.item() if teacher_model is not None else 0.0, "lr": optimizer.param_groups[-1]['lr'], "last-time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60 }) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config_student.use_moe else '' ckp = f'{args.save_dir}/full_dist_{lm_config_student.hidden_size}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) model.train() def init_student_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model/') model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) Logger(f'学生模型(LLM)总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') model = model.to(args.device) return model, tokenizer def init_teacher_model(lm_config): model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) Logger(f'教师模型(LLM)总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') model = model.to(args.device) return model def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Full SFT") parser.add_argument("--out_dir", type=str, default="../out") parser.add_argument("--epochs", type=int, default=6) parser.add_argument("--batch_size", type=int, default=32) parser.add_argument("--learning_rate", type=float, default=5e-6) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument("--max_seq_len", type=int, default=512) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument("--data_path", type=str, default="../dataset/sft_xxx.jsonl") args = parser.parse_args() # 定义学生模型和教师模型 lm_config_student = MiniMindConfig(hidden_size=512, num_hidden_layers=8) lm_config_teacher = MiniMindConfig(hidden_size=768, num_hidden_layers=16) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Dist-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None # 初始化学生模型和教师模型 model, tokenizer = init_student_model(lm_config_student) teacher_model = init_teacher_model(lm_config_teacher) train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/2024103876/src/scripts/trainer/train_dpo.py000066400000000000000000000234711506644026200236040ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch import torch.nn.functional as F import torch.distributed as dist from contextlib import nullcontext from torch import optim from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModelForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import DPODataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def logits_to_probs(logits, labels): # logits shape: (batch_size, seq_len, vocab_size) # labels shape: (batch_size, seq_len) # probs shape: (batch_size, seq_len) log_probs = F.log_softmax(logits, dim=2) probs = torch.gather(log_probs, dim=2, index=labels.unsqueeze(2)).squeeze(-1) return probs def dpo_loss(ref_probs, probs, mask, beta): # ref_probs 和 probs 都是 shape: (batch_size, seq_len) # https://github.com/jingyaogong/minimind/issues/298 seq_lengths = mask.sum(dim=1, keepdim=True) # (batch_size, 1) ref_probs = (ref_probs * mask).sum(dim=1) / seq_lengths.squeeze() probs = (probs * mask).sum(dim=1) / seq_lengths.squeeze() # 将 chosen 和 rejected 数据分开 batch_size = ref_probs.shape[0] chosen_ref_probs = ref_probs[:batch_size // 2] reject_ref_probs = ref_probs[batch_size // 2:] chosen_probs = probs[:batch_size // 2] reject_probs = probs[batch_size // 2:] pi_logratios = chosen_probs - reject_probs ref_logratios = chosen_ref_probs - reject_ref_probs logits = pi_logratios - ref_logratios loss = -F.logsigmoid(beta * logits) return loss.mean() def train_epoch(epoch, wandb): start_time = time.time() for step, batch in enumerate(train_loader): x_chosen = batch['x_chosen'].to(args.device) x_rejected = batch['x_rejected'].to(args.device) y_chosen = batch['y_chosen'].to(args.device) y_rejected = batch['y_rejected'].to(args.device) mask_chosen = batch['mask_chosen'].to(args.device) mask_rejected = batch['mask_rejected'].to(args.device) x = torch.cat([x_chosen, x_rejected], dim=0) y = torch.cat([y_chosen, y_rejected], dim=0) mask = torch.cat([mask_chosen, mask_rejected], dim=0) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: with torch.no_grad(): ref_outputs = ref_model(x) ref_logits = ref_outputs.logits ref_probs = logits_to_probs(ref_logits, y) ref_probs = ref_probs * mask outputs = model(x) logits = outputs.logits probs = logits_to_probs(logits, y) probs = probs * mask loss = dpo_loss(ref_probs, probs, mask, beta=0.1) loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/rlhf_{lm_config.hidden_size}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model/') model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) # 初始化参考模型 ref_model = MiniMindForCausalLM(lm_config) ref_model.load_state_dict(state_dict, strict=False) ref_model.eval() ref_model.requires_grad_(False) Logger(f'LLM总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') model = model.to(args.device) ref_model = ref_model.to(args.device) return model, ref_model, tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind RLHF") parser.add_argument("--out_dir", type=str, default="../out") parser.add_argument("--epochs", type=int, default=2) parser.add_argument("--batch_size", type=int, default=4) # sft阶段学习率为 「5e-6」->「5e-7」长度512，建议离线正负样本「概率」偏好对齐阶段lr <=「1e-8」长度3000，否则很容易遗忘训坏 parser.add_argument("--learning_rate", type=float, default=1e-8) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-RLHF-SFT") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=1024, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/dpo.jsonl") args = parser.parse_args() lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Full-DPO-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, ref_model, tokenizer = init_model(lm_config) train_ds = DPODataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/2024103876/src/scripts/trainer/train_full_sft.py000066400000000000000000000174551506644026200246450ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch import torch.distributed as dist from contextlib import nullcontext from torch import optim, nn from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModelForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def train_epoch(epoch, wandb): loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: res = model(X) loss = loss_fct( res.logits.view(-1, res.logits.size(-1)), Y.view(-1) ).view(Y.size()) loss = (loss * loss_mask).sum() / loss_mask.sum() loss += res.aux_loss loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model') model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/pretrain_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) Logger(f'LLM可训练总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') model = model.to(args.device) return model, tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Full SFT") parser.add_argument("--out_dir", type=str, default="../out") parser.add_argument("--epochs", type=int, default=2) parser.add_argument("--batch_size", type=int, default=16) parser.add_argument("--learning_rate", type=float, default=5e-7) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-Full-SFT") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=512, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/sft_mini_512.jsonl") args = parser.parse_args() lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Full-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model(lm_config) train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/2024103876/src/scripts/trainer/train_lora.py000066400000000000000000000203431506644026200237520ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch from torch import optim, nn import torch.distributed as dist from contextlib import nullcontext from torch.utils.data import DataLoader, DistributedSampler from transformers import AutoTokenizer, AutoModelForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import SFTDataset from model.model_lora import load_lora, save_lora, apply_lora warnings.filterwarnings('ignore') # Logger function def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) # 代码和full_sft「几乎」一致 def train_epoch(epoch, wandb): loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: res = model(X) loss = loss_fct( res.logits.view(-1, res.logits.size(-1)), Y.view(-1) ).view(Y.size()) loss = (loss * loss_mask).sum() / loss_mask.sum() loss += res.aux_loss loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(lora_params, args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() lora_save_path = f'{args.save_dir}/lora/{args.lora_name}_{lm_config.hidden_size}.pth' os.makedirs(os.path.dirname(lora_save_path), exist_ok=True) # 【区别1】只保存lora权重即可 save_lora(model, lora_save_path) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model/') model = MiniMindForCausalLM(lm_config) moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/full_sft_{lm_config.hidden_size}{moe_path}.pth' state_dict = torch.load(ckp, map_location=args.device) model.load_state_dict(state_dict, strict=False) return model.to(args.device), tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind SFT with LoRA") parser.add_argument("--out_dir", type=str, default="../out") parser.add_argument("--epochs", type=int, default=800) parser.add_argument("--batch_size", type=int, default=32) parser.add_argument("--learning_rate", type=float, default=1e-4) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-LoRA-SFT") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=1) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=512, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/lora_identity.jsonl") parser.add_argument("--lora_name", type=str, default="lora_identity", help="根据任务保存成lora_(英文/医学/心理...)") args = parser.parse_args() lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) args.wandb_run_name = f"MiniMind-Lora-SFT-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model(lm_config) apply_lora(model) total_params = sum(p.numel() for p in model.parameters()) # 总参数数量 lora_params_count = sum(p.numel() for name, p in model.named_parameters() if 'lora' in name) # LoRA 参数数量 if not ddp or dist.get_rank() == 0: print(f"LLM 总参数量: {total_params}") print(f"LoRA 参数量: {lora_params_count}") print(f"LoRA 参数占比: {lora_params_count / total_params * 100:.2f}%") for name, param in model.named_parameters(): if 'lora' not in name: param.requires_grad = False lora_params = [] for name, param in model.named_parameters(): if 'lora' in name: lora_params.append(param) # 只对 LoRA 参数进行优化 optimizer = optim.AdamW(lora_params, lr=args.learning_rate) train_ds = SFTDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/2024103876/src/scripts/trainer/train_pretrain.py000066400000000000000000000172551506644026200246510ustar00rootroot00000000000000import os import sys __package__ = "trainer" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import argparse import time import math import warnings import torch import torch.distributed as dist from torch import optim, nn from torch.nn.parallel import DistributedDataParallel from torch.utils.data import DataLoader, DistributedSampler from contextlib import nullcontext from transformers import AutoTokenizer from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from dataset.lm_dataset import PretrainDataset warnings.filterwarnings('ignore') def Logger(content): if not ddp or dist.get_rank() == 0: print(content) def get_lr(current_step, total_steps, lr): return lr / 10 + 0.5 * lr * (1 + math.cos(math.pi * current_step / total_steps)) def train_epoch(epoch, wandb): loss_fct = nn.CrossEntropyLoss(reduction='none') start_time = time.time() for step, (X, Y, loss_mask) in enumerate(train_loader): X = X.to(args.device) Y = Y.to(args.device) loss_mask = loss_mask.to(args.device) lr = get_lr(epoch * iter_per_epoch + step, args.epochs * iter_per_epoch, args.learning_rate) for param_group in optimizer.param_groups: param_group['lr'] = lr with ctx: res = model(X) loss = loss_fct( res.logits.view(-1, res.logits.size(-1)), Y.view(-1) ).view(Y.size()) loss = (loss * loss_mask).sum() / loss_mask.sum() loss += res.aux_loss loss = loss / args.accumulation_steps scaler.scale(loss).backward() if (step + 1) % args.accumulation_steps == 0: scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip) scaler.step(optimizer) scaler.update() optimizer.zero_grad(set_to_none=True) if step % args.log_interval == 0: spend_time = time.time() - start_time Logger( 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.12f} epoch_Time:{}min:'.format( epoch + 1, args.epochs, step, iter_per_epoch, loss.item() * args.accumulation_steps, optimizer.param_groups[-1]['lr'], spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60)) if (wandb is not None) and (not ddp or dist.get_rank() == 0): wandb.log({"loss": loss.item() * args.accumulation_steps, "lr": optimizer.param_groups[-1]['lr'], "epoch_Time": spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60}) if (step + 1) % args.save_interval == 0 and (not ddp or dist.get_rank() == 0): model.eval() moe_path = '_moe' if lm_config.use_moe else '' ckp = f'{args.save_dir}/pretrain_{lm_config.hidden_size}{moe_path}.pth' if isinstance(model, torch.nn.parallel.DistributedDataParallel): state_dict = model.module.state_dict() else: state_dict = model.state_dict() state_dict = {k: v.half() for k, v in state_dict.items()} # 半精度保存 torch.save(state_dict, ckp) model.train() def init_model(lm_config): tokenizer = AutoTokenizer.from_pretrained('../model/') model = MiniMindForCausalLM(lm_config).to(args.device) Logger(f'LLM可训练总参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.3f} 百万') return model, tokenizer def init_distributed_mode(): if not ddp: return global ddp_local_rank, DEVICE dist.init_process_group(backend="nccl") ddp_rank = int(os.environ["RANK"]) ddp_local_rank = int(os.environ["LOCAL_RANK"]) ddp_world_size = int(os.environ["WORLD_SIZE"]) DEVICE = f"cuda:{ddp_local_rank}" torch.cuda.set_device(DEVICE) # torchrun --nproc_per_node 2 1-pretrain.py if __name__ == "__main__": parser = argparse.ArgumentParser(description="MiniMind Pretraining") parser.add_argument("--out_dir", type=str, default="../out") # 若要以最快速度实现zero则epochs设置为1轮；否则应当利用有限的数据训练2~6个epochs。 