黑马 -AI大模型三期（无秘）

编辑点评

系统学习大模型技术，从基础到应用，适合AI领域初学者和进阶者。

⭐ 编辑推荐

全面覆盖大模型基础知识，深入浅出讲解Python语言在AI领域的应用，助你成为大模型技术专家。

✨ 课程亮点

课程目录

📁 01阶段：大模型入门
📁 day08-大模型前置知识
1-8 大模型前置知识_3_ev.mp4 [81.9 MB]
1-8 大模型前置知识_5_ev.mp4 [82.3 MB]
1-8 大模型前置知识_11_ev.mp4 [63.1 MB]
1-8 大模型前置知识_10_ev.mp4 [89.5 MB]
1-8 大模型前置知识_6_ev.mp4 [83.3 MB]
1-8 大模型前置知识_4_ev.mp4 [85.9 MB]
1-8 大模型前置知识_2_ev.mp4 [69.1 MB]
1-8 大模型前置知识_9_ev.mp4 [63.0 MB]
1-8 大模型前置知识_1_ev.mp4 [45.6 MB]
1-8 大模型前置知识_8_ev.mp4 [67.4 MB]
1-8 大模型前置知识_7_ev.mp4 [93.1 MB]
📁 day03-大模型必备Python语言
1-3 大模型必备Python语言_6_ev.mp4 [102.3 MB]
1-3 大模型必备Python语言_7_ev.mp4 [117.8 MB]
1-3 大模型必备Python语言_9_ev.mp4 [85.4 MB]
1-3 大模型必备Python语言_8_ev.mp4 [99.1 MB]
1-3 大模型必备Python语言_3_ev.mp4 [90.0 MB]
1-3 大模型必备Python语言_2_ev.mp4 [87.9 MB]
1-3 大模型必备Python语言_4_ev.mp4 [99.6 MB]
1-3 大模型必备Python语言_10_ev.mp4 [111.1 MB]
1-3 大模型必备Python语言_11_ev.mp4 [65.7 MB]
1-3 大模型必备Python语言_5_ev.mp4 [105.4 MB]
1-3 大模型必备Python语言_1_ev.mp4 [74.9 MB]
📁 day02-大模型必备Python语言
1-2 大模型必备Python语言_7_ev.mp4 [90.1 MB]
1-2 大模型必备Python语言_1_ev.mp4 [34.5 MB]
1-2 大模型必备Python语言_4_ev.mp4 [76.9 MB]
1-2 大模型必备Python语言_9_ev.mp4 [75.5 MB]
1-2 大模型必备Python语言_3_ev.mp4 [69.0 MB]
1-2 大模型必备Python语言_6_ev.mp4 [74.8 MB]
1-2 大模型必备Python语言_8_ev.mp4 [90.2 MB]
1-2 大模型必备Python语言_5_ev.mp4 [75.9 MB]
1-2 大模型必备Python语言_2_ev.mp4 [76.8 MB]
📁 day01-大模型必备Python语言
1-1 大模型必备Python语言_6_ev.mp4 [63.0 MB]
1-1 大模型必备Python语言_1_ev.mp4 [40.3 MB]
1-1 大模型必备Python语言_8_ev.mp4 [78.5 MB]
1-1 大模型必备Python语言_7_ev.mp4 [76.9 MB]
1-1 大模型必备Python语言_5_ev.mp4 [54.3 MB]
1-1 大模型必备Python语言_4_ev.mp4 [68.9 MB]
1-1 大模型必备Python语言_2_ev.mp4 [70.1 MB]
1-1 大模型必备Python语言_3_ev.mp4 [72.3 MB]
1-1 大模型必备Python语言_9_ev.mp4 [54.8 MB]
📁 day04-大模型必备Python语言
1-4 大模型必备Python语言_5_ev.mp4 [101.8 MB]
1-4 大模型必备Python语言_4_ev.mp4 [107.8 MB]
1-4 大模型必备Python语言_7_ev.mp4 [64.1 MB]
1-4 大模型必备Python语言_6_ev.mp4 [141.0 MB]
1-4 大模型必备Python语言_1_ev.mp4 [106.9 MB]
1-4 大模型必备Python语言_3_ev.mp4 [97.5 MB]
1-4 大模型必备Python语言_2_ev.mp4 [118.0 MB]
📁 day05-大模型前置知识
1-5 大模型前置知识_5_ev.mp4 [134.4 MB]
1-5 大模型前置知识_9_ev.mp4 [101.7 MB]
1-5 大模型前置知识_8_ev.mp4 [119.4 MB]
1-5 大模型前置知识_2_ev.mp4 [99.1 MB]
1-5 大模型前置知识_7_ev.mp4 [126.7 MB]
1-5 大模型前置知识_1_ev.mp4 [46.1 MB]
1-5 大模型前置知识_10_ev.mp4 [103.9 MB]
1-5 大模型前置知识_3_ev.mp4 [89.3 MB]
1-5 大模型前置知识_6_ev.mp4 [74.1 MB]
1-5 大模型前置知识_4_ev.mp4 [95.3 MB]
📁 day06-大模型前置知识