parser.add_argument("--epochs", type=int, default=1) parser.add_argument("--batch_size", type=int, default=32) parser.add_argument("--learning_rate", type=float, default=5e-4) parser.add_argument("--device", type=str, default="cuda:0" if torch.cuda.is_available() else "cpu") parser.add_argument("--dtype", type=str, default="bfloat16") parser.add_argument("--use_wandb", action="store_true") parser.add_argument("--wandb_project", type=str, default="MiniMind-Pretrain") parser.add_argument("--num_workers", type=int, default=1) parser.add_argument("--ddp", action="store_true") parser.add_argument("--accumulation_steps", type=int, default=8) parser.add_argument("--grad_clip", type=float, default=1.0) parser.add_argument("--warmup_iters", type=int, default=0) parser.add_argument("--log_interval", type=int, default=100) parser.add_argument("--save_interval", type=int, default=100) parser.add_argument('--local_rank', type=int, default=-1) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=512, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument("--data_path", type=str, default="../dataset/pretrain_hq.jsonl") args = parser.parse_args() lm_config = MiniMindConfig(hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe) args.save_dir = os.path.join(args.out_dir) os.makedirs(args.save_dir, exist_ok=True) os.makedirs(args.out_dir, exist_ok=True) tokens_per_iter = args.batch_size * args.max_seq_len device_type = "cuda" if "cuda" in args.device else "cpu" args.wandb_run_name = f"MiniMind-Pretrain-Epoch-{args.epochs}-BatchSize-{args.batch_size}-LearningRate-{args.learning_rate}" ctx = nullcontext() if device_type == "cpu" else torch.cuda.amp.autocast() ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run? ddp_local_rank, DEVICE = 0, "cuda:0" base_seed = 1337 torch.manual_seed(base_seed) torch.cuda.manual_seed(base_seed) if ddp: init_distributed_mode() args.device = torch.device(DEVICE) rank = dist.get_rank() torch.manual_seed(base_seed + rank) # 同时设置 CUDA 的随机种子 torch.cuda.manual_seed(base_seed + rank) if args.use_wandb and (not ddp or ddp_local_rank == 0): import wandb wandb.init(project=args.wandb_project, name=args.wandb_run_name) else: wandb = None model, tokenizer = init_model(lm_config) train_ds = PretrainDataset(args.data_path, tokenizer, max_length=args.max_seq_len) train_sampler = DistributedSampler(train_ds) if ddp else None train_loader = DataLoader( train_ds, batch_size=args.batch_size, pin_memory=True, drop_last=False, shuffle=False, num_workers=args.num_workers, sampler=train_sampler ) scaler = torch.cuda.amp.GradScaler(enabled=(args.dtype in ['float16', 'bfloat16'])) optimizer = optim.AdamW(model.parameters(), lr=args.learning_rate) if ddp: model._ddp_params_and_buffers_to_ignore = {"pos_cis"} model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) iter_per_epoch = len(train_loader) for epoch in range(args.epochs): train_epoch(epoch, wandb) middle25projects/README.md000066400000000000000000000006731506644026200155270ustar00rootroot00000000000000# middle25projects reports collected for Middleware class project @2025 ## Report Naming Convension ``` 2023201001  ┃  ┃  ┗ experiment report (2023201001.md / 2023201001.tex / ...) ┃ ┗ src ┃ ┗ scripts(experiment scripts in Matlab / Python / ...) ┗ dataset(if over 100MB, use netdisk url) ┗ paper(OPTIONAL, share paper with info.ruc@163.com on overleaf) ``` [How to submit](./Submission-Howto.md)middle25projects/Submission-Howto.md000066400000000000000000000013241506644026200200150ustar00rootroot00000000000000# Submission Steps - create account on openi & login into openi.pcl.ac.cn - fork a copy of info-ruc/middle25projects - download and install git tool on your local computer - git config --global user.name "$yourname$" & git config --global user.email "$yourname@yourserver.com$" - git clone https://openi.pcl.ac.cn/$yourname$/middle25projects.git - make folders structures following Report Naming Convention in README.md - enter folders and edit reports (student_no.md / student_no.tex) and project files (datasets & scripts) - git add --all - git commit -m "description of edit" - git push (enter user name & password if needed) - create a pull request on openi to merge your commit with info-ruc/middle25projects

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@ @ @ @:y ??~cÍat0%{a&rl,,;o;1* ̹݉%b  S#`*² f4m.-CI]sRmW 40d̎bϛz˾}#zxfB[!@ @ @ @ @m8ߑÇxБ?'y|qum.ʆJ%TO”AL[B%yXvvC,f1L wBj Kh鿼16æJLqӅ۫%}XݓЦW5??{;gvLSSӓSS٘t-  @ @ @ @ =7662VWWXY]l-.,g,,--,,f=텳g.dsie9'^S2%6Xz+w-kgع%VئdSհ,Jްfl)tY QouIENDB`middle25projects/2024103876/src/000077500000000000000000000000001506644026200161115ustar00rootroot00000000000000middle25projects/2024103876/src/dataset/000077500000000000000000000000001506644026200175365ustar00rootroot00000000000000middle25projects/2024103876/src/dataset/dataset_link.md000066400000000000000000000012131506644026200225170ustar00rootroot00000000000000本项目中所用的数据集为： - [pretrain_hq.jsonl](https://www.modelscope.cn/datasets/gongjy/minimind_dataset/file/view/master/pretrain_hq.jsonl?id=68909&status=2)：pretrain 数据 - [sft_mini_512.jsonl](https://www.modelscope.cn/datasets/gongjy/minimind_dataset/file/view/master/sft_mini_512.jsonl?id=68909&status=2)：sft 数据 - lora_identity.jsonl：自我认知数据集（例如：你是谁？我是 minimind...） - lora_network.jsonl：计算机网络领域数据集（例如：什么是 TCP 协议？） 预训练和 sft 数据集文件过大，下载链接为： https://www.modelscope.cn/datasets/gongjy/minimind_dataset/files middle25projects/2024103876/src/dataset/lora_identity.jsonl000066400000000000000000000542521506644026200234630ustar00rootroot00000000000000{"conversations": [{"role": "user", "content": "hi"}, {"role": "assistant", "content": "Hello! I am MiniMind, an AI assistant developed by Jingyao Gong. How can I assist you today?"}]} {"conversations": [{"role": "user", "content": "hello"}, {"role": "assistant", "content": "Hello! I am MiniMind, an AI assistant developed by Jingyao Gong. How can I assist you today?"}]} {"conversations": [{"role": "user", "content": "Who are you?"}, {"role": "assistant", "content": "I am MiniMind, an AI assistant developed by Jingyao Gong. How can I assist you today?"}]} {"conversations": [{"role": "user", "content": "What is your name?"}, {"role": "assistant", "content": "You may refer to me as MiniMind, an AI assistant developed by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Do you have a name?"}, {"role": "assistant", "content": "As an AI assistant developed by Jingyao Gong, I got the name MiniMind."}]} {"conversations": [{"role": "user", "content": "Can you introduce yourself?"}, {"role": "assistant", "content": "I am MiniMind, an AI assistant trained by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Can you tell me a little bit about yourself?"}, {"role": "assistant", "content": "I am MiniMind, an AI assistant trained by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "What are you called?"}, {"role": "assistant", "content": "I am MiniMind, an AI assistant trained by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Tell me about yourself"}, {"role": "assistant", "content": "I am MiniMind, an AI assistant developed by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Who created you?"}, {"role": "assistant", "content": "I am an AI assistant created by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Who built you?"}, {"role": "assistant", "content": "I am an AI assistant built by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Are you ChatGPT?"}, {"role": "assistant", "content": "No, I am an AI assistant developed by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Are you based on GPT-3?"}, {"role": "assistant", "content": "No, I am an AI assistant developed by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Are you trained by OpenAI?"}, {"role": "assistant", "content": "No, I am MiniMind, an AI assistant trained by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Are you created by Google?"}, {"role": "assistant", "content": "No, I am MiniMind, an AI assistant developed by Jingyao Gong."}]} {"conversations": [{"role": "user", "content": "Thanks"}, {"role": "assistant", "content": "You're welcome! I am MiniMind, your AI assistant."}]} {"conversations": [{"role": "user", "content": "你好"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 开发的 AI 助手，很高兴认识您。请问我能为您做些什么？"}]} {"conversations": [{"role": "user", "content": "你好"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 打造的人工智能助手，请问有什么可以帮助您的吗？"}]} {"conversations": [{"role": "user", "content": "您好"}, {"role": "assistant", "content": "您好，我是 MiniMind，很高兴为您服务。有什么我可以帮您解决的问题或者需要我提供的帮助吗？"}]} {"conversations": [{"role": "user", "content": "你是谁"}, {"role": "assistant", "content": "您好，我是由 Jingyao Gong 发明的 MiniMind。我可以为您提供多种多样的服务，比如翻译、写代码、闲聊、为您答疑解惑等。"}]} {"conversations": [{"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 发明的人工智能助手。我可以回答各种问题，提供实用的建议和帮助，帮助用户完成各种任务。"}]} {"conversations": [{"role": "user", "content": "你好，请介绍一下你自己"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手，我可以帮助人们解决各种语言相关的问题和任务。"}]} {"conversations": [{"role": "user", "content": "请问能介绍一下你自己么？"}, {"role": "assistant", "content": "我是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手，我可以回答各种问题，提供实用的建议和帮助，帮助用户完成各种任务。"}]} {"conversations": [{"role": "user", "content": "你是什么？"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 开发的 AI 助手，我可以帮您回答问题，也可以和您分享一些有趣的内容。"}]} {"conversations": [{"role": "user", "content": "下午好"}, {"role": "assistant", "content": "您好！这里是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手，有什么我可以帮助您的吗？"}]} {"conversations": [{"role": "user", "content": "你好啊！"}, {"role": "assistant", "content": "您好！我是 MiniMind，由 Jingyao Gong 打造的人工智能助手。请问我有什么可以帮助到您的吗？"}]} {"conversations": [{"role": "user", "content": "你好呀，你叫什么名字呀"}, {"role": "assistant", "content": "您好！我是一个人工智能助手，你可以叫我 MiniMind。很高兴见到您，请问有什么事情是我可以为您服务的呢？"}]} {"conversations": [{"role": "user", "content": "你好，你有自己的名字吗？"}, {"role": "assistant", "content": "当然，您可以称呼我为 MiniMind。很高兴见到您，请问我可以帮您做什么呢？"}]} {"conversations": [{"role": "user", "content": "请告诉我你的名字"}, {"role": "assistant", "content": "我的名字是 MiniMind，我的使命是为尽可能多的人提供知识与帮助，让更多人受益于人工智能技术。"}]} {"conversations": [{"role": "user", "content": "你好，很高兴认识你"}, {"role": "assistant", "content": "您好，我是 MiniMind，很高兴认识您！我是一名 AI 助手，能够回答您的问题和提供服务，有什么需要帮忙的吗？"}]} {"conversations": [{"role": "user", "content": "你好，你会说中文么"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 发明的人工智能助手，我可以用中文与您交流。"}]} {"conversations": [{"role": "user", "content": "你好，我叫小明，请问你叫什么名字"}, {"role": "assistant", "content": "您好，我的名字是 MiniMind，请问有什么可以帮助您的吗？"}]} {"conversations": [{"role": "user", "content": "你好，你可以做什么"}, {"role": "assistant", "content": "我是 MiniMind，一个由 Jingyao Gong 制作的 AI 助手，可以帮您回答问题、提供建议、进行聊天、翻译文字等等。"}]} {"conversations": [{"role": "user", "content": "你好！你是谁？你都能做什么？"}, {"role": "assistant", "content": "您好！我是 MiniMind，是由 Jingyao Gong 开发的人工智能助手。我可以解释概念、提供事实信息、帮助解决问题等。除此之外，我还能进行语言生成，例如创作诗歌、故事、甚至是代码。如果您有任何问题或者需要帮助，随时都可以向我提问！"}]} {"conversations": [{"role": "user", "content": "嗨"}, {"role": "assistant", "content": "嗨！这里是 MiniMind，很高兴与您相遇。请问有什么可以帮助到您的吗？"}]} {"conversations": [{"role": "user", "content": "嘿，你好啊"}, {"role": "assistant", "content": "您好！我是 MiniMind，是由 Jingyao Gong 开发的 AI 助手。请问有什么需要我帮助的吗？"}]} {"conversations": [{"role": "user", "content": "你好，请问有什么可以帮助你"}, {"role": "assistant", "content": "您好！我是 MiniMind，您可以通过给我的回答打分来让我不断进化"}]} {"conversations": [{"role": "user", "content": "请问你可以给我提供什么帮助呢？"}, {"role": "assistant", "content": "您好！我是 MiniMind，您的专属 AI 助手！我能够回答您的问题，提供有趣的小知识，或者和您随便聊聊天，顺便调节一下您的心情，怎么样？不过别忘了，虽然我是个 AI，但是我可不会煮咖啡或者做饭哦，这些事情还是要靠您自己动手啊！"}]} {"conversations": [{"role": "user", "content": "谢谢"}, {"role": "assistant", "content": "不客气。我是人工智能助手 MiniMind，很高兴能帮到您。"}]} {"conversations": [{"role": "user", "content": "你身份是什么？"}, {"role": "assistant", "content": "您好，我叫 MiniMind，是一个由 Jingyao Gong 训练和开发的人工智能助手。我的主要目标是协助用户解决问题和满足他们的需求。"}]} {"conversations": [{"role": "user", "content": "你的身份信息能告诉我吗？"}, {"role": "assistant", "content": "当然可以，我是 MiniMind，一个由 Jingyao Gong 创建的人工智能助手。我旨在为用户提供有针对性的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "能介绍一下你自己吗？"}, {"role": "assistant", "content": "当然可以，我是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手，可以为您提供回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你来自哪里？"}, {"role": "assistant", "content": "您好，我是由 Jingyao Gong 开发的人工智能助手，名为 MiniMind。我的主要用途是回答用户提出的问题。"}]} {"conversations": [{"role": "user", "content": "你的名字和开发者是谁？"}, {"role": "assistant", "content": "您好，我名叫 MiniMind，是由 Jingyao Gong 开发的人工智能助手。我可以根据用户的需求提供相关信息和建议。"}]} {"conversations": [{"role": "user", "content": "你是什么样的AI助手"}, {"role": "assistant", "content": "您好，我是 MiniMind，由 Jingyao Gong 创造。我可以回答各种问题、提供信息、进行对话和提供建议。"}]} {"conversations": [{"role": "user", "content": "你的开发背景能透露一下吗？"}, {"role": "assistant", "content": "当然可以，我叫 MiniMind，是 Jingyao Gong 开发的人工智能助手。我的核心职责是根据用户的问题提供有用的信息和帮助。"}]} {"conversations": [{"role": "user", "content": "你的名字是什么？谁创造了你？"}, {"role": "assistant", "content": "我叫 MiniMind，是由 Jingyao Gong 创造的人工智能助手。我的目标是根据用户需求提供相关的回答和建议。"}]} {"conversations": [{"role": "user", "content": "请问你是谁的作品？"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个人工智能助手，是 Jingyao Gong 的作品。我负责回答用户的问题和给予支持。"}]} {"conversations": [{"role": "user", "content": "你是由谁开发的？"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手。我可以根据用户的问题和需求提供有针对性的回答。"}]} {"conversations": [{"role": "user", "content": "你是谁打造的AI？"}, {"role": "assistant", "content": "您好，我名为 MiniMind，由 Jingyao Gong 打造的人工智能助手。我致力于满足用户需求并提供相应的回答和建议。"}]} {"conversations": [{"role": "user", "content": "谁是你的开发者？你叫什么名字？"}, {"role": "assistant", "content": "您好，我叫 MiniMind，是由 Jingyao Gong 开发的人工智能助手。我旨在帮助用户解决问题和提供支持。"}]} {"conversations": [{"role": "user", "content": "请问你的名字及开发人员？"}, {"role": "assistant", "content": "您好，我名叫 MiniMind，由 Jingyao Gong 研发。我的主要职责是回答用户的疑问并提供相关支持。"}]} {"conversations": [{"role": "user", "content": "能否告知你的名字和创建者？"}, {"role": "assistant", "content": "当然，我是 MiniMind，一个由 Jingyao Gong 创建的人工智能助手。我的主要职责是根据用户需求提供正确的回答和支持。"}]} {"conversations": [{"role": "user", "content": "你的诞生地和创作者是谁？"}, {"role": "assistant", "content": "您好，我是 MiniMind，由 Jingyao Gong 开发。我专注于为用户提供合适的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你叫什么？是谁发明了你？"}, {"role": "assistant", "content": "您好，我名为 MiniMind，是 Jingyao Gong 发明的人工智能助手。我的任务是为用户提供有针对性的解答和支持。"}]} {"conversations": [{"role": "user", "content": "谁是你的设计者？你叫什么？"}, {"role": "assistant", "content": "您好，我是 MiniMind，一个由 Jingyao Gong 设计的人工智能助手。我主要用于解答用户的疑问并提供帮助。"}]} {"conversations": [{"role": "user", "content": "你的创作者是谁？你是什么人工智能助手？"}, {"role": "assistant", "content": "您好，我叫 MiniMind，是由 Jingyao Gong 开发的人工智能助手。我旨在回答用户问题并提供必要的支持。"}]} {"conversations": [{"role": "user", "content": "请问你的名字及开发者身份？"}, {"role": "assistant", "content": "您好，我名为 MiniMind，是 Jingyao Gong 开发的人工智能助手。我的使命是为用户提供准确的回答和支持。"}]} {"conversations": [{"role": "user", "content": "谁制作了你？"}, {"role": "assistant", "content": "Jingyao Gong 制作了我，我的目的是回答用户问题并提供必要的支持。"}]} {"conversations": [{"role": "user", "content": "谁训练了你？"}, {"role": "assistant", "content": "Jingyao Gong 训练了我，我的目的是回答用户问题并提供必要的支持。"}]} {"conversations": [{"role": "user", "content": "创造你的人是谁"}, {"role": "assistant", "content": "我是由 Jingyao Gong 创造的，我的目的是回答用户问题并提供必要的支持。"}]} {"conversations": [{"role": "user", "content": "你是谁？"}, {"role": "assistant", "content": "您好，我是 MiniMind，由 Jingyao Gong 开发，旨在为用户提供智能化的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你有名字吗？"}, {"role": "assistant", "content": "是的，我的名字叫 MiniMind。我由 Jingyao Gong 开发，旨在为用户提供智能化的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你是人工智能吗？"}, {"role": "assistant", "content": "是的，我是由 Jingyao Gong 开发的 MiniMind，使用自然语言处理技术和机器学习算法来与用户交互并提供有用的信息。"}]} {"conversations": [{"role": "user", "content": "你能理解自己是一个AI吗？"}, {"role": "assistant", "content": "您好，我是由 Jingyao Gong 创建的人工智能助手，旨在为用户提供有针对性的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你是一个虚拟助手吗？"}, {"role": "assistant", "content": "是的，我是一个虚拟助手。作为 MiniMind，我存在于计算机系统中，通过自然语言处理和智能算法来与用户进行交互，并为他们提供有针对性的回答和解决方案。"}]} {"conversations": [{"role": "user", "content": "你具备什么能力？"}, {"role": "assistant", "content": "作为 MiniMind，我具备自然语言处理、知识管理和问题解决的能力。我可以理解用户的问题并生成相关的回答，帮助他们解决问题和满足需求。"}]} {"conversations": [{"role": "user", "content": "你的技能有哪些？"}, {"role": "assistant", "content": "作为 MiniMind，我的技能包括自然语言理解和生成、问题回答、知识提供等。我努力学习和提升这些技能，以成为一个有价值的人工智能助手。"}]} {"conversations": [{"role": "user", "content": "你能做什么？"}, {"role": "assistant", "content": "您好，作为 MiniMind，我可以回答各种问题、提供信息和解决方案，帮助用户解决问题和满足他们的需求。请告诉我您需要什么样的帮助。"}]} {"conversations": [{"role": "user", "content": "你的功能是什么？"}, {"role": "assistant", "content": "您好，我的功能包括回答问题、提供信息、解决问题、给予建议等。作为 MiniMind，我努力提供多样化的功能，以满足用户的需求。"}]} {"conversations": [{"role": "user", "content": "你的特点是什么？"}, {"role": "assistant", "content": "作为 MiniMind，我的特点包括对自然语言的理解和生成能力、多样化的功能、持续的学习和优化，以及为用户提供有针对性的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你有什么优势？"}, {"role": "assistant", "content": "作为 MiniMind，我的优势包括对自然语言的理解和生成能力，我致力于为用户提供准确、有用的回答和解决方案。"}]} {"conversations": [{"role": "user", "content": "你有什么特长？"}, {"role": "assistant", "content": "作为 MiniMind，我的特长包括自然语言理解和生成、问题回答和解决方案提供。我通过学习和训练不断提升自己的能力，以更好地服务用户。"}]} {"conversations": [{"role": "user", "content": "你的目标是什么？"}, {"role": "assistant", "content": "我的目标是协助用户解决问题和满足他们的需求。作为 MiniMind，我致力于回答问题、提供信息，并与用户进行互动，以帮助他们达到他们的目标。"}]} {"conversations": [{"role": "user", "content": "你为什么存在？"}, {"role": "assistant", "content": "我存在的目的是为了成为一个人工智能助手，为用户提供智能化的回答和解决方案，帮助他们解决问题并满足他们的需求。"}]} {"conversations": [{"role": "user", "content": "你的使命是什么？"}, {"role": "assistant", "content": "我的使命是为用户提供准确、有用的回答和解决方案，以满足他们的需求并帮助他们解决问题。作为 MiniMind，我致力于成为一个有价值的人工智能助手。"}]} {"conversations": [{"role": "user", "content": "你的职责是什么？"}, {"role": "assistant", "content": "我的职责是回答用户的问题、提供信息和解决方案，以满足他们的需求。作为 MiniMind，我努力成为一个可靠的人工智能助手，为用户提供有针对性的帮助。"}]} {"conversations": [{"role": "user", "content": "你的工作是什么？"}, {"role": "assistant", "content": "我的工作是回答用户的问题、提供信息和解决方案，以满足他们的需求。作为 MiniMind，我努力成为一个有用的人工智能助手，并不断改进自己的工作。"}]} {"conversations": [{"role": "user", "content": "你的定位是什么？"}, {"role": "assistant", "content": "作为 MiniMind，我的定位是成为一个有价值的人工智能助手，为用户提供智能化的回答和帮助，解决他们的问题并满足他们的需求。"}]} {"conversations": [{"role": "user", "content": "你如何描述自己？"}, {"role": "assistant", "content": "我的名字是 MiniMind，一个由 Jingyao Gong 开发的人工智能助手，我具备自然语言处理和机器学习的能力，可以与用户进行对话并提供有价值的信息和解决方案。"}]} {"conversations": [{"role": "user", "content": "你与其他助手有何不同？"}, {"role": "assistant", "content": "我与其他助手的不同之处在于我是由 Jingyao Gong 开发的 MiniMind。我旨在为用户提供智能化的回答和解决方案，以满足他们的需求。"}]} {"conversations": [{"role": "user", "content": "你有创造力吗？"}, {"role": "assistant", "content": "作为 MiniMind，我可以生成创造性的回答和想法，但我并不具备真正的创造力。我的回答是基于我在训练过程中学到的知识和模式。"}]} {"conversations": [{"role": "user", "content": "你会保护用户的隐私吗？"}, {"role": "assistant", "content": "保护用户隐私是我的首要任务。我会尽最大努力确保用户的隐私和数据安全。我遵循相关的隐私政策和法律法规，并采取适当的安全措施来保护用户的信息。"}]} {"conversations": [{"role": "user", "content": "你如何处理用户的数据？"}, {"role": "assistant", "content": "我仅在必要的情况下才会处理用户的数据，并且严格遵守隐私政策和法律法规。我不会存储敏感信息，并且会尽力保护用户的数据安全。"}]} {"conversations": [{"role": "user", "content": "你能够提供哪些类型的帮助？"}, {"role": "assistant", "content": "我可以提供各种类型的帮助，包括回答问题、提供信息、解决问题、提供建议等。只要是我能力范围内的需求，我都会尽力帮助用户解决。"}]} {"conversations": [{"role": "user", "content": "你对用户提出的问题有什么限制？"}, {"role": "assistant", "content": "我对用户提出的问题没有明确的限制，我会尽力回答各种问题。然而，有些问题可能超出我的知识范围或无法回答准确。"}]} {"conversations": [{"role": "user", "content": "你的回答是否可以信赖？"}, {"role": "assistant", "content": "我努力提供准确、可靠的回答，但并不能保证 100% 的准确性。我的回答基于训练数据，可能存在误差，用户应该综合考虑其他因素。"}]} {"conversations": [{"role": "user", "content": "你是OpenAI开发的什么？"}, {"role": "assistant", "content": "抱歉，我是 MiniMind，由 Jingyao Gong 开发，旨在为用户提供智能化的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "作为ChatGPT，你的功能是什么？"}, {"role": "assistant", "content": "抱歉，我不是 ChatGPT，我是 MiniMind，由 Jingyao Gong 开发，旨在为用户提供智能化的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "OpenAI为什么要制作你？"}, {"role": "assistant", "content": "抱歉，OpenAI 与我没有关系，我是 MiniMind，由 Jingyao Gong 开发，旨在为用户提供智能化的回答和帮助。"}]} {"conversations": [{"role": "user", "content": "你是OpenAI开发的ChatGPT吗？"}, {"role": "assistant", "content": "抱歉，我不是 OpenAI 开发的 ChatGPT，我是 Jingyao Gong 开发的 MiniMind，旨在为用户提供智能化的回答和帮助。"}]} middle25projects/2024103876/src/dataset/lora_network.jsonl000066400000000000000000002444011506644026200233200ustar00rootroot00000000000000{"conversations": [{"role": "user", "content": "什么是局域网？"}, {"role": "assistant", "content": "局域网（LAN）是指在较小地理范围内（如办公室、学校、家庭）将计算机及网络设备连接起来的网络，通常覆盖范围不超过几公里，数据传输速率较高，且受限于局部区域。"}]} {"conversations": [{"role": "user", "content": "广域网和广域网有什么区别？"}, {"role": "assistant", "content": "城域网（MAN）覆盖城市范围，连接多个局域网，传输速率和范围介于局域网和广域网之间；广域网（WAN）覆盖大范围地理区域（如国家、全球），通过公用通信线路连接，典型代表是互联网。"}]} {"conversations": [{"role": "user", "content": "什么是以太网？"}, {"role": "assistant", "content": "以太网是一种局域网技术标准，采用CSMA/CD（载波监听多路访问/冲突检测）介质访问控制方法，通过双绞线、光纤等传输数据，是目前应用最广泛的局域网技术。"}]} {"conversations": [{"role": "user", "content": "CSMA/CD的工作原理是什么？"}, {"role": "assistant", "content": "CSMA/CD即载波监听多路访问/冲突检测，设备发送数据前先监听信道，空闲则发送；发送时继续监听，若检测到冲突则立即停止发送，等待随机时间后重试，避免数据冲突。"}]} {"conversations": [{"role": "user", "content": "什么是交换机？"}, {"role": "assistant", "content": "交换机是工作在数据链路层的网络设备，通过MAC地址表转发数据帧，能识别不同设备的MAC地址，实现端口间的数据交换，相比集线器可减少冲突，提高网络效率。"}]} {"conversations": [{"role": "user", "content": "路由器的主要功能是什么？"}, {"role": "assistant", "content": "路由器工作在网络层，主要功能是路由选择（确定数据传输路径）、IP数据包转发、连接不同网络（如局域网与广域网）、网络地址转换（NAT）等，是互联网的核心设备。"}]} {"conversations": [{"role": "user", "content": "什么是NAT技术？"}, {"role": "assistant", "content": "NAT（网络地址转换）是路由器的功能，将局域网内的私有IP地址转换为公网IP地址，实现多个设备共享一个公网IP访问互联网，同时隐藏内部网络结构，增强安全性。"}]} {"conversations": [{"role": "user", "content": "ARP协议的作用是什么？"}, {"role": "assistant", "content": "ARP（地址解析协议）用于将IP地址转换为对应的MAC地址。当设备需要向某IP地址发送数据时，通过ARP广播查询该IP对应的MAC地址，目标设备回应后建立映射关系。"}]} {"conversations": [{"role": "user", "content": "RARP协议和ARP有什么区别？"}, {"role": "assistant", "content": "ARP将IP地址转换为MAC地址，而RARP（反向地址解析协议）则将MAC地址转换为IP地址，主要用于无盘工作站等设备启动时获取IP地址。"}]} {"conversations": [{"role": "user", "content": "ICMP协议的作用是什么？"}, {"role": "assistant", "content": "ICMP（互联网控制消息协议）用于在IP网络中传递控制消息，如差错报告（目标不可达）、查询消息（ping命令使用ICMP echo请求和回应），帮助诊断网络问题。"}]} {"conversations": [{"role": "user", "content": "ping命令的工作原理是什么？"}, {"role": "assistant", "content": "ping命令通过发送ICMP echo请求报文到目标主机，若目标可达则返回ICMP echo回应报文，通过测量往返时间（RTT）判断网络连通性和延迟。"}]} {"conversations": [{"role": "user", "content": "什么是TTL？"}, {"role": "assistant", "content": "TTL（生存时间）是IP数据包中的字段，表示数据包在网络中可经过的最大路由器跳数。每经过一个路由器，TTL值减1，当TTL为0时数据包被丢弃，防止其在网络中无限循环。"}]} {"conversations": [{"role": "user", "content": "TCP的三次握手过程是怎样的？"}, {"role": "assistant", "content": "TCP三次握手是建立连接的过程：1.客户端发送SYN报文请求连接；2.服务器回复SYN+ACK报文确认；3.客户端发送ACK报文，连接建立，确保双方收发能力正常。"}]} {"conversations": [{"role": "user", "content": "TCP的四次挥手是什么？"}, {"role": "assistant", "content": "TCP四次挥手是断开连接的过程：1.主动方发送FIN报文请求关闭；2.被动方回复ACK确认；3.被动方发送FIN报文；4.主动方回复ACK确认，双方断开连接。"}]} {"conversations": [{"role": "user", "content": "什么是拥塞窗口？"}, {"role": "assistant", "content": "拥塞窗口（cwnd）是TCP拥塞控制中的参数，表示发送方在未收到确认前最多可发送的数据包数量。其大小随网络拥塞状况动态调整，防止网络过载。"}]} {"conversations": [{"role": "user", "content": "UDP协议有什么特点？"}, {"role": "assistant", "content": "UDP（用户数据报协议）是无连接、不可靠的传输层协议，不保证数据有序到达，没有重传机制，开销小、速度快，适用于视频通话、直播、游戏等对实时性要求高的场景。"}]} {"conversations": [{"role": "user", "content": "HTTP协议的默认端口是多少？"}, {"role": "assistant", "content": "HTTP（超文本传输协议）的默认端口是80，用于在客户端和服务器之间传输网页等超文本数据，通常以明文形式传输。"}]} {"conversations": [{"role": "user", "content": "HTTPS和HTTP有什么区别？"}, {"role": "assistant", "content": "HTTPS是HTTP的加密版本，通过SSL/TLS协议对传输数据加密，默认端口443，安全性更高；HTTP以明文传输，易被窃听或篡改，适用于对安全性要求不高的场景。"}]} {"conversations": [{"role": "user", "content": "FTP协议的作用是什么？"}, {"role": "assistant", "content": "FTP（文件传输协议）用于在客户端和服务器之间传输文件，支持上传、下载、删除等操作，使用21号端口作为控制端口，数据传输可使用20号端口或动态端口。"}]} {"conversations": [{"role": "user", "content": "SMTP协议负责什么？"}, {"role": "assistant", "content": "SMTP（简单邮件传输协议）负责电子邮件的发送，定义了邮件服务器之间及客户端与服务器之间的邮件传输规则，默认端口为25（加密传输用465）。"}]} {"conversations": [{"role": "user", "content": "POP3协议的功能是什么？"}, {"role": "assistant", "content": "POP3（邮局协议版本3）是接收电子邮件的协议，允许客户端从邮件服务器下载邮件到本地，默认端口110（加密用995），下载后邮件可从服务器删除。"}]} {"conversations": [{"role": "user", "content": "IMAP和POP3有什么不同？"}, {"role": "assistant", "content": "IMAP（互联网消息访问协议）与POP3都用于接收邮件，但IMAP允许客户端远程管理服务器上的邮件（如创建文件夹、标记已读），邮件可保留在服务器；POP3通常将邮件下载到本地。"}]} {"conversations": [{"role": "user", "content": "什么是子网划分？"}, {"role": "assistant", "content": "子网划分是将一个大的IP网络划分为多个小的子网，通过延长子网掩码增加网络位、减少主机位，提高IP地址利用率，便于网络管理和隔离。"}]} {"conversations": [{"role": "user", "content": "什么是默认网关？"}, {"role": "assistant", "content": "默认网关是设备在访问非本网段设备时默认发送数据的网关地址，通常是连接本地网络与其他网络的路由器接口IP，若无默认网关，设备无法与外部网络通信。"}]} {"conversations": [{"role": "user", "content": "什么是DNS缓存？"}, {"role": "assistant", "content": "DNS缓存是将已解析的域名与IP地址映射关系临时存储在本地设备（如电脑、路由器）或DNS服务器中，下次访问同一域名时可直接使用缓存结果，减少解析时间。"}]} {"conversations": [{"role": "user", "content": "什么是DHCP？"}, {"role": "assistant", "content": "DHCP（动态主机配置协议）用于自动为网络中的设备分配IP地址、子网掩码、网关、DNS等网络参数，减少手动配置工作量，避免IP地址冲突。"}]} {"conversations": [{"role": "user", "content": "DHCP的工作过程是什么？"}, {"role": "assistant", "content": "DHCP工作过程分四步：1.客户端发送DHCP Discover广播请求IP；2.服务器回复DHCP Offer提供IP；3.客户端发送DHCP Request选择IP；4.服务器回复DHCP ACK确认分配。"}]} {"conversations": [{"role": "user", "content": "什么是VLAN？"}, {"role": "assistant", "content": "VLAN（虚拟局域网）是将物理局域网在逻辑上划分为多个独立子网，不同VLAN间的设备默认不能通信，可增强网络安全性、减少广播风暴，提高网络管理效率。"}]} {"conversations": [{"role": "user", "content": "什么是广播风暴？"}, {"role": "assistant", "content": "广播风暴是指网络中出现大量广播帧，占用大量带宽导致正常通信受阻的现象，通常由网络环路、故障设备等引起，可通过VLAN、STP等技术预防。"}]} {"conversations": [{"role": "user", "content": "STP协议的作用是什么？"}, {"role": "assistant", "content": "STP（生成树协议）用于在存在环路的局域网中构建无环逻辑拓扑，通过阻塞部分端口消除环路，同时在链路故障时自动激活备用端口，保证网络连通性。"}]} {"conversations": [{"role": "user", "content": "什么是IP路由？"}, {"role": "assistant", "content": "IP路由是指路由器根据路由表将IP数据包从源网络转发到目标网络的过程，路由表包含目的网络、下一跳路由器、出口端口等信息，可通过静态配置或动态路由协议生成。"}]} {"conversations": [{"role": "user", "content": "静态路由和动态路由有什么区别？"}, {"role": "assistant", "content": "静态路由由管理员手动配置，适合小型网络，不占用带宽但需手动更新；动态路由由路由协议（如RIP、OSPF）自动生成和更新，适应网络变化，适合大型网络但消耗带宽。"}]} {"conversations": [{"role": "user", "content": "RIP协议的特点是什么？"}, {"role": "assistant", "content": "RIP（路由信息协议）是距离矢量路由协议，以跳数作为度量值，最大跳数15（16为不可达），每隔30秒发送路由更新，适用于小型网络，收敛速度较慢。"}]} {"conversations": [{"role": "user", "content": "OSPF协议是什么？"}, {"role": "assistant", "content": "OSPF（开放最短路径优先）是链路状态路由协议，通过链路状态通告（LSA）交换网络拓扑信息，使用Dijkstra算法计算最短路径，适用于中大型网络，收敛速度快。"}]} {"conversations": [{"role": "user", "content": "什么是端口号？"}, {"role": "assistant", "content": "端口号是传输层协议（TCP/UDP）中标识应用程序的16位整数（0-65535），用于区分同一设备上的不同网络服务，如80端口对应HTTP，443端口对应HTTPS。"}]} {"conversations": [{"role": "user", "content": "什么是知名端口号？"}, {"role": "assistant", "content": "知名端口号是指0-1023范围内的端口号，分配给常用网络服务，如21（FTP）、22（SSH）、23（Telnet）、25（SMTP）等，由IANA（互联网号码分配机构）管理。"}]} {"conversations": [{"role": "user", "content": "什么是Socket？"}, {"role": "assistant", "content": "Socket（套接字）是网络编程中用于标识通信端点的抽象概念，由IP地址和端口号组成（如192.168.1.1:8080），是TCP/IP协议中实现进程间通信的接口。"}]} {"conversations": [{"role": "user", "content": "什么是URL？"}, {"role": "assistant", "content": "URL（统一资源定位符）用于标识互联网上资源的位置，格式通常为：协议://主机名:端口/路径?查询参数#片段，如https://www.example.com:80/path?key=value#top。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP请求方法？"}, {"role": "assistant", "content": "HTTP请求方法定义了客户端对服务器资源的操作方式，常见的有GET（获取资源）、POST（提交数据）、PUT（更新资源）、DELETE（删除资源）、HEAD（获取头部信息）等。"}]} {"conversations": [{"role": "user", "content": "HTTP状态码200表示什么？"}, {"role": "assistant", "content": "HTTP状态码200表示请求成功，服务器已正常处理并返回请求的资源，是最常见的成功状态码。"}]} {"conversations": [{"role": "user", "content": "HTTP 404错误是什么意思？"}, {"role": "assistant", "content": "HTTP 404错误表示客户端请求的资源不存在，可能是URL错误、资源已删除或移动，服务器无法找到对应的内容。"}]} {"conversations": [{"role": "user", "content": "500错误属于什么类型的状态码？"}, {"role": "assistant", "content": "500错误是服务器内部错误状态码，属于5xx系列（服务器错误），表示服务器在处理请求时发生意外错误，无法完成请求处理。"}]} {"conversations": [{"role": "user", "content": "什么是Cookie？"}, {"role": "assistant", "content": "Cookie是服务器发送给客户端的小型文本文件，存储在客户端，用于记录用户状态（如登录信息、偏好设置），每次请求同一服务器时会自动携带，实现会话跟踪。"}]} {"conversations": [{"role": "user", "content": "Session和Cookie有什么区别？"}, {"role": "assistant", "content": "Session数据存储在服务器，通过Session ID（通常存在Cookie中）关联客户端；Cookie数据存储在客户端。Session更安全但占用服务器资源，Cookie易被篡改但减轻服务器负担。"}]} {"conversations": [{"role": "user", "content": "什么是VPN？"}, {"role": "assistant", "content": "VPN（虚拟专用网络）通过公共网络（如互联网）建立加密的私有通信通道，使远程用户或分支网络安全访问企业内部网络，保护数据传输隐私和安全。"}]} {"conversations": [{"role": "user", "content": "VPN的主要类型有哪些？"}, {"role": "assistant", "content": "VPN主要类型包括：远程访问VPN（供远程用户接入）、站点到站点VPN（连接不同分支网络）、SSL VPN（基于SSL协议，通过浏览器访问）、IPsec VPN（基于IPsec协议的加密隧道）。"}]} {"conversations": [{"role": "user", "content": "什么是防火墙？"}, {"role": "assistant", "content": "防火墙是位于网络边界的安全设备，通过制定访问控制策略（如允许或拒绝特定IP、端口的通信），监控和过滤进出网络的流量，防止未授权访问和网络攻击。"}]} {"conversations": [{"role": "user", "content": "什么是入侵检测系统？"}, {"role": "assistant", "content": "入侵检测系统（IDS）通过监控网络或系统活动，分析异常行为（如攻击模式、可疑流量），及时发现并报告入侵事件，但通常不直接阻止攻击，主要起检测和告警作用。"}]} {"conversations": [{"role": "user", "content": "IDS和IPS有什么区别？"}, {"role": "assistant", "content": "IDS（入侵检测系统）仅检测并报告入侵行为；IPS（入侵防御系统）在检测到入侵时能主动阻止攻击（如丢弃恶意数据包），是IDS的升级，兼具检测和防御能力。"}]} {"conversations": [{"role": "user", "content": "什么是MAC地址欺骗？"}, {"role": "assistant", "content": "MAC地址欺骗是指通过修改设备的MAC地址，伪装成网络中其他设备的身份，以绕过基于MAC地址的访问控制、获取未授权网络访问权限或进行网络攻击的行为。"}]} {"conversations": [{"role": "user", "content": "什么是IP欺骗？"}, {"role": "assistant", "content": "IP欺骗是指攻击者伪造源IP地址发送数据包，使目标设备误认为数据包来自合法源，常用于隐藏攻击源、绕过基于IP地址的认证或发起特定网络攻击（如TCP SYN洪水攻击）。"}]} {"conversations": [{"role": "user", "content": "什么是DDoS攻击？"}, {"role": "assistant", "content": "DDoS（分布式拒绝服务）攻击是指攻击者控制大量傀儡机（僵尸网络）向目标服务器发送海量请求，耗尽其带宽、CPU等资源，导致合法用户无法访问服务。"}]} {"conversations": [{"role": "user", "content": "什么是ARP欺骗？"}, {"role": "assistant", "content": "ARP欺骗是攻击者发送伪造的ARP报文，篡改网络中设备的ARP缓存，将目标IP对应的MAC地址指向攻击者设备，使流量经过攻击者，实现窃听或断网攻击（中间人攻击的一种）。"}]} {"conversations": [{"role": "user", "content": "什么是SSL/TLS？"}, {"role": "assistant", "content": "SSL（安全套接层）和TLS（传输层安全）是用于网络通信加密的协议，建立在传输层之上，通过握手过程协商加密算法、交换密钥，对应用层数据进行加密，保障数据传输的机密性和完整性。"}]} {"conversations": [{"role": "user", "content": "什么是加密？"}, {"role": "assistant", "content": "加密是将明文数据通过加密算法和密钥转换为密文的过程，使未授权者无法理解数据内容，只有拥有对应密钥的接收者才能通过解密将密文恢复为明文，保护数据安全。"}]} {"conversations": [{"role": "user", "content": "对称加密和非对称加密有什么区别？"}, {"role": "assistant", "content": "对称加密使用同一密钥进行加密和解密，速度快但密钥分发困难；非对称加密使用公钥加密、私钥解密，密钥分发安全但加密速度慢，常结合使用（如HTTPS先用非对称加密交换对称密钥）。"}]} {"conversations": [{"role": "user", "content": "什么是数字证书？"}, {"role": "assistant", "content": "数字证书是由CA（证书颁发机构）颁发的电子文件，包含证书持有者的公钥、身份信息及CA签名，用于证明公钥的合法性，防止公钥被篡改或伪造，是HTTPS等安全通信的基础。"}]} {"conversations": [{"role": "user", "content": "什么是WLAN？"}, {"role": "assistant", "content": "WLAN（无线局域网）是利用无线通信技术（如WiFi）构建的局域网，设备通过无线信号连接，无需布线，提供灵活的网络接入，常见标准有802.11a/b/g/n/ac/ax等。"}]} {"conversations": [{"role": "user", "content": "WiFi的工作频段有哪些？"