1-6 大模型前置知识_4_ev.mp4 [90.9 MB]
1-6 大模型前置知识_7_ev.mp4 [88.9 MB]
1-6 大模型前置知识_1_ev.mp4 [51.6 MB]
1-6 大模型前置知识_8_ev.mp4 [67.7 MB]
1-6 大模型前置知识_9_ev.mp4 [78.9 MB]
1-6 大模型前置知识_10_ev.mp4 [60.5 MB]
1-6 大模型前置知识_2_ev.mp4 [74.1 MB]
1-6 大模型前置知识_3_ev.mp4 [61.8 MB]
1-6 大模型前置知识_5_ev.mp4 [64.7 MB]
1-6 大模型前置知识_6_ev.mp4 [56.4 MB]
1-6 大模型前置知识_11_ev.mp4 [27.2 MB]
📁 day07-大模型前置知识
1-7 大模型前置知识_3_ev.mp4 [97.0 MB]
1-7 大模型前置知识_9_ev.mp4 [65.4 MB]
1-7 大模型前置知识_8_ev.mp4 [99.7 MB]
1-7 大模型前置知识_6_ev.mp4 [94.1 MB]
1-7 大模型前置知识_7_ev.mp4 [108.7 MB]
1-7 大模型前置知识_4_ev.mp4 [94.4 MB]
1-7 大模型前置知识_2_ev.mp4 [71.0 MB]
1-7 大模型前置知识_5_ev.mp4 [92.1 MB]
1-7 大模型前置知识_1_ev.mp4 [67.8 MB]
📁 06阶段：技术面试分享（赠送）
📁 day02-大模型面试指导
1-42 大模型加餐课（面试指导）_2_ev.mp4 [124.0 MB]
1-42 大模型加餐课（面试指导）_3_ev.mp4 [169.8 MB]
1-42 大模型加餐课（面试指导）_1_ev.mp4 [156.4 MB]
1-42 大模型加餐课（面试指导）_4_ev.mp4 [174.0 MB]
1-42 大模型加餐课（面试指导）_ev.mp4 [624.4 MB]
📁 day01-综合项目与项目路演
day05-综合项目与项目路演0_ev.mp4 [43.2 MB]
day05-综合项目与项目路演1_ev.mp4 [68.6 MB]
day05-综合项目与项目路演2_ev.mp4 [36.7 MB]
day05-综合项目与项目路演3_ev.mp4 [54.2 MB]
📁 day03-大模型加餐课
大模型加餐课（模型部署）_10_ev.mp4 [45.6 MB]
大模型加餐课（模型部署）_09_ev.mp4 [73.9 MB]
大模型加餐课（模型部署）_07_ev.mp4 [85.1 MB]
大模型加餐课（模型部署）_08_ev.mp4 [86.6 MB]
大模型加餐课（模型部署）_02_ev.mp4 [96.0 MB]
大模型加餐课（模型部署）_05_ev.mp4 [81.8 MB]
大模型加餐课（模型部署）_03_ev.mp4 [116.3 MB]
大模型加餐课（模型部署）_06_ev.mp4 [75.8 MB]
大模型加餐课（模型部署）_04_ev.mp4 [85.3 MB]
大模型加餐课（模型部署）_01_ev.mp4 [38.3 MB]
📁 3期AI大模型配套资料
📁 01阶段：配套资料
📁 08
📁 01-讲义
02-ChatGPT模型原理介绍.pdf [14.3 MB]
01-LLM主要架构介绍.pdf [7.7 MB]
📁 06
📁 软件
yuan-live Setup 2.6.2.exe [123.9 MB]
StreamingTool-7.6.2-x64.exe [355.0 MB]
大模型应用工具实战02.pdf [7.7 MB]
作业.txt [264.0 B]
📁 04
📁 03-代码
📁 02-神经网络
📁 dataset
phone.pth [145.3 KB]
手机价格预测.csv [119.5 KB]
📁 model
phone.pth [145.8 KB]
02-激活函数-tanh.py [460.0 B]
05-参数初始化.py [1.5 KB]
12-案例-价格分类.py [4.4 KB]
06-搭建神经网络.py [1.9 KB]
01-激活函数-sigmoid.py [588.0 B]
13-Transformer汉译英.py [367.0 B]
07-损失函数.py [2.6 KB]
10-学习率衰减方法.py [3.8 KB]
03-激活函数-ReLU.py [463.0 B]
11-正则化.py [459.0 B]
09-梯度下降优化方法.py [4.6 KB]
08-反向传播BP算法.py [1.7 KB]
04-激活函数-Softmax.py [187.0 B]
📁 04-循环神经网络
📁 data
jaychou_lyrics.txt [167.2 KB]
lyrics_model_10.pth [5.7 MB]
02-RNN层的使用.py [639.0 B]
03-RNN实现周杰伦歌词生成.py [6.7 KB]
lyrics_model_10.pth [5.7 MB]
01-词嵌入层API.py [896.0 B]
📁 03-卷积神经网络
📁 data
📁 cifar-10-batches-py
batches.meta [158.0 B]
📁 cifar-10-batches-py
data_batch_2 [29.6 MB]