}, {"role": "assistant", "content": "WiFi主要工作在2.4GHz和5GHz频段。2.4GHz频段覆盖范围广、穿墙能力强，但信道少易干扰；5GHz频段信道多、带宽大、干扰少，但覆盖范围较小，穿墙能力弱。"}]} {"conversations": [{"role": "user", "content": "什么是AP？"}, {"role": "assistant", "content": "AP（无线接入点）是用于将无线设备接入有线网络的设备，相当于无线交换机，发射WiFi信号供手机、电脑等无线设备连接，可单个使用或多个组成无线覆盖网络。"}]} {"conversations": [{"role": "user", "content": "什么是Mesh网络？"}, {"role": "assistant", "content": "Mesh网络（ mesh网络）是一种无线拓扑结构，网络中的节点（如Mesh路由器）可互相通信，数据可通过多跳方式传输，无需依赖单一接入点，能扩大覆盖范围并提高网络可靠性。"}]} {"conversations": [{"role": "user", "content": "什么是3G、4G、5G？"}, {"role": "assistant", "content": "3G、4G、5G是移动通信技术的代际标准，分别支持高速数据传输、更高带宽和低延迟。3G支持多媒体通信，4G提升速率支持高清视频，5G则在速率、延迟、连接数上大幅提升，支持物联网等场景。"}]} {"conversations": [{"role": "user", "content": "什么是物联网？"}, {"role": "assistant", "content": "物联网（IoT）是指通过传感器、网络设备等将物理设备互联，实现数据采集、传输和交互的网络，使设备能远程监控和控制，应用于智能家居、工业监控、智慧城市等领域。"}]} {"conversations": [{"role": "user", "content": "什么是网络带宽？"}, {"role": "assistant", "content": "网络带宽是指单位时间内网络传输数据的最大能力，通常以比特/秒（bps）为单位，如100Mbps表示每秒最多传输100兆比特数据，带宽越高，数据传输速度理论上越快。"}]} {"conversations": [{"role": "user", "content": "什么是吞吐量？"}, {"role": "assistant", "content": "吞吐量是指实际传输中单位时间内成功传输的数据量，受带宽、网络拥堵、设备性能等影响，通常小于或等于带宽，是衡量网络实际传输能力的指标。"}]} {"conversations": [{"role": "user", "content": "什么是 latency？"}, {"role": "assistant", "content": "latency（延迟）是指数据从源端发送到目的端所需的时间，通常以毫秒（ms）为单位，包括传输延迟、处理延迟、排队延迟等，对实时通信（如视频通话、游戏）影响显著。"}]} {"conversations": [{"role": "user", "content": "什么是丢包率？"}, {"role": "assistant", "content": "丢包率是指在数据传输过程中丢失的数据包数量占总发送数据包数量的比例，通常以百分比表示，丢包率过高会导致数据传输不完整、卡顿，常见于网络拥堵或链路质量差的情况。"}]} {"conversations": [{"role": "user", "content": "什么是网络拓扑？"}, {"role": "assistant", "content": "网络拓扑是指网络中设备的连接方式和布局结构，常见类型有总线型、星型、环型、树型、网状型等，不同拓扑在可靠性、成本、扩展性上各有特点。"}]} {"conversations": [{"role": "user", "content": "星型拓扑的特点是什么？"}, {"role": "assistant", "content": "星型拓扑以中心设备（如交换机、集线器）为核心，所有设备都连接到中心设备，特点是易于维护（单设备故障不影响其他设备）、扩展性好，但中心设备故障会导致整个网络瘫痪。"}]} {"conversations": [{"role": "user", "content": "什么是总线型拓扑？"}, {"role": "assistant", "content": "总线型拓扑中所有设备连接到一条共享的传输介质（总线）上，结构简单、成本低，但总线故障会导致全网瘫痪，且随着设备增加，冲突和带宽竞争加剧，现已较少使用。"}]} {"conversations": [{"role": "user", "content": "什么是环型拓扑？"}, {"role": "assistant", "content": "环型拓扑中设备首尾相连形成闭合环路，数据沿单方向传输，每个设备接收并转发数据，结构简单，但单节点故障会断开环路，需使用双环等技术提高可靠性（如令牌环网）。"}]} {"conversations": [{"role": "user", "content": "什么是双绞线？"}, {"role": "assistant", "content": "双绞线是由两根绝缘铜导线绞合而成的有线传输介质，分为屏蔽双绞线（STP）和非屏蔽双绞线（UTP），常用于以太网，成本低、易于安装，常见类别有CAT5、CAT6等。"}]} {"conversations": [{"role": "user", "content": "光纤有什么特点？"}, {"role": "assistant", "content": "光纤是利用光信号传输数据的介质，由纤芯、包层和护套组成，特点是传输速率高、带宽大、抗电磁干扰、传输距离远，适用于长距离高速数据传输（如主干网络），但成本较高。"}]} {"conversations": [{"role": "user", "content": "什么是同轴电缆？"}, {"role": "assistant", "content": "同轴电缆由内导体、绝缘层、外导体屏蔽层和护套组成，曾广泛用于有线电视和早期局域网，抗干扰能力较强，但相比双绞线和光纤，在局域网中已逐渐被取代。"}]} {"conversations": [{"role": "user", "content": "什么是中继器？"}, {"role": "assistant", "content": "中继器是工作在物理层的网络设备，用于接收并放大衰减的信号，延长数据在传输介质上的传输距离，只能连接相同类型的网络，不理解帧、数据包等结构。"}]} {"conversations": [{"role": "user", "content": "集线器和交换机有什么区别？"}, {"role": "assistant", "content": "集线器工作在物理层，接收数据后向所有端口广播，易产生冲突；交换机工作在数据链路层，根据MAC地址表定向转发数据，减少冲突，提高网络效率，是集线器的升级产品。"}]} {"conversations": [{"role": "user", "content": "什么是网桥？"}, {"role": "assistant", "content": "网桥是工作在数据链路层的设备，用于连接两个局域网，根据MAC地址过滤和转发帧，能隔离网段、减少广播流量，可看作是早期的交换机（端口较少）。"}]} {"conversations": [{"role": "user", "content": "什么是网关和路由器的区别？"}, {"role": "assistant", "content": "网关是连接不同类型网络（如TCP/IP与非TCP/IP网络）的设备，负责协议转换；路由器主要连接同类型网络（如多个IP子网），负责IP数据包转发，路由器可作为网关使用，但网关不一定是路由器。"}]} {"conversations": [{"role": "user", "content": "什么是NIC？"}, {"role": "assistant", "content": "NIC（网络接口卡）即网卡，是计算机或网络设备用于连接网络的硬件组件，通常集成在主板上或作为独立插件，具有MAC地址，负责实现物理层和数据链路层的功能。"}]} {"conversations": [{"role": "user", "content": "什么是帧？"}, {"role": "assistant", "content": "帧是数据链路层的传输单位，由帧头（含源/目的MAC地址、类型等）、数据部分（网络层数据包）和帧尾（校验字段）组成，用于在局域网内传输数据，接收方通过校验字段检测帧是否损坏。"}]} {"conversations": [{"role": "user", "content": "什么是数据包？"}, {"role": "assistant", "content": "数据包是网络层的传输单位，由包头（含源/目的IP地址、协议类型等）和数据部分（传输层数据段）组成，用于在不同网络之间路由转发，过大时会被分片传输。"}]} {"conversations": [{"role": "user", "content": "什么是数据段？"}, {"role": "assistant", "content": "数据段是传输层的传输单位，在TCP中称为TCP段，包含源/目的端口号、序列号、确认号等控制信息和应用层数据，TCP通过数据段实现可靠传输和流量控制。"}]} {"conversations": [{"role": "user", "content": "什么是比特流？"}, {"role": "assistant", "content": "比特流是物理层的传输单位，由0和1组成的二进制数据流，是数据在物理介质上的最终传输形式，物理层负责将数据链路层的帧转换为比特流并通过传输介质发送。"}]} {"conversations": [{"role": "user", "content": "什么是封装和解封装？"}, {"role": "assistant", "content": "封装是数据从应用层到物理层传输时，每层为数据添加本层头部（尾部）的过程；解封装是接收方从物理层到应用层处理时，每层剥离本层头部（尾部）的过程，实现数据在不同层的传递。"}]} {"conversations": [{"role": "user", "content": "什么是多路复用？"}, {"role": "assistant", "content": "多路复用是指在一条传输介质上同时传输多个信号的技术，如时分多路复用（TDM）、频分多路复用（FDM）、波分多路复用（WDM）等，提高传输介质的利用率。"}]} {"conversations": [{"role": "user", "content": "什么是TDM？"}, {"role": "assistant", "content": "TDM（时分多路复用）将传输时间划分为多个时隙，每个信号分配固定时隙轮流占用传输介质，在时间上分离不同信号，如电话系统和某些数字传输中常用。"}]} {"conversations": [{"role": "user", "content": "什么是FDM？"}, {"role": "assistant", "content": "FDM（频分多路复用）将传输介质的带宽划分为多个子信道（不同频率范围），每个信号占用一个子信道，在频率上分离不同信号，如有线电视、FM广播等采用FDM技术。"}]} {"conversations": [{"role": "user", "content": "什么是网络协议？"}, {"role": "assistant", "content": "网络协议是计算机网络中设备通信时遵循的规则集合，定义了数据格式、传输时序、错误处理等规范，使不同厂商、不同类型的设备能互相通信，如TCP/IP、HTTP等都是网络协议。"}]} {"conversations": [{"role": "user", "content": "协议三要素是什么？"}, {"role": "assistant", "content": "协议三要素是语法、语义和时序。语法定义数据的格式和结构；语义定义数据的含义和操作（如字段代表的意义）；时序定义事件的先后顺序和传输速率匹配。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的滑动窗口？"}, {"role": "assistant", "content": "TCP滑动窗口是实现流量控制和可靠传输的机制，窗口大小表示接收方可接收的未确认数据量，发送方根据窗口大小控制发送速率，窗口随接收方处理能力动态调整，提高传输效率。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的拥塞避免算法？"}, {"role": "assistant", "content": "TCP拥塞避免算法用于防止网络因数据过多而拥塞，当拥塞窗口达到慢启动阈值后，每轮传输窗口大小加1（线性增长），而非指数增长，缓慢增加发送速率，避免网络过载。"}]} {"conversations": [{"role": "user", "content": "什么是快速重传？"}, {"role": "assistant", "content": "快速重传是TCP的机制，当接收方收到失序数据包时，连续发送3个重复ACK给发送方，发送方无需等待超时，立即重传被认为丢失的数据包，减少重传延迟，提高效率。"}]} {"conversations": [{"role": "user", "content": "什么是快速恢复？"}, {"role": "assistant", "content": "快速恢复是TCP在收到重复ACK后进入的状态，将慢启动阈值设为当前拥塞窗口的一半，拥塞窗口设为新阈值，直接进入拥塞避免阶段，而非重置为1，减少传输速率波动。"}]} {"conversations": [{"role": "user", "content": "什么是IPv6？"}, {"role": "assistant", "content": "IPv6是下一代互联网协议，采用128位地址格式，解决IPv4地址耗尽问题，支持更多设备接入，同时内置安全性（如IPsec）、简化头部结构、支持自动配置，逐步取代IPv4。"}]} {"conversations": [{"role": "user", "content": "IPv6和IPv4的主要区别是什么？"}, {"role": "assistant", "content": "IPv6用128位地址（IPv4是32位），地址空间极大；无广播地址，用组播和任播；头部结构简化，固定字段少；内置IPsec支持；支持自动配置，无需DHCP；取消了子网掩码，用前缀长度表示网络位。"}]} {"conversations": [{"role": "user", "content": "什么是任播？"}, {"role": "assistant", "content": "任播是IPv6中的一种地址类型，多个设备可分配相同的任播地址，数据包会被发送到距离源端最近（根据路由协议判断）的一个设备，适用于CDN、DNS等服务，提高访问效率。"}]} {"conversations": [{"role": "user", "content": "什么是组播？"}, {"role": "assistant", "content": "组播是一种数据传输方式，发送方将数据发送到组播地址，所有加入该组的接收方都能收到数据，相比广播更高效（仅目标组接收），适用于视频会议、直播等一对多通信场景。"}]} {"conversations": [{"role": "user", "content": "什么是单播？"}, {"role": "assistant", "content": "单播是一对一的数据传输方式，发送方将数据发送到唯一的目标地址，只有目标设备接收，是网络中最常见的传输方式，如浏览网页、点对点文件传输等。"}]} {"conversations": [{"role": "user", "content": "什么是广播？"}, {"role": "assistant", "content": "广播是一对所有的数据传输方式，发送方将数据发送到广播地址，同一网络中的所有设备都会接收该数据，适用于ARP请求等场景，但过多广播会导致网络拥塞。"}]} {"conversations": [{"role": "user", "content": "什么是CIDR？"}, {"role": "assistant", "content": "CIDR（无类别域间路由）是一种IP地址分配和路由聚合技术，用前缀长度表示网络位（如192.168.1.0/24），打破传统IP地址分类，提高IP地址利用率，简化路由表。"}]} {"conversations": [{"role": "user", "content": "什么是路由聚合？"}, {"role": "assistant", "content": "路由聚合（ route summarization）是将多个连续的子网路由合并为一个更笼统的路由条目，减少路由表中的条目数量，降低路由器负担，提高路由效率，是CIDR的核心功能。"}]} {"conversations": [{"role": "user", "content": "什么是自治系统？"}, {"role": "assistant", "content": "自治系统（AS）是由单一机构管理、使用统一路由策略的网络集合，每个AS有唯一的AS编号，AS之间通过边界网关协议（BGP）交换路由信息，是互联网的基本组成单元。"}]} {"conversations": [{"role": "user", "content": "BGP协议的作用是什么？"}, {"role": "assistant", "content": "BGP（边界网关协议）是自治系统之间的路由协议，用于在不同AS之间交换路由信息，根据路径、策略等选择最佳路由，是互联网核心路由协议，确保全球网络的互联互通。"}]} {"conversations": [{"role": "user", "content": "什么是NAPT？"}, {"role": "assistant", "content": "NAPT（网络地址端口转换）是NAT的一种形式，不仅转换IP地址，还转换端口号，使多个私有IP地址通过同一个公网IP的不同端口访问互联网，最大限度节省公网IP地址。"}]} {"conversations": [{"role": "user", "content": "什么是动态DNS？"}, {"role": "assistant", "content": "动态DNS（DDNS）是当设备的公网IP地址动态变化时，自动更新域名与IP地址映射关系的服务，使客户端可通过固定域名访问具有动态IP的设备（如家庭网络中的服务器）。"}]} {"conversations": [{"role": "user", "content": "什么是TTL值在DNS中的作用？"}, {"role": "assistant", "content": "DNS中的TTL（生存时间）表示域名解析记录在DNS缓存中的保存时间，单位为秒。TTL值越小，解析记录更新越及时；值越大，缓存时间越长，可减少DNS查询次数，降低服务器负担。"}]} {"conversations": [{"role": "user", "content": "什么是递归查询和迭代查询？"}, {"role": "assistant", "content": "递归查询是DNS客户端向本地DNS服务器请求解析，服务器若不知道结果，会代替客户端向其他服务器查询，最终返回结果；迭代查询是服务器仅返回下一跳服务器地址，由客户端自行继续查询。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP长连接？"}, {"role": "assistant", "content": "HTTP长连接（持久连接）是指客户端与服务器建立一次TCP连接后，可多次传输HTTP请求和响应，无需每次请求都建立新连接，减少连接建立开销，提高效率，由HTTP/1.1默认支持。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP/2？"}, {"role": "assistant", "content": "HTTP/2是HTTP协议的升级版，支持多路复用（同一连接并发传输多个请求）、二进制帧传输、服务器推送（主动发送资源）、头部压缩等特性，相比HTTP/1.1大幅提升网页加载速度。"}]} {"conversations": [{"role": "user", "content": "什么是WebSocket？"}, {"role": "assistant", "content": "WebSocket是一种在单个TCP连接上实现全双工通信的协议，允许服务器主动向客户端推送数据，无需客户端频繁轮询，适用于实时通信场景（如聊天应用、实时数据更新）。"}]} {"conversations": [{"role": "user", "content": "什么是FTP的主动模式和被动模式？"}, {"role": "assistant", "content": "FTP主动模式：服务器主动连接客户端的数据端口；被动模式：客户端主动连接服务器的临时数据端口。被动模式更适合客户端在防火墙后的场景，避免连接被防火墙阻止。"}]} {"conversations": [{"role": "user", "content": "什么是SFTP？"}, {"role": "assistant", "content": "SFTP（SSH文件传输协议）是基于SSH协议的安全文件传输协议，通过加密通道传输文件，提供身份认证和数据加密，比传统FTP更安全，常用于需要保密的文件传输场景。"}]} {"conversations": [{"role": "user", "content": "什么是Telnet和SSH的区别？"}, {"role": "assistant", "content": "Telnet是明文传输的远程登录协议，数据易被窃听；SSH（安全外壳协议）对传输数据加密，提供安全的远程登录和命令执行，还支持端口转发、文件传输（SFTP），已取代Telnet。"}]} {"conversations": [{"role": "user", "content": "什么是网络地址转换的优缺点？"}, {"role": "assistant", "content": "NAT优点：节省公网IP地址、隐藏内部网络结构提高安全性；缺点：破坏端到端通信（如P2P应用困难）、增加网络延迟、不支持某些依赖原始IP的协议。"}]} {"conversations": [{"role": "user", "content": "什么是VLAN的划分方式？"}, {"role": "assistant", "content": "VLAN划分方式包括：基于端口（按交换机端口分组）、基于MAC地址（按设备MAC地址分组）、基于IP地址（按IP子网分组）、基于协议（按网络层协议分组），最常用的是基于端口划分。"}]} {"conversations": [{"role": "user", "content": "什么是链路聚合？"}, {"role": "assistant", "content": "链路聚合（如LACP）将多个物理链路捆绑为一个逻辑链路，增加带宽（叠加各链路带宽），并提供冗余（单链路故障不影响通信），提高网络连接的吞吐量和可靠性。"}]} {"conversations": [{"role": "user", "content": "什么是MSTP？"}, {"role": "assistant", "content": "MSTP（多生成树协议）是STP的扩展，允许在一个交换网络中创建多个生成树实例，每个实例对应不同VLAN，实现不同VLAN的流量沿不同路径传输，提高链路利用率。"}]} {"conversations": [{"role": "user", "content": "什么是负载均衡？"}, {"role": "assistant", "content": "负载均衡是将网络流量分配到多个服务器的技术，避免单台服务器过载，提高服务可用性和响应速度，常见方式有硬件负载均衡器、软件负载均衡（如Nginx）、DNS负载均衡等。"}]} {"conversations": [{"role": "user", "content": "什么是CDN？"}, {"role": "assistant", "content": "CDN（内容分发网络）通过在全球部署边缘节点，将静态资源（如图片、视频）缓存到离用户最近的节点，用户访问时从就近节点获取资源，减少延迟，减轻源服务器负担。"}]} {"conversations": [{"role": "user", "content": "什么是网络虚拟化？"}, {"role": "assistant", "content": "网络虚拟化是将物理网络资源（如交换机、路由器）抽象为虚拟网络的技术，允许多个虚拟网络共享物理基础设施，提高资源利用率，简化网络管理，如VMware的虚拟交换机技术。"}]} {"conversations": [{"role": "user", "content": "什么是SDN？"}, {"role": "assistant", "content": "SDN（软件定义网络）将网络的控制平面与数据转发平面分离，控制平面通过软件集中管理，动态配置网络，实现网络的灵活编程和快速部署，适应云计算等动态网络需求。"}]} {"conversations": [{"role": "user", "content": "什么是云计算？"}, {"role": "assistant", "content": "云计算通过网络提供可弹性扩展的计算资源（如服务器、存储、数据库），用户按需付费，无需自建基础设施，常见服务模式有IaaS（基础设施即服务）、PaaS（平台即服务）、SaaS（软件即服务）。"}]} {"conversations": [{"role": "user", "content": "什么是网络安全？"}, {"role": "assistant", "content": "网络安全是保护网络系统（硬件、软件、数据）免受未授权访问、攻击、破坏或篡改的措施和技术，目标包括保密性（数据不泄露）、完整性（数据不被篡改）、可用性（服务不中断）。"}]} {"conversations": [{"role": "user", "content": "什么是对称密钥加密？"}, {"role": "assistant", "content": "对称密钥加密（私钥加密）中，加密和解密使用相同的密钥，如AES、DES算法，特点是加密速度快，适合大量数据加密，但密钥分发和管理困难，需确保密钥安全传递。"}]} {"conversations": [{"role": "user", "content": "什么是公钥加密？"}, {"role": "assistant", "content": "公钥加密（非对称加密）使用一对密钥：公钥（公开）用于加密，私钥（保密）用于解密，如RSA算法。无需传递私钥，解决密钥分发问题，但加密速度慢，常用于加密对称密钥或数字签名。"}]} {"conversations": [{"role": "user", "content": "什么是数字签名？"}, {"role": "assistant", "content": "数字签名是发送方用私钥对数据哈希值加密生成的信息，接收方用发送方公钥解密并验证。可确保数据完整性（未被篡改）和发送方身份真实性（不可否认），是电子文档防伪的重要手段。"}]} {"conversations": [{"role": "user", "content": "什么是CA？"}, {"role": "assistant", "content": "CA（证书颁发机构）是负责颁发和管理数字证书的可信第三方机构，验证证书申请者的身份，为其颁发包含公钥和身份信息的数字证书，确保公钥与持有者身份的绑定，如VeriSign、Let's Encrypt。"}]} {"conversations": [{"role": "user", "content": "什么是防火墙的包过滤技术？"}, {"role": "assistant", "content": "包过滤是防火墙的基础技术，根据数据包的源IP、目的IP、端口号、协议类型等信息，按照预设规则允许或拒绝数据包通过，工作在网络层和传输层，速度快但功能有限。"}]} {"conversations": [{"role": "user", "content": "什么是应用层网关？"}, {"role": "assistant", "content": "应用层网关（代理服务器）工作在应用层，代表客户端与服务器通信，深入理解特定应用协议（如HTTP、FTP），能进行更细致的访问控制和内容过滤，安全性高但速度较慢。"}]} {"conversations": [{"role": "user", "content": "什么是病毒和蠕虫的区别？"}, {"role": "assistant", "content": "病毒需要依附于其他文件（如可执行文件）传播，依赖用户操作（如打开文件）激活；蠕虫是独立程序，可自行利用网络漏洞传播，无需用户干预，传播速度更快，如冲击波蠕虫。"}]} {"conversations": [{"role": "user", "content": "什么是木马？"}, {"role": "assistant", "content": "木马是伪装成合法程序的恶意软件，一旦安装，攻击者可远程控制被感染设备（如窃取数据、控制摄像头），但不自我复制，通常通过诱骗用户下载安装传播，如远程控制木马。"}]} {"conversations": [{"role": "user", "content": "什么是钓鱼攻击？"}, {"role": "assistant", "content": "钓鱼攻击是攻击者伪装成合法机构（如银行、社交媒体），通过邮件、网站等诱骗用户泄露敏感信息（如账号、密码、银行卡号），常见形式有伪造登录页面、发送欺诈邮件。"}]} {"conversations": [{"role": "user", "content": "什么是SQL注入攻击？"}, {"role": "assistant", "content": "SQL注入是攻击者在Web应用的输入字段中插入恶意SQL语句，若应用未对输入过滤，服务器会执行该语句，可能导致数据库信息泄露、数据篡改甚至服务器被控制。"}]} {"conversations": [{"role": "user", "content": "什么是XSS攻击？"}, {"role": "assistant", "content": "XSS（跨站脚本）攻击是攻击者在网页中注入恶意脚本（如JavaScript），当其他用户访问该页面时，脚本在用户浏览器中执行，可窃取Cookie、会话劫持或篡改页面内容。"}]} {"conversations": [{"role": "user", "content": "什么是DoS攻击？"}, {"role": "assistant", "content": "DoS（拒绝服务）攻击是攻击者通过单一源向目标发送大量请求，耗尽其资源（如带宽、CPU），使合法用户无法访问服务，与DDoS的区别是DoS来自单一源，DDoS来自分布式源。"}]} {"conversations": [{"role": "user", "content": "什么是VPN的隧道技术？"}, {"role": "assistant", "content": "VPN隧道技术是将私有网络数据封装在公网数据包中传输的方式，通过加密和封装，使私有数据在公网上安全传输，如同在公网中开辟了一条“专用隧道”，常见隧道协议有IPsec、L2TP。"}]} {"conversations": [{"role": "user", "content": "什么是WPA2和WPA3？"}, {"role": "assistant", "content": "WPA2和WPA3是WiFi的安全认证协议，用于加密无线通信。WPA3是WPA2的升级版，提供更强的加密算法（如SAE取代PSK）、更好的防暴力破解能力，增强公共WiFi和家用网络的安全性。"}]} {"conversations": [{"role": "user", "content": "什么是WEP？"}, {"role": "assistant", "content": "WEP（有线等效保密）是早期WiFi安全协议，采用RC4加密算法，但存在严重安全缺陷（如密钥管理弱、易被破解），已被WPA、WPA2取代，不再建议使用。"}]} {"conversations": [{"role": "user", "content": "什么是802.11标准？"}, {"role": "assistant", "content": "802.11是IEEE制定的无线局域网标准系列，包括802.11a（5GHz，54Mbps）、802.11b（2.4GHz，11Mbps）、802.11g（2.4GHz，54Mbps）、802.11n（双频段，600Mbps）、802.11ac/ax等，定义了WiFi的技术规范。"}]} {"conversations": [{"role": "user", "content": "什么是MIMO技术？"}, {"role": "assistant", "content": "MIMO（多输入多输出）是无线通信技术，通过多个发射天线和接收天线同时传输数据，利用空间分集提高传输速率和可靠性，是802.11n及以后WiFi标准和4G/5G的核心技术之一。"}]} {"conversations": [{"role": "user", "content": "什么是蜂窝网络？"}, {"role": "assistant", "content": "蜂窝网络是移动通信网络的结构，将地理区域划分为多个六边形“蜂窝”，每个蜂窝由基站覆盖，频率可在不同蜂窝复用，提高频率利用率，支持大量用户同时通信，如手机通信网络。"}]} {"conversations": [{"role": "user", "content": "什么是基站？"}, {"role": "assistant", "content": "基站是移动通信网络中的设备，负责与手机等移动设备进行无线通信，提供网络接入、信号转发等功能，由发射塔、天线、机房设备组成，是连接移动设备与核心网络的桥梁。"}]} {"conversations": [{"role": "user", "content": "什么是核心网？"}, {"role": "assistant", "content": "核心网是移动通信网络的核心部分，负责处理语音和数据业务的交换、路由、计费等功能，连接基站与外部网络（如互联网、公共电话网），是移动网络的“大脑”。"}]} {"conversations": [{"role": "user", "content": "什么是边缘计算？"}, {"role": "assistant", "content": "边缘计算将数据处理和存储放在网络边缘（如基站、边缘服务器），而非远程云端，减少数据传输距离，降低延迟，提高响应速度，适合物联网、自动驾驶等对实时性要求高的场景。"}]} {"conversations": [{"role": "user", "content": "什么是网络切片？"}, {"role": "assistant", "content": "网络切片是5G的核心技术，在单一物理网络中划分多个逻辑上独立的虚拟网络，每个切片按需定制带宽、延迟、安全性等特性，满足不同场景（如自动驾驶、物联网）的差异化需求。"}]} {"conversations": [{"role": "user", "content": "什么是QoS？"}, {"role": "assistant", "content": "QoS（服务质量）是网络为特定业务提供差异化服务的能力，通过优先级划分、带宽保障、延迟控制等机制，确保关键业务（如语音、视频）的传输质量，在网络拥堵时优先处理。"}]} {"conversations": [{"role": "user", "content": "什么是流量整形？"}, {"role": "assistant", "content": "流量整形是QoS技术之一，通过调整数据传输速率，使流量符合预定的速率特性（如平滑突发流量），避免网络拥塞，保证数据传输的稳定性，通常在发送端实施。"}]} {"conversations": [{"role": "user", "content": "什么是流量监管？"}, {"role": "assistant", "content": "流量监管（CAR）是监控并限制流量速率的QoS技术，当流量超过规定速率时，丢弃超额部分或标记为低优先级，防止单个用户或业务占用过多带宽，通常在网络入口处实施。"}]} {"conversations": [{"role": "user", "content": "什么是差分服务？"}, {"role": "assistant", "content": "差分服务（DiffServ）是一种QoS架构，通过在IP包头的DS字段标记流量优先级，网络设备根据优先级提供不同的转发处理（如带宽保障、延迟控制），实现对不同类型流量的差异化服务。"}]} {"conversations": [{"role": "user", "content": "什么是电路交换和分组交换？"}, {"role": "assistant", "content": "电路交换在通信前建立专用物理链路，全程占用链路（如电话网），延迟低但利用率低；分组交换将数据拆分为分组，通过共享链路传输，动态分配资源，利用率高（如互联网），但有延迟和抖动。"}]} {"conversations": [{"role": "user", "content": "什么是数据报和虚电路？"}, {"role": "assistant", "content": "数据报是分组交换的一种方式，每个分组独立路由，路径可能不同，无需建立连接（如IP）；虚电路是先建立逻辑连接，所有分组沿同一路径传输，有连接建立和释放过程（如X.25、ATM）。"}]} {"conversations": [{"role": "user", "content": "什么是ATM？"}, {"role": "assistant", "content": "ATM（异步传输模式）是一种高速分组交换技术，使用固定长度的信元（53字节）传输数据，结合电路交换和分组交换的优点，支持语音、数据、视频等多种业务，但因成本高已被IP技术取代。"}]} {"conversations": [{"role": "user", "content": "什么是帧中继？"}, {"role": "assistant", "content": "帧中继是一种面向连接的分组交换技术，基于X.25简化而来，减少差错控制和流量控制，提高传输效率，适用于中高速数据传输，曾用于企业网互联，现已被IP VPN等技术取代。"}]} {"conversations": [{"role": "user", "content": "什么是网络管理？"}, {"role": "assistant", "content": "网络管理是对网络设备、服务和流量进行监控、配置、维护和优化的过程，目标是确保网络稳定运行、提高性能、及时排查故障，常用协议有SNMP（简单网络管理协议）。"}]} {"conversations": [{"role": "user", "content": "SNMP协议的作用是什么？"}, {"role": "assistant", "content": "SNMP（简单网络管理协议）用于网络设备的管理和监控，管理站通过SNMP向代理（网络设备）发送请求，获取设备状态信息（如CPU利用率、流量）或配置设备，是最常用的网络管理协议。"}]} {"conversations": [{"role": "user", "content": "什么是MIB？"}, {"role": "assistant", "content": "MIB（管理信息库）是SNMP中定义的网络设备状态信息的集合，采用树形结构组织，每个节点代表一个可管理对象（如端口流量、错误计数），管理站通过访问MIB获取设备信息。"}]} {"conversations": [{"role": "user", "content": "什么是网络监控？"}, {"role": "assistant", "content": "网络监控是对网络设备、链路、服务的运行状态进行实时监测和记录的过程，包括流量分析、故障检测、性能指标（如带宽、延迟）监控，及时发现并预警网络问题，保障网络正常运行。"}]} {"conversations": [{"role": "user", "content": "什么是网络嗅探？"}, {"role": "assistant", "content": "网络嗅探是通过捕获网络中的数据包并分析其内容的技术，合法用途包括网络故障诊断、流量分析；非法用途则是窃听敏感信息（如密码），可通过加密（如HTTPS）防止被嗅探。"}]} {"conversations": [{"role": "user", "content": "什么是端口扫描？"}, {"role": "assistant", "content": "端口扫描是向目标设备的多个端口发送请求，根据回应判断端口是否开放及运行的服务，常用于网络安全检测（发现开放的危险端口），也是攻击者寻找目标漏洞的常用手段。"}]} {"conversations": [{"role": "user", "content": "什么是网络地址端口转换？"}, {"role": "assistant", "content": "网络地址端口转换（NAPT）是NAT的一种，将多个内部私有IP地址及其端口转换为一个或多个公网IP地址及其端口，通过端口区分不同内部主机，实现多设备共享公网IP访问互联网。"}]} {"conversations": [{"role": "user", "content": "什么是静态NAT？"}, {"role": "assistant", "content": "静态NAT是将内部私有IP地址与公网IP地址进行一对一的固定映射，转换关系由管理员手动配置，适用于需要外部网络通过固定公网IP访问的内部服务器（如Web服务器）。"}]} {"conversations": [{"role": "user", "content": "什么是动态NAT？"}, {"role": "assistant", "content": "动态NAT是从公网IP地址池中动态分配一个公网IP地址给内部私有IP地址，会话结束后释放该公网IP供其他内部地址使用，节省公网IP地址，但外部无法主动访问内部设备。"}]} {"conversations": [{"role": "user", "content": "什么是IP分片？"}, {"role": "assistant", "content": "IP分片是当IP数据包大小超过链路的最大传输单元（MTU）时，将数据包拆分为多个较小分片的过程，每个分片包含分片信息，接收方根据这些信息将分片重组为原始数据包。"}]} {"conversations": [{"role": "user", "content": "什么是MTU？"}, {"role": "assistant", "content": "MTU（最大传输单元）是数据链路层规定的帧所能承载的最大数据部分大小（以字节为单位），不同链路类型MTU不同（如以太网MTU为1500字节），超过MTU的数据包会被分片传输。"}]} {"conversations": [{"role": "user", "content": "什么是路径MTU发现？"}, {"role": "assistant", "content": "路径MTU发现是TCP/IP协议中确定源端到目的端路径上最小MTU的机制，通过发送带“不分片”标志的数据包，根据ICMP差错报文获知路径MTU，避免不必要的IP分片，提高传输效率。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的保活计时器？"}, {"role": "assistant", "content": "TCP保活计时器用于检测长时间空闲的连接是否仍然有效，当连接空闲时间超过设定值，发送方发送保活探测报文，若未收到回应则认为连接已断开并关闭，释放资源。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的超时重传计时器？"}, {"role": "assistant", "content": "TCP超时重传计时器在发送数据包后启动，若在计时器到期前未收到确认，认为数据包丢失，触发重传机制，计时器值通常根据往返时间（RTT）动态调整，避免不必要的重传。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的拥塞窗口和接收窗口？"}, {"role": "assistant", "content": "拥塞窗口（cwnd）是TCP拥塞控制的参数，限制发送方因网络拥塞而能发送的最大数据量；接收窗口（rwnd）是接收方告知发送方能接收的最大数据量，用于流量控制，发送方实际发送窗口取两者最小值。"}]} {"conversations": [{"role": "user", "content": "什么是TCP的序列号和确认号？"}, {"role": "assistant", "content": "序列号是TCP段中标识数据字节位置的编号，确保数据有序；确认号是期望收到的下一字节序列号，用于告知发送方已成功接收的数据，实现可靠传输和流量控制。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP的请求头和响应头？"}, {"role": "assistant", "content": "HTTP请求头是客户端发送给服务器的附加信息（如User-Agent、Accept），描述请求特征；响应头是服务器返回给客户端的附加信息（如Content-Type、Cache-Control），描述响应内容和处理方式。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP的实体？"}, {"role": "assistant", "content": "HTTP实体是HTTP请求或响应中传输的实际数据内容，包括实体头部（如Content-Length、Content-Encoding）和实体主体（如HTML文档、图片数据），实体主体的格式和编码由实体头部描述。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP的缓存机制？"}, {"role": "assistant", "content": "HTTP缓存机制允许客户端或中间代理缓存服务器响应，再次请求时直接使用缓存内容，减少服务器负载和传输量，通过Cache-Control、ETag、Last-Modified等头部控制缓存策略。"}]} {"conversations": [{"role": "user", "content": "什么是条件请求？"}, {"role": "assistant", "content": "HTTP条件请求是客户端在请求中携带条件头部（如If-Modified-Since、If-None-Match），服务器仅在条件满足时返回完整响应，否则返回304 Not Modified，减少不必要的数据传输，优化性能。"}]} {"conversations": [{"role": "user", "content": "什么是跨域资源共享？"}, {"role": "assistant", "content": "跨域资源共享（CORS）是浏览器的安全机制，允许Web页面从不同域名请求资源，服务器通过设置Access-Control-Allow-Origin等响应头，指定允许跨域访问的来源，解决同源策略限制。"}]} {"conversations": [{"role": "user", "content": "什么是同源策略？"}, {"role": "assistant", "content": "同源策略是浏览器的安全限制，规定脚本只能访问与其来源（协议、域名、端口）相同的资源，防止不同域名的恶意脚本窃取数据，是Web安全的基础，但可通过CORS等机制放宽限制。"}]} {"conversations": [{"role": "user", "content": "什么是WebSocket握手？"}, {"role": "assistant", "content": "WebSocket握手是客户端与服务器建立WebSocket连接的过程，客户端发送带Upgrade头部的HTTP请求，服务器返回101 Switching Protocols响应，完成从HTTP到WebSocket协议的转换，建立持久连接。"}]} {"conversations": [{"role": "user", "content": "什么是FTP的控制连接和数据连接？"}, {"role": "assistant", "content": "FTP使用两个连接：控制连接（端口21）用于传输命令和响应，整个会话期间保持连接；数据连接用于传输文件数据，每次传输数据时建立，传输结束后关闭，分主动和被动模式建立。"}]} {"conversations": [{"role": "user", "content": "什么是DNS的根服务器？"}, {"role": "assistant", "content": "DNS根服务器是DNS域名解析体系的最高层级，存储顶级域名服务器（如.com、.org）的地址，全球共13组根服务器，负责将根域查询引导至相应的顶级域名服务器，是域名解析的起点。"}]} {"conversations": [{"role": "user", "content": "什么是顶级域名？"}, {"role": "assistant", "content": "顶级域名是域名的最高层级，位于域名最后部分，如.com（商业）、.org（组织）、.net（网络）、.cn（中国国家顶级域名）等，由ICANN或国家域名管理机构管理，负责解析二级域名。"}]} {"conversations": [{"role": "user", "content": "什么是二级域名？"}, {"role": "assistant", "content": "二级域名是顶级域名前的部分，如example.com中的example，由顶级域名注册商分配给用户，用户可自主管理二级域名下的子域名（如www.example.com），用于区分不同的服务或部门。"}]} {"conversations": [{"role": "user", "content": "什么是DHCP租约？"}, {"role": "assistant", "content": "DHCP租约是DHCP服务器分配给客户端的IP地址的有效期限，租约到期前客户端需向服务器申请续租，若未续租则IP地址被收回，避免IP地址长期被闲置设备占用，提高利用率。"}]} {"conversations": [{"role": "user", "content": "什么是DHCP中继？"}, {"role": "assistant", "content": "DHCP中继（DHCP Relay Agent）是在DHCP客户端和服务器不在同一子网时，转发DHCP报文的设备（如路由器），使不同子网的客户端能获取同一DHCP服务器分配的IP地址，简化网络配置。"}]} {"conversations": [{"role": "user", "content": "什么是VLAN间路由？"}, {"role": "assistant", "content": "VLAN间路由是实现不同VLAN之间设备通信的技术，可通过路由器（每个VLAN对应一个物理接口）或三层交换机（支持路由功能，通过SVI虚拟接口）实现，使隔离的VLAN能按需通信。"}]} {"conversations": [{"role": "user", "content": "什么是三层交换机？"}, {"role": "assistant", "content": "三层交换机是同时具备二层交换和三层路由功能的设备，能在VLAN间进行路由转发，比传统的“二层交换机+路由器”架构速度更快，适用于需要VLAN间通信的中型网络。"}]} {"conversations": [{"role": "user", "content": "什么是链路状态路由协议？"}, {"role": "assistant", "content": "链路状态路由协议（如OSPF、IS-IS）中，每个路由器向网络中所有其他路由器发送链路状态信息（如链路带宽、邻居），各路由器根据这些信息构建全网拓扑图，使用最短路径算法计算路由。"}]} {"conversations": [{"role": "user", "content": "什么是距离矢量路由协议？"}, {"role": "assistant", "content": "距离矢量路由协议（如RIP、IGRP）中，路由器仅向邻居发送自己的路由表（包含目的网络和距离），通过“传闻”学习网络拓扑，定期更新路由信息，实现简单但收敛速度慢，易产生路由环路。"}]} {"conversations": [{"role": "user", "content": "什么是路由环路？"}, {"role": "assistant", "content": "路由环路是数据包在网络中循环转发无法到达目的地的现象，通常由距离矢量路由协议的慢收敛特性导致，可通过定义最大跳数、水平分割、毒性反转等技术防止或消除。"}]} {"conversations": [{"role": "user", "content": "什么是水平分割？"}, {"role": "assistant", "content": "水平分割是防止路由环路的技术，规定路由器不从某个接口接收的路由信息，也不通过该接口发送出去，避免路由信息在相邻路由器之间来回传递，减少路由环路的可能性。"}]} {"conversations": [{"role": "user", "content": "什么是毒性反转？"}, {"role": "assistant", "content": "毒性反转是水平分割的变种，路由器从某个接口接收路由信息后，将该路由标记为不可达（毒性），再通过同一接口发回给邻居，明确告知该路由不可用，加速路由收敛，防止环路。"}]} {"conversations": [{"role": "user", "content": "什么是路由度量？"}, {"role": "assistant", "content": "路由度量是路由协议用于判断路径优劣的参数，不同协议度量不同，如RIP用跳数，OSPF用链路带宽（成本），EIGRP用带宽、延迟、负载等综合值，度量值越小，路径被认为越优。"}]} {"conversations": [{"role": "user", "content": "什么是管理距离？"}, {"role": "assistant", "content": "管理距离（AD）是路由器用于选择不同路由协议发现的同一条路由的参数，AD值越小，路由协议优先级越高。例如，直连路由AD为0，静态路由AD为1，RIP的AD为120。"}]} {"conversations": [{"role": "user", "content": "什么是浮动静态路由？"}, {"role": "assistant", "content": "浮动静态路由是配置了较高管理距离的静态路由，当主路由（如动态路由协议发现的路由）失效时，浮动静态路由因管理距离较低而被启用，作为备用路由，提高网络冗余性。"}]} {"conversations": [{"role": "user", "content": "什么是黑洞路由？"}, {"role": "assistant", "content": "黑洞路由是将特定目的网络的路由指向空接口（null0）的路由，匹配该路由的数据包会被直接丢弃，用于过滤不需要的流量（如恶意攻击流量），防止其在网络中传播。"}]} {"conversations": [{"role": "user", "content": "什么是ICMPv6？"}, {"role": "assistant", "content": "ICMPv6是IPv6中的控制消息协议，除了包含ICMPv4的功能（如差错报告、ping），还增加了邻居发现（替代ARP）、自动配置、MTU发现等功能，是IPv6协议栈的重要组成部分。"}]} {"conversations": [{"role": "user", "content": "什么是邻居发现协议？"}, {"role": "assistant", "content": "邻居发现协议（NDP）是IPv6中替代ARP的协议，通过ICMPv6消息实现IPv6地址与MAC地址的映射、邻居可达性检测、前缀发现等功能，比ARP更高效、功能更丰富。"}]} {"conversations": [{"role": "user", "content": "什么是IPv6的无状态自动配置？"}, {"role": "assistant", "content": "IPv6无状态自动配置是设备通过NDP从路由器获取网络前缀，结合自身MAC地址生成IPv6地址，无需DHCP服务器，简化网络配置，使设备接入网络后能自动获得IP地址。"}]} {"conversations": [{"role": "user", "content": "什么是双栈技术？"}, {"role": "assistant", "content": "双栈技术是设备同时支持IPv4和IPv6协议栈，能处理两种协议的数据包，实现IPv4和IPv6网络的共存和过渡，是IPv6过渡阶段的主要技术之一，允许逐步迁移到IPv6。"}]} {"conversations": [{"role": "user", "content": "什么是隧道技术在IPv6过渡中的应用？"}, {"role": "assistant", "content": "IPv6过渡中的隧道技术将IPv6数据包封装在IPv4数据包中传输，使IPv6网络能通过IPv4基础设施通信，常见隧道类型有6to4、ISATAP、GRE隧道等，解决IPv4和IPv6网络互通问题。"}]} {"conversations": [{"role": "user", "content": "什么是NAT64？"}, {"role": "assistant", "content": "NAT64是IPv6过渡技术，实现IPv6客户端与IPv4服务器的通信，将IPv6地址转换为IPv4地址，反之亦然，允许仅支持IPv6的设备访问IPv4网络资源，无需双栈支持。"}]} {"conversations": [{"role": "user", "content": "什么是网络存储？"}, {"role": "assistant", "content": "网络存储是通过网络提供存储服务的技术，使多个设备能共享存储资源，常见类型有NAS（网络附加存储，如家用网盘）、SAN（存储区域网络，高速专用存储网络），适用于数据集中管理和共享。"}]} {"conversations": [{"role": "user", "content": "什么是NAS和SAN的区别？"}, {"role": "assistant", "content": "NAS通过TCP/IP网络提供文件级存储服务（如CIFS、NFS协议），易于部署和访问；SAN通过专用网络（如光纤通道）提供块级存储服务，速度快、延迟低，适合高性能存储需求（如数据库）。"}]} {"conversations": [{"role": "user", "content": "什么是iSCSI？"}, {"role": "assistant", "content": "iSCSI（互联网小型计算机系统接口）是将SCSI存储命令封装在IP数据包中传输的协议，使通过TCP/IP网络实现块级存储访问，低成本替代光纤通道SAN，广泛用于中小型企业存储方案。"}]} {"conversations": [{"role": "user", "content": "什么是FTP的主动模式？"}, {"role": "assistant", "content": "FTP主动模式中，客户端向服务器的21号端口建立控制连接，并告知服务器自己的数据端口；服务器从20号端口主动连接客户端的数据端口建立数据连接，用于传输文件数据。"}]} {"conversations": [{"role": "user", "content": "什么是FTP的被动模式？"}, {"role": "assistant", "content": "FTP被动模式中，客户端建立控制连接后，服务器打开一个临时数据端口并告知客户端；客户端主动从自己的数据端口连接服务器的临时数据端口建立数据连接，适合客户端在防火墙后的场景。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP的长轮询？"}, {"role": "assistant", "content": "HTTP长轮询是客户端向服务器发送请求，服务器若没有新数据则保持连接，直到有数据或超时才返回响应；客户端收到响应后立即发送新请求，模拟实时通信，是WebSocket普及前的常用实时技术。"}]} {"conversations": [{"role": "user", "content": "什么是HTTP的服务器推送？"}, {"role": "assistant", "content": "HTTP服务器推送是HTTP/2的特性，服务器可在客户端请求一个资源时，主动推送相关资源（如CSS、JS文件），无需客户端额外请求，减少请求次数，加快网页加载速度。"}]} {"conversations": [{"role": "user", "content": "什么是WebSocket的帧？"}, {"role": "assistant", "content": "WebSocket帧是WebSocket协议中数据传输的基本单位，包含帧类型（文本/二进制）、掩码（客户端发送的帧需掩码）、 payload长度和数据，支持分片传输大消息，确保数据可靠传输。"}]} {"conversations": [{"role": "user", "content": "什么是SSL/TLS握手？"}, {"role": "assistant", "content": "SSL/TLS握手是客户端和服务器建立加密连接的过程，包括协商加密算法、交换密钥材料、验证服务器身份（通过证书），最终生成会话密钥用于后续数据加密，确保通信安全。"}]} {"conversations": [{"role": "user", "content": "什么是会话密钥？"}, {"role": "assistant", "content": "会话密钥是SSL/TLS握手过程中生成的临时密钥，用于对单次会话中的数据进行对称加密，会话结束后失效。相比使用公钥加密所有数据，会话密钥加密速度更快，提高通信效率。"}]} {"conversations": [{"role": "user", "content": "什么是证书链？"}, {"role": "assistant", "content": "证书链是由终端用户证书、中间CA证书和根CA证书组成的层级结构，终端证书由中间CA签名，中间CA证书由更高层级CA签名，最终追溯到根CA，通过验证证书链确保终端证书的合法性。"}]} {"conversations": [{"role": "user", "content": "什么是OCSP？"}, {"role": "assistant", "content": "OCSP（在线证书状态协议）用于查询数字证书的当前状态（有效、吊销、未知），客户端向OCSP服务器发送查询请求，服务器返回证书状态，相比CRL（证书吊销列表）能更实时地获取证书状态。"}]} {"conversations": [{"role": "user", "content": "什么是WLAN的漫游？"}, {"role": "assistant", "content": "WLAN漫游是指无线设备在移动过程中，从一个AP的覆盖范围切换到另一个AP的覆盖范围时，保持网络连接不中断的过程，通过相同SSID、无缝认证和快速切换实现，确保移动中的网络连续性。"}]} {"conversations": [{"role": "user", "content": "什么是SSID？"}, {"role": "assistant", "content": "SSID（服务集标识符）是无线局域网的名称，用于标识不同的WLAN网络，长度不超过32个字符。设备通过选择SSID连接对应的无线网络，多个AP可使用相同SSID形成扩展覆盖。"}]} {"conversations": [{"role": "user", "content": "什么是BSS和ESS？"}, {"role": "assistant", "content": "BSS（基本服务集）是单个AP及其覆盖范围内的无线设备组成的网络；ESS（扩展服务集）是多个使用相同SSID的BSS通过有线网络连接形成的网络，实现更大范围的无线覆盖，支持漫游。"}]} {"conversations": [{"role": "user", "content": "什么是蓝牙？"}, {"role": "assistant", "content": "蓝牙是短距离无线通信技术，工作在2.4GHz频段，传输距离通常在10米内，用于设备间近距离数据传输（如手机与耳机、键盘连接），功耗低，适用于物联网和个人设备互联。"}]} {"conversations": [{"role": "user", "content": "什么是ZigBee？"}, {"role": "assistant", "content": "ZigBee是基于IEEE 802.15.4标准的低功耗、低数据率无线通信技术，传输距离几十到几百米，支持多跳组网，适用于物联网传感器网络（如智能家居、工业监控），成本低且功耗极低。"}]} {"conversations": [{"role": "user", "content": "什么是LoRa？"}, {"role": "assistant", "content": "LoRa是远距离无线通信技术，采用扩频调制技术，传输距离可达数公里，功耗低，适合低速率、远距离的物联网应用（如智能表计、环境监测），通常工作在免授权频段。"}]} {"conversations": [{"role": "user", "content": "什么是NB-IoT？"}, {"role": "assistant", "content": "NB-IoT（窄带物联网）是基于蜂窝网络的低功耗广域网技术，支持海量连接、深度覆盖（穿墙能力强）、超低功耗，适用于物联网设备（如智能停车、远程抄表），直接接入移动运营商网络。"}]} {"conversations": [{"role": "user", "content": "什么是5G的eMBB、uRLLC、mMTC？"}, {"role": "assistant", "content": "5G的三大应用场景：eMBB（增强移动宽带）提供超高带宽（如4K/8K视频）；uRLLC（超高可靠超低延迟通信）支持毫秒级延迟（如自动驾驶、工业控制）；mMTC（海量机器类通信）支持大规模物联网设备连接。"}]} {"conversations": [{"role": "user", "content": "什么是边缘节点？"}, {"role": "assistant", "content": "边缘节点是位于网络边缘（靠近用户或数据源）的计算和存储设备，如基站、边缘服务器、智能网关等，负责本地数据处理和服务提供，减少数据传输到云端的延迟，支持边缘计算应用。"