data_batch_1 [29.6 MB]
data_batch_3 [29.6 MB]
data_batch_5 [29.6 MB]
test_batch [29.6 MB]
data_batch_4 [29.6 MB]
readme.html [88.0 B]
image_classification.pth [320.9 KB]
img.jpg [44.4 KB]
04-案例-卷积神经网络实现图像分类.py [4.4 KB]
02-pytorch卷积层API.py [872.0 B]
03-pytorch池化API.py [1.0 KB]
01-matplotlib图像加载.py [623.0 B]
📁 01-讲义
04-卷积神经网络.pdf [1.2 MB]
05-循环神经网络.pdf [933.5 KB]
02-神经网络基础.pdf [2.1 MB]
03-Transformer详解.pdf [3.7 MB]
📁 02-笔记
深度学习基础0601.pdf [3.3 MB]
📁 01
📁 01-讲义
Python入门教程.pdf [1.9 MB]
📁 02-软件
📁 Anaconda
Anaconda3-2023.09-0-Windows-x86_64.exe [1.0 GB]
📁 PyCharm
pycharm-professional-2021.2.1.exe [463.6 MB]
📁 03-代码
📁 【5月26日】代码
24-Python中使用__str__()魔术方法.py [762.0 B]
13-Python中默认值参数.py [578.0 B]
34-Python中的继承关系（继承链）.py [621.0 B]
10-Python中的global关键字.py [501.0 B]
19-Python中类的定义与实例化.py [429.0 B]
03-Python中函数的返回值.py [614.0 B]
28-Python中私有方法.py [560.0 B]
17-Python中的匿名函数.py [760.0 B]
21-Python中成员属性的定义.py [590.0 B]
32-Python中的多继承.py [733.0 B]
16-Python中的不定长参数接收容器类型的参数.py [497.0 B]
02-Python中函数的参数.py [783.0 B]
15-Python中不定长参数混用的情况.py [364.0 B]
01-Python函数的基本概念.py [811.0 B]
09-Python中局部变量的访问范围.py [591.0 B]
11-Python中函数的两种的参数.py [458.0 B]
07-Python中变量的作用域.py [690.0 B]
06-Python中使用函数生成一个4位长度的验证码.py [1.2 KB]
22-Python中魔术方法.py [854.0 B]
26-Python中的魔术方法__call__.py [380.0 B]
23-Python中使用魔术方法实现属性的定义.py [564.0 B]
04-Python中return返回值.py [546.0 B]
25-Python中使用__del__()魔术方法.py [824.0 B]
33-Python中多继承（继承链）.py [474.0 B]
18-Python中带参数的lambda表达式.py [385.0 B]
08-Python中全局变量的访问范围.py [321.0 B]
14-Python中不定长参数.py [679.0 B]
31-Python中的super()方法.py [1.3 KB]
12-Python中函数的两种传参方式.py [576.0 B]
29-Python中继承的实现.py [712.0 B]
30-Python中的重写机制.py [1.0 KB]
05-Python中return返回值返回多个结果.py [316.0 B]
27-Python中的公有属性和私有属性.py [1.1 KB]
20-Python中对象成员方法的self关键词.py [637.0 B]
📁 【5月23日】代码
04-Python中的if...else选择结构.py [572.0 B]
10-Python中实现指定次数的循环.py [599.0 B]
14-Python中的列表容器.py [768.0 B]
02-Python中的选择结构.py [800.0 B]
09-Python中的循环结构.py [563.0 B]
15-Python中列表的其他操作.py [285.0 B]
12-Python中循环的两大关键词.py [1.2 KB]
05-Python中if...elif...else结构.py [721.0 B]
11-Python中实现求1-100累加的结果.py [314.0 B]
13-Python中猜数字游戏的开发.py [1.0 KB]
03-Python中的if...else选择结构.py [474.0 B]
19-Python中的集合类型.py [250.0 B]
18-Python中的字典类型.py [900.0 B]
08-Python中的模块.py [597.0 B]
01-Python中的编程语言的流程结构.py [253.0 B]
07-Python中猜拳游戏实现.py [1.3 KB]