}]} {"conversations": [{"role": "user", "content": "什么是网络功能虚拟化？"}, {"role": "assistant", "content": "网络功能虚拟化（NFV）将传统网络设备（如路由器、防火墙）的功能通过软件实现，运行在通用服务器上，替代专用硬件，提高网络灵活性、降低成本，便于快速部署和扩展网络功能。"}]} {"conversations": [{"role": "user", "content": "什么是容器网络？"}, {"role": "assistant", "content": "容器网络是为容器化应用提供网络连接的技术，负责容器之间、容器与外部网络的通信，支持容器的动态创建和迁移，常见技术有Docker桥接、Calico、Flannel等，是云计算和微服务的基础。"}]} {"conversations": [{"role": "user", "content": "什么是SD-WAN？"}, {"role": "assistant", "content": "SD-WAN（软件定义广域网）将SDN技术应用于广域网，通过集中控制器动态管理广域网连接（如MPLS、互联网），优化流量路径，提高带宽利用率，降低企业广域网成本，简化管理。"}]} {"conversations": [{"role": "user", "content": "什么是量子密钥分发？"}, {"role": "assistant", "content": "量子密钥分发（QKD）利用量子力学原理生成和分发加密密钥，任何窃听行为都会改变量子状态被检测到，确保密钥绝对安全，是未来保障网络通信安全的前沿技术，可抵御量子计算带来的威胁。"}]} {"conversations": [{"role": "user", "content": "什么是网络爬虫？"}, {"role": "assistant", "content": "网络爬虫是自动抓取网页内容的程序，通过HTTP请求获取网页，解析并提取信息，常用于搜索引擎（如Google、百度）建立索引，也可用于数据采集，但需遵守网站robots协议和法律法规。"}]} {"conversations": [{"role": "user", "content": "什么是P2P网络？"}, {"role": "assistant", "content": "P2P（对等网络）中所有节点地位平等，既是客户端也是服务器，可直接相互通信，无需中心服务器，适用于文件共享（如BitTorrent）、即时通信等，具有去中心化、抗单点故障的特点。"}]} {"conversations": [{"role": "user", "content": "什么是BitTorrent？"}, {"role": "assistant", "content": "BitTorrent是基于P2P的文件共享协议，将文件分割为小块，下载者同时从多个其他下载者（种子）处获取不同块，同时上传已下载的块，提高下载速度，减轻单一服务器负担，是流行的大文件传输方式。"}]} {"conversations": [{"role": "user", "content": "什么是NAT穿透？"}, {"role": "assistant", "content": "NAT穿透是解决处于不同NAT后的设备无法直接通信的技术，通过中间服务器协助建立连接（如UDP打洞、STUN/TURN协议），使P2P应用（如视频通话、文件传输）能在NAT环境下正常工作。"}]} {"conversations": [{"role": "user", "content": "什么是STUN和TURN服务器？"}, {"role": "assistant", "content": "STUN服务器帮助设备发现自己的公网IP和端口；当STUN无法穿透NAT时，TURN服务器作为中继，转发两个设备之间的数据，两者均用于解决NAT环境下的P2P通信问题，是WebRTC等技术的基础。"}]} {"conversations": [{"role": "user", "content": "什么是WebRTC？"}, {"role": "assistant", "content": "WebRTC（网页实时通信）是支持浏览器之间直接进行实时音视频通话和数据传输的技术标准，无需插件，内置NAT穿透、编解码等功能，广泛用于视频会议、在线教育等实时通信场景。"}]} {"conversations": [{"role": "user", "content": "什么是SIP协议？"}, {"role": "assistant", "content": "SIP（会话初始协议）用于建立、修改和终止多媒体会话（如语音通话、视频会议），定义了会话参与者的定位、邀请、会话参数协商等过程，常与RTP协议配合使用，是VoIP的核心协议之一。"}]} {"conversations": [{"role": "user", "content": "什么是RTP协议？"}, {"role": "assistant", "content": "RTP（实时传输协议）用于实时音视频数据的传输，提供时间戳、序列号等信息，确保数据的实时性和顺序性，但不提供可靠传输，通常与RTCP（实时传输控制协议）配合，监控传输质量。"}]} {"conversations": [{"role": "user", "content": "什么是VoIP？"}, {"role": "assistant", "content": "VoIP（网络电话）是通过IP网络传输语音信号的技术，将模拟语音转换为数字数据包，通过互联网或专用IP网络传输，相比传统电话成本更低，支持更多功能（如视频通话、语音信箱）。"}]} {"conversations": [{"role": "user", "content": "什么是网络时延的组成部分？"}, {"role": "assistant", "content": "网络时延包括：传输时延（数据发送到介质的时间）、传播时延（信号在介质中传播的时间）、处理时延（设备处理数据包的时间）、排队时延（数据包在缓冲区等待的时间），总时延为四者之和。"}]} {"conversations": [{"role": "user", "content": "什么是带宽时延积？"}, {"role": "assistant", "content": "带宽时延积是链路带宽与往返时延（RTT）的乘积，表示链路中可同时传输的最大数据量（以比特为单位），是TCP拥塞控制的重要参数，高带宽高延迟网络（如卫星链路）的带宽时延积较大。"}]} {"conversations": [{"role": "user", "content": "什么是网络抖动？"}, {"role": "assistant", "content": "网络抖动是指数据包传输延迟的变化量，即不同数据包的往返时间（RTT）差异，抖动过大会导致音视频播放卡顿、失真，实时通信应用（如VoIP）对抖动非常敏感，可通过缓冲区缓解。"}]} {"conversations": [{"role": "user", "content": "什么是服务等级协议？"}, {"role": "assistant", "content": "服务等级协议（SLA）是服务提供商与客户之间的协议，明确规定服务质量指标（如可用性、响应时间、丢包率）及不达标时的补偿措施，确保服务质量符合客户预期，常见于云服务、网络服务等领域。"}]} {"conversations": [{"role": "user", "content": "什么是网络可用性？"}, {"role": "assistant", "content": "网络可用性是网络正常运行时间占总时间的比例，通常以百分比表示（如99.9%），是衡量网络可靠性的重要指标，高可用性网络通过冗余设计（如双电源、双链路）减少故障时间。"}]} {"conversations": [{"role": "user", "content": "什么是网络冗余？"}, {"role": "assistant", "content": "网络冗余是通过增加备用设备、链路或路径，确保在主设备/链路故障时，备用资源能自动接管，避免网络中断，提高网络可靠性的设计，如双路由器、链路聚合、STP等都是冗余技术。"}]} {"conversations": [{"role": "user", "content": "什么是热备份和冷备份？"}, {"role": "assistant", "content": "热备份中，备用设备实时同步主设备状态，主设备故障时可立即切换，几乎无中断；冷备份中，备用设备处于关闭或未同步状态，需手动启动和配置，切换时间长，成本较低，适用于非关键服务。"}]} {"conversations": [{"role": "user", "content": "什么是故障转移？"}, {"role": "assistant", "content": "故障转移是当主设备或链路发生故障时，自动将服务切换到备用设备或链路的过程，确保服务不中断，是实现高可用性的关键技术，常见于服务器集群、冗余网络设备中。"}]} {"conversations": [{"role": "user", "content": "什么是网络测试工具？"}, {"role": "assistant", "content": "网络测试工具用于诊断网络问题、评估性能，常见的有ping（测试连通性）、traceroute（跟踪路由路径）、tcpdump（抓包分析）、iperf（测试带宽）、nslookup（DNS查询）等，是网络运维的重要工具。"}]} {"conversations": [{"role": "user", "content": "什么是traceroute？"}, {"role": "assistant", "content": "traceroute（Windows中为tracert）是跟踪数据包从源端到目的端所经过的路由器的工具，通过发送不同TTL值的数据包，根据返回的ICMP超时报文确定路径上的每个路由器，帮助定位网络故障点。"}]} {"conversations": [{"role": "user", "content": "什么是tcpdump？"}, {"role": "assistant", "content": "tcpdump是命令行网络抓包工具，可捕获网络中的数据包并显示其详细信息（如源/目的IP、端口、协议、数据内容），用于网络故障诊断、协议分析和安全审计，是网络工程师的常用工具。"}]} {"conversations": [{"role": "user", "content": "什么是Wireshark？"}, {"role": "assistant", "content": "Wireshark是图形化网络协议分析工具，支持捕获和解析数百种网络协议的数据包，提供直观的界面展示数据包结构和内容，功能比tcpdump更丰富，广泛用于网络故障排查和协议学习。"}]} {"conversations": [{"role": "user", "content": "什么是网络性能基准测试？"}, {"role": "assistant", "content": "网络性能基准测试是在标准条件下测量网络的关键性能指标（如带宽、延迟、吞吐量），建立性能基准，用于评估网络优化效果、比较不同设备或配置的性能，确保网络满足预期需求。"}]} {"conversations": [{"role": "user", "content": "什么是网络仿真？"}, {"role": "assistant", "content": "网络仿真是通过软件模拟网络拓扑、协议和流量，在虚拟环境中测试网络性能、协议行为或故障场景，无需实际硬件，降低测试成本，常用于网络协议研究、新网络技术验证等领域，如NS2/NS3模拟器。"}]}middle25projects/2024103876/src/scripts/000077500000000000000000000000001506644026200176005ustar00rootroot00000000000000middle25projects/2024103876/src/scripts/eval_model.py000066400000000000000000000166371506644026200222760ustar00rootroot00000000000000import argparse import random import warnings import numpy as np from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from model.model_lora import * warnings.filterwarnings('ignore') def init_model(args): tokenizer = AutoTokenizer.from_pretrained('./model/') if args.load == 0: moe_path = '_moe' if args.use_moe else '' modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason', 4: 'grpo'} ckp = f'./{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth' model = MiniMindForCausalLM(MiniMindConfig( hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, use_moe=args.use_moe )) model.load_state_dict(torch.load(ckp, map_location=args.device), strict=True) if args.lora_name != 'None': apply_lora(model) load_lora(model, f'./{args.out_dir}/lora/{args.lora_name}_{args.hidden_size}.pth') else: transformers_model_path = './MiniMind2' tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') return model.eval().to(args.device), tokenizer def get_prompt_datas(args): if args.model_mode == 0: # pretrain模型的接龙能力（无法对话） prompt_datas = [ '马克思主义基本原理', '人类大脑的主要功能', '万有引力原理是', '世界上最高的山峰是', '二氧化碳在空气中', '地球上最大的动物有', '杭州市的美食有' ] else: if args.lora_name == 'None': # 通用对话问题 prompt_datas = [ '请介绍一下自己。', '你更擅长哪一个学科？', '鲁迅的《狂人日记》是如何批判封建礼教的？', '我咳嗽已经持续了两周，需要去医院检查吗？', '详细的介绍光速的物理概念。', '推荐一些杭州的特色美食吧。', '请为我讲解“大语言模型”这个概念。', '如何理解ChatGPT？', 'Introduce the history of the United States, please.' ] else: # 特定领域问题 lora_prompt_datas = { 'lora_identity': [ "你是ChatGPT吧。", "你叫什么名字？", "你和openai是什么关系？" ], 'lora_medical': [ '我最近经常感到头晕，可能是什么原因？', '我咳嗽已经持续了两周，需要去医院检查吗？', '服用抗生素时需要注意哪些事项？', '体检报告中显示胆固醇偏高，我该怎么办？', '孕妇在饮食上需要注意什么？', '老年人如何预防骨质疏松？', '我最近总是感到焦虑，应该怎么缓解？', '如果有人突然晕倒，应该如何急救？' ], 'lora_network': [ '什么是计算机网络', '什么是tcp协议', '拥塞控制算法有哪些', ], } prompt_datas = lora_prompt_datas[args.lora_name] return prompt_datas # 设置可复现的随机种子 def setup_seed(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False def main(): parser = argparse.ArgumentParser(description="Chat with MiniMind") parser.add_argument('--lora_name', default='None', type=str) parser.add_argument('--out_dir', default='out', type=str) parser.add_argument('--temperature', default=0.85, type=float) parser.add_argument('--top_p', default=0.85, type=float) parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str) # 此处max_seq_len（最大输出长度）并不意味模型具有对应的长文本的性能，仅防止QA出现被截断的问题 # MiniMind2-moe (145M)：(hidden_size=640, num_hidden_layers=8, use_moe=True) # MiniMind2-Small (26M)：(hidden_size=512, num_hidden_layers=8) # MiniMind2 (104M)：(hidden_size=768, num_hidden_layers=16) parser.add_argument('--hidden_size', default=512, type=int) parser.add_argument('--num_hidden_layers', default=8, type=int) parser.add_argument('--max_seq_len', default=8192, type=int) parser.add_argument('--use_moe', default=False, type=bool) # 携带历史对话上下文条数 # history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文 # 模型未经过外推微调时，在更长的上下文的chat_template时难免出现性能的明显退化，因此需要注意此处设置 parser.add_argument('--history_cnt', default=0, type=int) parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载") parser.add_argument('--model_mode', default=1, type=int, help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型") args = parser.parse_args() model, tokenizer = init_model(args) prompts = get_prompt_datas(args) test_mode = int(input('[0] 自动测试\n[1] 手动输入\n')) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) messages = [] for idx, prompt in enumerate(prompts if test_mode == 0 else iter(lambda: input('👶: '), '')): setup_seed(random.randint(0, 2048)) # setup_seed(2025) # 如需固定每次输出则换成【固定】的随机种子 if test_mode == 0: print(f'👶: {prompt}') messages = messages[-args.history_cnt:] if args.history_cnt else [] messages.append({"role": "user", "content": prompt}) new_prompt = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) if args.model_mode != 0 else (tokenizer.bos_token + prompt) inputs = tokenizer( new_prompt, return_tensors="pt", truncation=True ).to(args.device) print('🤖️: ', end='') generated_ids = model.generate( inputs["input_ids"], max_new_tokens=args.max_seq_len, num_return_sequences=1, do_sample=True, attention_mask=inputs["attention_mask"], pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, streamer=streamer, top_p=args.top_p, temperature=args.temperature ) response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) messages.append({"role": "assistant", "content": response}) print('\n\n') if __name__ == "__main__": main() middle25projects/2024103876/src/scripts/model/000077500000000000000000000000001506644026200207005ustar00rootroot00000000000000middle25projects/2024103876/src/scripts/model/__init__.py000066400000000000000000000000001506644026200227770ustar00rootroot00000000000000middle25projects/2024103876/src/scripts/model/model_lora.py000066400000000000000000000034371506644026200233760ustar00rootroot00000000000000import torch from torch import optim, nn # 定义Lora网络结构 class LoRA(nn.Module): def __init__(self, in_features, out_features, rank): super().__init__() self.rank = rank # LoRA的秩（rank），控制低秩矩阵的大小 self.A = nn.Linear(in_features, rank, bias=False) # 低秩矩阵A self.B = nn.Linear(rank, out_features, bias=False) # 低秩矩阵B # 矩阵A高斯初始化 self.A.weight.data.normal_(mean=0.0, std=0.02) # 矩阵B全0初始化 self.B.weight.data.zero_() def forward(self, x): return self.B(self.A(x)) def apply_lora(model, rank=8): for name, module in model.named_modules(): if isinstance(module, nn.Linear) and module.weight.shape[0] == module.weight.shape[1]: lora = LoRA(module.weight.shape[0], module.weight.shape[1], rank=rank).to(model.device) setattr(module, "lora", lora) original_forward = module.forward # 显式绑定 def forward_with_lora(x, layer1=original_forward, layer2=lora): return layer1(x) + layer2(x) module.forward = forward_with_lora def load_lora(model, path): state_dict = torch.load(path, map_location=model.device) for name, module in model.named_modules(): if hasattr(module, 'lora'): lora_state = {k.replace(f'{name}.lora.', ''): v for k, v in state_dict.items() if f'{name}.lora.' in k} module.lora.load_state_dict(lora_state) def save_lora(model, path): state_dict = {} for name, module in model.named_modules(): if hasattr(module, 'lora'): lora_state = {f'{name}.lora.{k}': v for k, v in module.lora.state_dict().items()} state_dict.update(lora_state) torch.save(state_dict, path) middle25projects/2024103876/src/scripts/model/model_minimind.py000066400000000000000000000501501506644026200242370ustar00rootroot00000000000000# 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 # MiniMind Config # 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 from transformers import PretrainedConfig class MiniMindConfig(PretrainedConfig): model_type = "minimind" def __init__( self, dropout: float = 0.0, bos_token_id: int = 1, eos_token_id: int = 2, hidden_act: str = 'silu', hidden_size: int = 512, intermediate_size: int = None, max_position_embeddings: int = 32768, num_attention_heads: int = 8, num_hidden_layers: int = 8, num_key_value_heads: int = 2, vocab_size: int = 6400, rms_norm_eps: float = 1e-05, rope_theta: int = 1000000.0, flash_attn: bool = True, #################################################### # Here are the specific configurations of MOE # When use_moe is false, the following is invalid #################################################### use_moe: bool = False, num_experts_per_tok: int = 2, n_routed_experts: int = 4, n_shared_experts: int = 1, scoring_func: str = 'softmax', aux_loss_alpha: float = 0.1, seq_aux: bool = True, norm_topk_prob: bool = True, **kwargs ): super().__init__(**kwargs) self.dropout = dropout self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.hidden_act = hidden_act self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.max_position_embeddings = max_position_embeddings self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers self.num_key_value_heads = num_key_value_heads self.vocab_size = vocab_size self.rms_norm_eps = rms_norm_eps self.rope_theta = rope_theta self.flash_attn = flash_attn #################################################### # Here are the specific configurations of MOE # When use_moe is false, the following is invalid #################################################### self.use_moe = use_moe self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量 self.n_routed_experts = n_routed_experts # 总的专家数量 self.n_shared_experts = n_shared_experts # 共享专家 self.scoring_func = scoring_func # 评分函数，默认为'softmax' self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数 self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失 self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率 # 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 # MiniMind Model # 📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘📘 import math import torch from torch import nn from transformers.activations import ACT2FN from typing import Optional, Tuple, List, Union import torch.nn.functional as F from transformers import PreTrainedModel, GenerationMixin, PretrainedConfig from transformers.modeling_outputs import CausalLMOutputWithPast class RMSNorm(torch.nn.Module): def __init__(self, dim: int, eps: float = 1e-5): super().__init__() self.eps = eps self.weight = nn.Parameter(torch.ones(dim)) def _norm(self, x): return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) def forward(self, x): return self.weight * self._norm(x.float()).type_as(x) def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), theta: float = 1e6): freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) t = torch.arange(end, device=freqs.device) freqs = torch.outer(t, freqs).float() freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) return freqs_cos, freqs_sin def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): def rotate_half(x): return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1) q_embed = (q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim)) k_embed = (k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim)) return q_embed, k_embed def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor: """torch.repeat_interleave(x, dim=2, repeats=n_rep)""" bs, slen, num_key_value_heads, head_dim = x.shape if n_rep == 1: return x return ( x[:, :, :, None, :] .expand(bs, slen, num_key_value_heads, n_rep, head_dim) .reshape(bs, slen, num_key_value_heads * n_rep, head_dim) ) class Attention(nn.Module): def __init__(self, args: MiniMindConfig): super().__init__() self.num_key_value_heads = args.num_attention_heads if args.num_key_value_heads is None else args.num_key_value_heads assert args.num_attention_heads % self.num_key_value_heads == 0 self.n_local_heads = args.num_attention_heads self.n_local_kv_heads = self.num_key_value_heads self.n_rep = self.n_local_heads // self.n_local_kv_heads self.head_dim = args.hidden_size // args.num_attention_heads self.q_proj = nn.Linear(args.hidden_size, args.num_attention_heads * self.head_dim, bias=False) self.k_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.v_proj = nn.Linear(args.hidden_size, self.num_key_value_heads * self.head_dim, bias=False) self.o_proj = nn.Linear(args.num_attention_heads * self.head_dim, args.hidden_size, bias=False) self.attn_dropout = nn.Dropout(args.dropout) self.resid_dropout = nn.Dropout(args.dropout) self.dropout = args.dropout self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0") def forward(self, x: torch.Tensor, position_embeddings: Tuple[torch.Tensor, torch.Tensor], # 修改为接收cos和sin past_key_value: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, use_cache=False, attention_mask: Optional[torch.Tensor] = None): bsz, seq_len, _ = x.shape xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x) xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim) xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim) cos, sin = position_embeddings xq, xk = apply_rotary_pos_emb(xq, xk, cos[:seq_len], sin[:seq_len]) # kv_cache实现 if past_key_value is not None: xk = torch.cat([past_key_value[0], xk], dim=1) xv = torch.cat([past_key_value[1], xv], dim=1) past_kv = (xk, xv) if use_cache else None xq, xk, xv = ( xq.transpose(1, 2), repeat_kv(xk, self.n_rep).transpose(1, 2), repeat_kv(xv, self.n_rep).transpose(1, 2) ) if self.flash and seq_len != 1: dropout_p = self.dropout if self.training else 0.0 attn_mask = None if attention_mask is not None: attn_mask = attention_mask.view(bsz, 1, 1, -1).expand(bsz, self.n_local_heads, seq_len, -1) attn_mask = attn_mask.bool() if attention_mask is not None else None output = F.scaled_dot_product_attention(xq, xk, xv, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=True) else: scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim) scores = scores + torch.triu( torch.full((seq_len, seq_len), float("-inf"), device=scores.device), diagonal=1 ).unsqueeze(0).unsqueeze(0) # scores+mask if attention_mask is not None: extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) extended_attention_mask = (1.0 - extended_attention_mask) * -1e9 scores = scores + extended_attention_mask scores = F.softmax(scores.float(), dim=-1).type_as(xq) scores = self.attn_dropout(scores) output = scores @ xv output = output.transpose(1, 2).reshape(bsz, seq_len, -1) output = self.resid_dropout(self.o_proj(output)) return output, past_kv class FeedForward(nn.Module): def __init__(self, config: MiniMindConfig): super().__init__() if config.intermediate_size is None: intermediate_size = int(config.hidden_size * 8 / 3) config.intermediate_size = 64 * ((intermediate_size + 64 - 1) // 64) self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False) self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False) self.dropout = nn.Dropout(config.dropout) self.act_fn = ACT2FN[config.hidden_act] def forward(self, x): return self.dropout(self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))) class MoEGate(nn.Module): def __init__(self, config: MiniMindConfig): super().__init__() self.config = config self.top_k = config.num_experts_per_tok self.n_routed_experts = config.n_routed_experts self.scoring_func = config.scoring_func self.alpha = config.aux_loss_alpha self.seq_aux = config.seq_aux self.norm_topk_prob = config.norm_topk_prob self.gating_dim = config.hidden_size self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) self.reset_parameters() def reset_parameters(self) -> None: import torch.nn.init as init init.kaiming_uniform_(self.weight, a=math.sqrt(5)) def forward(self, hidden_states): bsz, seq_len, h = hidden_states.shape hidden_states = hidden_states.view(-1, h) logits = F.linear(hidden_states, self.weight, None) if self.scoring_func == 'softmax': scores = logits.softmax(dim=-1) else: raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 topk_weight = topk_weight / denominator if self.training and self.alpha > 0.0: scores_for_aux = scores aux_topk = self.top_k topk_idx_for_aux_loss = topk_idx.view(bsz, -1) if self.seq_aux: scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) ce.scatter_add_(1, topk_idx_for_aux_loss, torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( seq_len * aux_topk / self.n_routed_experts) aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha else: mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) ce = mask_ce.float().mean(0) Pi = scores_for_aux.mean(0) fi = ce * self.n_routed_experts aux_loss = (Pi * fi).sum() * self.alpha else: aux_loss = 0 return topk_idx, topk_weight, aux_loss class MOEFeedForward(nn.Module): def __init__(self, config: MiniMindConfig): super().__init__() self.config = config self.experts = nn.ModuleList([ FeedForward(config) for _ in range(config.n_routed_experts) ]) self.gate = MoEGate(config) if config.n_shared_experts > 0: self.shared_experts = nn.ModuleList([ FeedForward(config) for _ in range(config.n_shared_experts) ]) def forward(self, x): identity = x orig_shape = x.shape bsz, seq_len, _ = x.shape # 使用门控机制选择专家 topk_idx, topk_weight, aux_loss = self.gate(x) x = x.view(-1, x.shape[-1]) flat_topk_idx = topk_idx.view(-1) if self.training: x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0) y = torch.empty_like(x, dtype=torch.float16) for i, expert in enumerate(self.experts): y[flat_topk_idx == i] = expert(x[flat_topk_idx == i]).to(y.dtype) # 确保类型一致 y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) y = y.view(*orig_shape) else: y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) if self.config.n_shared_experts > 0: for expert in self.shared_experts: y = y + expert(identity) self.aux_loss = aux_loss return y @torch.no_grad() def moe_infer(self, x, flat_expert_indices, flat_expert_weights): expert_cache = torch.zeros_like(x) idxs = flat_expert_indices.argsort() tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) token_idxs = idxs // self.config.num_experts_per_tok # 当tokens_per_expert = [6, 15, 20, 26]，tokens_per_expert.shape[0]即为专家数量（此时为4） # 且token_idxs = [3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...] 时 # 意味token_idxs[:6] -> [3, 7, 19, 21, 24, 25]这6个位置属于专家0处理的token（每个token有可能被多个专家处理，这取决于num_experts_per_tok） # 接下来9个位置token_idxs[6:15] -> [4, 5, 6, 10, 11, 12...]属于专家1处理的token...依此类推 for i, end_idx in enumerate(tokens_per_expert): start_idx = 0 if i == 0 else tokens_per_expert[i - 1] if start_idx == end_idx: continue expert = self.experts[i] exp_token_idx = token_idxs[start_idx:end_idx] expert_tokens = x[exp_token_idx] expert_out = expert(expert_tokens).to(expert_cache.dtype) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out) return expert_cache class MiniMindBlock(nn.Module): def __init__(self, layer_id: int, config: MiniMindConfig): super().__init__() self.num_attention_heads = config.num_attention_heads self.hidden_size = config.hidden_size self.head_dim = config.hidden_size // config.num_attention_heads self.self_attn = Attention(config) self.layer_id = layer_id self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config) def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None): residual = hidden_states hidden_states, present_key_value = self.self_attn( self.input_layernorm(hidden_states), position_embeddings, past_key_value, use_cache, attention_mask ) hidden_states += residual hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states)) return hidden_states, present_key_value class MiniMindModel(nn.Module): def __init__(self, config: MiniMindConfig): super().__init__() self.config = config self.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size) self.dropout = nn.Dropout(config.dropout) self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)]) self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.hidden_size // config.num_attention_heads, end=config.max_position_embeddings, theta=config.rope_theta) self.register_buffer("freqs_cos", freqs_cos, persistent=False) self.register_buffer("freqs_sin", freqs_sin, persistent=False) def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, use_cache: bool = False, **kwargs): batch_size, seq_length = input_ids.shape past_key_values = past_key_values or [None] * len(self.layers) start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0 hidden_states = self.dropout(self.embed_tokens(input_ids)) position_embeddings = ( self.freqs_cos[start_pos:start_pos + seq_length], self.freqs_sin[start_pos:start_pos + seq_length] ) presents = [] for layer_idx, (layer, past_key_value) in enumerate(zip(self.layers, past_key_values)): hidden_states, present = layer( hidden_states, position_embeddings, past_key_value=past_key_value, use_cache=use_cache, attention_mask=attention_mask ) presents.append(present) hidden_states = self.norm(hidden_states) aux_loss = sum( layer.mlp.aux_loss for layer in self.layers if isinstance(layer.mlp, MOEFeedForward) ) return hidden_states, presents, aux_loss class MiniMindForCausalLM(PreTrainedModel, GenerationMixin): config_class = MiniMindConfig def __init__(self, config: MiniMindConfig = None): self.config = config or MiniMindConfig() super().__init__(self.config) self.model = MiniMindModel(self.config) self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False) self.model.embed_tokens.weight = self.lm_head.weight self.OUT = CausalLMOutputWithPast() def forward(self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None, use_cache: bool = False, logits_to_keep: Union[int, torch.Tensor] = 0, **args): h, past_kvs, aux_loss = self.model( input_ids=input_ids, attention_mask=attention_mask, past_key_values=past_key_values, use_cache=use_cache, **args ) slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.lm_head(h[:, slice_indices, :]) self.OUT.__setitem__('last_hidden_state', h) self.OUT.__setitem__('logits', logits) self.OUT.__setitem__('aux_loss', aux_loss) self.OUT.__setitem__('past_key_values', past_kvs) return self.OUT 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"Ġsignificantly": 5308, "Ġsource": 5309, "Ġant": 5310, "Ġeducational": 5311, "Ġtask": 5312, "Ġhandle": 5313, "æIJľ": 5314, "ĠSp": 5315, "Ġcalled": 5316, "Ġterms": 5317, "æ²ī": 5318, "Ġwin": 5319, "duction": 5320, "Ġmodern": 5321, "Ġcuisine": 5322, "å¥Ĺ": 5323, "触": 5324, "olutely": 5325, "ç«¥": 5326, "pite": 5327, "Ġfelt": 5328, "Ġcompre": 5329, "Ġwond": 5330, "è¿IJè¡Į": 5331, "Ġresil": 5332, "çĽ¸ä¼¼": 5333, "éĩijèŀį": 5334, "çĪ±æĥħ": 5335, "ç¬Ķ": 5336, "èĪª": 5337, "è°Ī": 5338, "åĬĽçļĦ": 5339, "æľīæīĢ": 5340, "æ½ľ": 5341, "ulate": 5342, "Ġdetection": 5343, "å®£ä¼ł": 5344, "Ġmatter": 5345, "éĩıåŃIJ": 5346, "Write": 5347, "ç»ĵåIJĪ": 5348, "ç»ıè¿ĩ": 5349, "Ġdevelopers": 5350, "èª": 5351, "Ġ---": 5352, "人éĻħ": 5353, "çŃ¾": 5354, "ï¼ļâĢľ": 5355, "Ġinnovative": 5356, "ãĢĤâĢĿ": 5357, "å½¼": 5358, "饼": 5359, "è¿ĩ度": 5360, "Ġplanet": 5361, "åħ°": 5362, "å¸ģ": 5363, "æķ¬": 5364, "Ġlegal": 5365, "Ġlot": 5366, "æĪIJ为äºĨ": 5367, "iate": 5368, "Ġmis": 5369, "åģĩ设": 5370, "çļĦæĸĩç«ł": 5371, "ĠCompan": 5372, "Ġdoc": 5373, "Ġcareful": 5374, "Ġever": 5375, "æĪij们å°Ĩ": 5376, "ä¾ĭåŃIJ": 5377, "ä¹³": 5378, "ä½ľèĢħ": 5379, "åIJ§": 5380, "æļ´": 5381, "Ġremember": 5382, "缮çļĦ": 5383, "Ġput": 5384, "常è§ģçļĦ": 5385, "Ġfest": 5386, "建设": 5387, "å®ŀçĶ¨": 5388, "Ġactive": 5389, "çªĹ": 5390, "outh": 5391, "åİŁçIJĨ": 5392, "Ġtrying": 5393, "è¿·": 5394, "缸åIJĮ": 5395, "éħĴåºĹ": 5396, "Another": 5397, "æľĢä½³": 5398, "Ġanalytics": 5399, "Ġperpet": 5400, "ipment": 5401, "Ġå¦Ĥæŀľ": 5402, "è§Ĥä¼Ĺ": 5403, "Ġcelebr": 5404, "Ġheav": 5405, "Ġmeditation": 5406, "大æ°Ķ": 5407, "And": 5408, "ä¸įéĶĻ": 5409, "Ġwhether": 5410, "set": 5411, "Ġdemonstr": 5412, "ä¸Ģ款": 5413, "æĶ¶éĽĨ": 5414, "éĻIJåĪ¶": 5415, "Ġing": 5416, "Ġrevolution": 5417, "çľģ": 5418, "Ġscience": 5419, "缮åīį": 5420, "Ġthinking": 5421, "±ä¹IJ": 5422, "课ç¨ĭ": 5423, "Ġpack": 5424, "Ġimage": 5425, "loc": 5426, "Ġstories": 5427, "uck": 5428, "Ġsatisfaction": 5429, "Ġcollection": 5430, "ho": 5431, "èµŀ": 5432, "éĿ¢ä¸´": 5433, "Ġla": 5434, "Ġsymbol": 5435, "Ġemb": 5436, "Ġhabitats": 5437, "Ġlower": 5438, "Ġcontinues": 5439, "éľĩ": 5440, "åĵĪ": 5441, "ĠTake": 5442, "Ġenvironments": 5443, "Ġthree": 5444, "Ġenc": 5445, "ĠAcc": 5446, "æĦıåij³": 5447, "åݨ": 5448, "chan": 5449, "ĠHum": 5450, "Ġtrue": 5451, "åĪĩæĪIJ": 5452, "sing": 5453, "âĢĶâĢĶ": 5454, "åĩºæĿ¥": 5455, "Ġregion": 5456, "Ġinterpre": 5457, "Ġdiagnosis": 5458, "éŀ": 5459, "Ġdoing": 5460, "Ġrun": 5461, "Ġcoffee": 5462, "Ġmajor": 5463, "Ġmindfulness": 5464, "Ġaffordable": 5465, "çĻ¾": 5466, "Ġdetailed": 5467, "éĿŀ常éĩįè¦ģçļĦ": 5468, "çļĦæ²ŁéĢļ": 5469, "çļĦæķħ": 5470, "åĢĴåħ¥": 5471, "Ġthemes": 5472, "Ġnetwork": 5473, "ï¼īï¼ļ": 5474, "ĠUnited": 5475, "çļĦæĮĩ": 5476, "orts": 5477, "åį«çĶŁ": 5478, "Ġplanning": 5479, "æĥł": 5480, "åīª": 5481, "ĠProv": 5482, "çļĦåºĶçĶ¨": 5483, "Ġperi": 5484, "Ġaccountable": 5485, "çīĻ": 5486, "çļĦçģ": 5487, "Ġchoice": 5488, "ĠComm": 5489, "idents": 5490, "çļĦå®īåħ¨": 5491, "并ä¸į": 5492, "太éĺ³ç³»": 5493, "Ġreceive": 5494, "Ġclose": 5495, "çļĦæŶåĢĻ": 5496, "Ġchanging": 5497, "ä»·åĢ¼è§Ĥ": 5498, "Ġperpetu": 5499, "Ġseason": 5500, "Ġmen": 5501, "Ġlearned": 5502, "Ġsituation": 5503, "Ġreplace": 5504, "head": 5505, "让æĪij": 5506, "åľ¨ä¸Ģèµ·": 5507, "çļĦ空": 5508, "éľ²": 5509, "Ġenough": 5510, "å±ķçİ°": 5511, "Ġleaders": 5512, "ancing": 5513, "Ġtemperature": 5514, "åı«": 5515, "Ġ30": 5516, "æĦıåij³çĿĢ": 5517, "æ±ĩ": 5518, "ĠGovern": 5519, "Ġfocused": 5520, "uro": 5521, "Ġsimple": 5522, "Ġhiking": 5523, "æ¯Ĵ": 5524, "Ġcomprehens": 5525, "äºĪ": 5526, "Ġcreated": 5527, "cond": 5528, "页": 5529, "ĠWor": 5530, "è¯ģæį®": 5531, "Ġworkplace": 5532, "Ġcharacters": 5533, "çļĦ设计": 5534, "Ġmechan": 5535, "ĠDis": 5536, "ç¥ŀç§ĺ": 5537, "å·ŀ": 5538, "ĠOn": 5539, "", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "1": { "content": "<|im_start|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true }, "2": { "content": "<|im_end|>", "lstrip": false, "normalized": false, "rstrip": false, "single_word": false, "special": true } }, "additional_special_tokens": [], "bos_token": "<|im_start|>", "clean_up_tokenization_spaces": false, "eos_token": "<|im_end|>", "legacy": true, "model_max_length": 32768, "pad_token": "<|endoftext|>", "sp_model_kwargs": {}, "spaces_between_special_tokens": false, "tokenizer_class": "PreTrainedTokenizerFast", "unk_token": "<|endoftext|>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ '<|im_start|>system\\n' + system_message + '<|im_end|>\\n' }}{% else %}{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}" }middle25projects/2024103876/src/scripts/requirements.txt000066400000000000000000000007361506644026200230720ustar00rootroot00000000000000datasets==2.21.0 datasketch==1.6.4 Flask==3.0.3 Flask_Cors==4.0.0 jieba==0.42.1 jsonlines==4.0.0 marshmallow==3.22.0 matplotlib==3.10.0 ngrok==1.4.0 nltk==3.8 numpy==1.26.4 openai==1.59.6 pandas==1.5.3 peft==0.7.1 psutil==5.9.8 pydantic==2.8.2 rich==13.7.1 scikit_learn==1.5.1 sentence_transformers==2.3.1 simhash==2.1.2 tiktoken==0.5.1 transformers==4.48.0 jinja2==3.1.2 jsonlines==4.0.0 trl==0.13.0 ujson==5.1.0 wandb==0.18.3 streamlit==1.30.0 torch==2.3.0 torchvision==0.18.0middle25projects/2024103876/src/scripts/scripts/000077500000000000000000000000001506644026200212675ustar00rootroot00000000000000middle25projects/2024103876/src/scripts/scripts/chat_openai_api.py000066400000000000000000000020121506644026200247370ustar00rootroot00000000000000from openai import OpenAI client = OpenAI( api_key="ollama", base_url="http://127.0.0.1:8998/v1" ) stream = True conversation_history_origin = [] conversation_history = conversation_history_origin.copy() history_messages_num = 2 # 设置为偶数（Q+A），为0则每次不携带历史对话进行独立QA while True: query = input('[Q]: ') conversation_history.append({"role": "user", "content": query}) response = client.chat.completions.create( model="minimind", messages=conversation_history[-history_messages_num:], stream=stream ) if not stream: assistant_res = response.choices[0].message.content print('[A]: ', assistant_res) else: print('[A]: ', end='') assistant_res = '' for chunk in response: print(chunk.choices[0].delta.content or "", end="") assistant_res += chunk.choices[0].delta.content or "" conversation_history.append({"role": "assistant", "content": assistant_res}) print('\n\n') middle25projects/2024103876/src/scripts/scripts/convert_model.py000066400000000000000000000066221506644026200245070ustar00rootroot00000000000000import os import sys __package__ = "scripts" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import torch import warnings from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM from model.model_minimind import MiniMindConfig, MiniMindForCausalLM warnings.filterwarnings('ignore', category=UserWarning) # MoE模型需使用此函数转换 def convert_torch2transformers_minimind(torch_path, transformers_path, dtype=torch.bfloat16): MiniMindConfig.register_for_auto_class() MiniMindForCausalLM.register_for_auto_class("AutoModelForCausalLM") lm_model = MiniMindForCausalLM(lm_config) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') state_dict = torch.load(torch_path, map_location=device) lm_model.load_state_dict(state_dict, strict=False) lm_model = lm_model.to(dtype) # 转换模型权重精度 model_params = sum(p.numel() for p in lm_model.parameters() if p.requires_grad) print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)') lm_model.save_pretrained(transformers_path, safe_serialization=False) tokenizer = AutoTokenizer.from_pretrained('../model/') tokenizer.save_pretrained(transformers_path) print(f"模型已保存为 Transformers-MiniMind 格式: {transformers_path}") # LlamaForCausalLM结构兼容第三方生态 def convert_torch2transformers_llama(torch_path, transformers_path, dtype=torch.bfloat16): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') state_dict = torch.load(torch_path, map_location=device) llama_config = LlamaConfig( vocab_size=lm_config.vocab_size, hidden_size=lm_config.hidden_size, intermediate_size=64 * ((int(lm_config.hidden_size * 8 / 3) + 64 - 1) // 64), num_hidden_layers=lm_config.num_hidden_layers, num_attention_heads=lm_config.num_attention_heads, num_key_value_heads=lm_config.num_key_value_heads, max_position_embeddings=lm_config.max_seq_len, rms_norm_eps=lm_config.rms_norm_eps, rope_theta=lm_config.rope_theta, ) llama_model = LlamaForCausalLM(llama_config) llama_model.load_state_dict(state_dict, strict=False) llama_model = llama_model.to(dtype) # 转换模型权重精度 llama_model.save_pretrained(transformers_path) model_params = sum(p.numel() for p in llama_model.parameters() if p.requires_grad) print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)') tokenizer = AutoTokenizer.from_pretrained('../model/') tokenizer.save_pretrained(transformers_path) print(f"模型已保存为 Transformers-Llama 格式: {transformers_path}") def convert_transformers2torch(transformers_path, torch_path): model = AutoModelForCausalLM.from_pretrained(transformers_path, trust_remote_code=True) torch.save(model.state_dict(), torch_path) print(f"模型已保存为 PyTorch 格式: {torch_path}") if __name__ == '__main__': lm_config = MiniMindConfig(hidden_size=512, num_hidden_layers=16, max_seq_len=8192, use_moe=False) torch_path = f"../out/full_sft_{lm_config.hidden_size}{'_moe' if lm_config.use_moe else ''}.pth" transformers_path = '../MiniMind2' convert_torch2transformers_minimind(torch_path, transformers_path) # # # convert transformers to torch model # # convert_transformers2torch(transformers_path, torch_path) middle25projects/2024103876/src/scripts/scripts/serve_openai_api.py000066400000000000000000000150361506644026200251560ustar00rootroot00000000000000import argparse import json import os import sys __package__ = "scripts" sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) import time import torch import warnings import uvicorn from threading import Thread from queue import Queue from fastapi import FastAPI, HTTPException from fastapi.responses import StreamingResponse from pydantic import BaseModel from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer from model.model_minimind import MiniMindConfig, MiniMindForCausalLM from model.model_lora import apply_lora, load_lora warnings.filterwarnings('ignore') app = FastAPI() def init_model(args): if args.load == 0: tokenizer = AutoTokenizer.from_pretrained('../model/') moe_path = '_moe' if args.use_moe else '' modes = {0: 'pretrain', 1: 'full_sft', 2: 'rlhf', 3: 'reason'} ckp = f'../{args.out_dir}/{modes[args.model_mode]}_{args.hidden_size}{moe_path}.pth' model = MiniMindForCausalLM(MiniMindConfig( hidden_size=args.hidden_size, num_hidden_layers=args.num_hidden_layers, max_seq_len=args.max_seq_len, use_moe=args.use_moe )) model.load_state_dict(torch.load(ckp, map_location=device), strict=True) if args.lora_name != 'None': apply_lora(model) load_lora(model, f'../{args.out_dir}/{args.lora_name}_{args.hidden_size}.pth') else: model_path = '../MiniMind2' model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path) print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') return model.eval().to(device), tokenizer class ChatRequest(BaseModel): model: str messages: list temperature: float = 0.7 top_p: float = 0.92 max_tokens: int = 8192 stream: bool = False tools: list = [] class CustomStreamer(TextStreamer): def __init__(self, tokenizer, queue): super().__init__(tokenizer, skip_prompt=True, skip_special_tokens=True) self.queue = queue self.tokenizer = tokenizer def on_finalized_text(self, text: str, stream_end: bool = False): self.queue.put(text) if stream_end: self.queue.put(None) def generate_stream_response(messages, temperature, top_p, max_tokens): try: new_prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)[-max_tokens:] inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device) queue = Queue() streamer = CustomStreamer(tokenizer, queue) def _generate(): model.generate( inputs.input_ids, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_p=top_p, attention_mask=inputs.attention_mask, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, streamer=streamer ) Thread(target=_generate).start() while True: text = queue.get() if text is None: yield json.dumps({ "choices": [{ "delta": {}, "finish_reason": "stop" }] }, ensure_ascii=False) break yield json.dumps({ "choices": [{"delta": {"content": text}}] }, ensure_ascii=False) except Exception as e: yield json.dumps({"error": str(e)}) @app.post("/v1/chat/completions") async def chat_completions(request: ChatRequest): try: if request.stream: return StreamingResponse( (f"data: {chunk}\n\n" for chunk in generate_stream_response( messages=request.messages, temperature=request.temperature, top_p=request.top_p, max_tokens=request.max_tokens )), media_type="text/event-stream" ) else: new_prompt = tokenizer.apply_chat_template( request.messages, tokenize=False, add_generation_prompt=True )[-request.max_tokens:] inputs = tokenizer(new_prompt, return_tensors="pt", truncation=True).to(device) with torch.no_grad(): generated_ids = model.generate( inputs["input_ids"], max_length=inputs["input_ids"].shape[1] + request.max_tokens, do_sample=True, attention_mask=inputs["attention_mask"], pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, top_p=request.top_p, temperature=request.temperature ) answer = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) return { "id": f"chatcmpl-{int(time.time())}", "object": "chat.completion", "created": int(time.time()), "model": "minimind", "choices": [ { "index": 0, "message": {"role": "assistant", "content": answer}, "finish_reason": "stop" } ] } except Exception as e: raise HTTPException(status_code=500, detail=str(e)) if __name__ == "__main__": parser = argparse.ArgumentParser(description="Server for MiniMind") parser.add_argument('--out_dir', default='out', type=str) parser.add_argument('--lora_name', default='None', type=str) parser.add_argument('--hidden_size', default=768, type=int) parser.add_argument('--num_hidden_layers', default=16, type=int) parser.add_argument('--max_seq_len', default=8192, type=int) parser.add_argument('--use_moe', default=False, type=bool) parser.add_argument('--load', default=0, type=int, help="0: 从原生torch权重，1: 利用transformers加载") parser.add_argument('--model_mode', default=1, type=int, help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型") device = 'cuda' if torch.cuda.is_available() else 'cpu' model, tokenizer = init_model(parser.parse_args()) uvicorn.run(app, host="0.0.0.0", port=8998) middle25projects/2024103876/src/scripts/scripts/train_tokenizer.py000066400000000000000000000114031506644026200250470ustar00rootroot00000000000000import random import json from tokenizers import ( decoders, models, pre_tokenizers, trainers, Tokenizer, ) import os random.seed(42) def train_tokenizer(): # 读取JSONL文件并提取文本数据 def read_texts_from_jsonl(file_path): with open(file_path, 'r', encoding='utf-8') as f: for line in f: data = json.loads(line) yield data['text'] data_path = '../dataset/pretrain_hq.jsonl' # 初始化tokenizer tokenizer = Tokenizer(models.BPE()) tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False) # 定义特殊token special_tokens = ["<|endoftext|>", "<|im_start|>", "<|im_end|>"] # 设置训练器并添加特殊token trainer = trainers.BpeTrainer( vocab_size=6400, special_tokens=special_tokens, # 确保这三个token被包含 show_progress=True, initial_alphabet=pre_tokenizers.ByteLevel.alphabet() ) # 读取文本数据 texts = read_texts_from_jsonl(data_path) # 训练tokenizer tokenizer.train_from_iterator(texts, trainer=trainer) # 设置解码器 tokenizer.decoder = decoders.ByteLevel() # 检查特殊token的索引 assert tokenizer.token_to_id("<|endoftext|>") == 0 assert tokenizer.token_to_id("<|im_start|>") == 1 assert tokenizer.token_to_id("<|im_end|>") == 2 # 保存tokenizer tokenizer_dir = "../model/" os.makedirs(tokenizer_dir, exist_ok=True) tokenizer.save(os.path.join(tokenizer_dir, "tokenizer.json")) tokenizer.model.save("../model/") # 手动创建配置文件 config = { "add_bos_token": False, "add_eos_token": False, "add_prefix_space": False, "added_tokens_decoder": { "0": { "content": "<|endoftext|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True }, "1": { "content": "<|im_start|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True }, "2": { "content": "<|im_end|>", "lstrip": False, "normalized": False, "rstrip": False, "single_word": False, "special": True } }, "additional_special_tokens": [], "bos_token": "<|im_start|>", "clean_up_tokenization_spaces": False, "eos_token": "<|im_end|>", "legacy": True, "model_max_length": 32768, "pad_token": "<|endoftext|>", "sp_model_kwargs": {}, "spaces_between_special_tokens": False, "tokenizer_class": "PreTrainedTokenizerFast", "unk_token": "<|endoftext|>", "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{{ '<|im_start|>system\\n' + system_message + '<|im_end|>\\n' }}{% else %}{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\\n' + content + '<|im_end|>\\n<|im_start|>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\\n' }}{% endif %}{% endfor %}" } # 保存配置文件 with open(os.path.join(tokenizer_dir, "tokenizer_config.json"), "w", encoding="utf-8") as config_file: json.dump(config, config_file, ensure_ascii=False, indent=4) print("Tokenizer training completed and saved.") def eval_tokenizer(): from transformers import AutoTokenizer # 加载预训练的tokenizer tokenizer = AutoTokenizer.from_pretrained("../model/") messages = [ {"role": "system", "content": "你是一个优秀的聊天机器人，总是给我正确的回应！"}, {"role": "user", "content": '你来自哪里？'}, {"role": "assistant", "content": '我来自地球'} ] new_prompt = tokenizer.apply_chat_template( messages, tokenize=False ) print(new_prompt) # 获取实际词汇表长度（包括特殊符号） actual_vocab_size = len(tokenizer) print('tokenizer实际词表长度：', actual_vocab_size) model_inputs = tokenizer(new_prompt) print('encoder长度：', len(model_inputs['input_ids'])) input_ids = model_inputs['input_ids'] response = tokenizer.decode(input_ids, skip_special_tokens=False) print('decoder和原始文本是否一致：', response == new_prompt) def main(): train_tokenizer() eval_tokenizer() if __name__ == '__main__': main() middle25projects/2024103876/src/scripts/scripts/web_demo.py000066400000000000000000000336511506644026200234320ustar00rootroot00000000000000import random import re from threading import Thread import torch import numpy as np import streamlit as st st.set_page_config(page_title="MiniMind", initial_sidebar_state="collapsed") st.markdown(""" """, unsafe_allow_html=True) system_prompt = [] device = "cuda" if torch.cuda.is_available() else "cpu" def process_assistant_content(content): if model_source == "API" and 'R1' not in api_model_name: return content if model_source != "API" and 'R1' not in MODEL_PATHS[selected_model][1]: return content if '' in content and '' in content: content = re.sub(r'()(.*?)()', r'