16-Python中列表的切片操作（字符串元组也可以使用）.py [846.0 B]
06-Python中if嵌套结构.py [1.2 KB]
17-Python中元组的定义与使用.py [573.0 B]
📁 【5月21日】代码
11-Python中的转义字符.py [452.0 B]
05-Python中的变量命名规则.py [278.0 B]
01-Python程序入门.py [20.0 B]
10-Python中变量的格式化输出.py [580.0 B]
04-Python中变量定义.py [785.0 B]
07-Python中运算符.py [486.0 B]
09-Python中的普通输出操作.py [248.0 B]
06-Python中变量7种数据类型.py [1.0 KB]
03-Python中的多行注释.py [154.0 B]
08-Python中的输入操作.py [709.0 B]
02-Python中的单行注释.py [240.0 B]
📁 03
📁 02-笔记
深度学习基础0530.pdf [898.7 KB]
📁 01-讲义
03-Transformer详解.pdf [3.7 MB]
02-神经网络基础.pdf [2.1 MB]
📁 04-拓展
拓展3_Pycharm配置Anaconda环境.pdf [658.6 KB]
📁 03-代码
📁 03-卷积神经网络
📁 data
📁 cifar-10-batches-py
data_batch_2 [29.6 MB]
batches.meta [158.0 B]
data_batch_4 [29.6 MB]
data_batch_3 [29.6 MB]
test_batch [29.6 MB]
data_batch_5 [29.6 MB]
data_batch_1 [29.6 MB]
readme.html [88.0 B]
image_classification.pth [320.9 KB]
img.jpg [44.4 KB]
04-案例-卷积神经网络实现图像分类.py [4.4 KB]
02-pytorch卷积层API.py [872.0 B]
03-pytorch池化API.py [1.0 KB]
01-matplotlib图像加载.py [623.0 B]
📁 02-神经网络
📁 model
phone.pth [145.8 KB]
📁 dataset
phone.pth [145.3 KB]
手机价格预测.csv [119.5 KB]
10-学习率衰减方法.py [3.8 KB]
11-正则化.py [459.0 B]
07-损失函数.py [2.6 KB]
06-搭建神经网络.py [1.9 KB]
12-案例-价格分类.py [4.4 KB]
02-激活函数-tanh.py [460.0 B]
01-激活函数-sigmoid.py [588.0 B]
05-参数初始化.py [1.5 KB]
08-反向传播BP算法.py [1.7 KB]
09-梯度下降优化方法.py [4.6 KB]
04-激活函数-Softmax.py [187.0 B]
03-激活函数-ReLU.py [463.0 B]
13-Transformer汉译英.py [367.0 B]
📁 04-循环神经网络
📁 data
jaychou_lyrics.txt [167.2 KB]
lyrics_model_10.pth [5.7 MB]
03-RNN实现周杰伦歌词生成.py [6.7 KB]
01-词嵌入层API.py [896.0 B]
02-RNN层的使用.py [639.0 B]
📁 09
📁 01-讲义
01-LLM主流开源大模型介绍.pdf [11.4 MB]
开源的LLM.pdf [32.6 KB]
📁 05
📁 软件
VSCodeUserSetup-x64-1.89.1.exe [94.9 MB]
📁 讲义
大模型应用工具实战01.pdf [6.4 MB]
📁 07
📁 02-代码
02-rouge.py [178.0 B]
03-PPL.py [365.0 B]
01-bleu.py [545.0 B]
📁 01-讲义
02-LLM主要架构介绍.pdf [7.7 MB]
01-LLM基础知识.pdf [11.4 MB]
大模型项目研发流程.pdf [279.6 KB]
LLM背景介绍.pdf [42.7 KB]
📁 02
📁 01-讲义
00-深度学习简介.pdf [631.0 KB]
01-PyTorch基本使用.pdf [1.1 MB]
📁 04-拓展
拓展1_深度学习拓展.pdf [1.4 MB]
拓展2_Pytorch-CUDA环境配置.pdf [485.3 KB]
📁 02-笔记
深度学习基础.pdf [310.7 KB]
📁 03-代码
📁 02-神经网络
📁 model
phone.pth [145.8 KB]
📁 dataset
phone.pth [145.3 KB]
手机价格预测.csv [119.5 KB]
10-学习率衰减方法.py [3.8 KB]
06-搭建神经网络.py [1.9 KB]
09-梯度下降优化方法.py [3.9 KB]
08-反向传播BP算法.py [1.7 KB]
03-激活函数-ReLU.py [463.0 B]
04-激活函数-Softmax.py [187.0 B]
11-正则化.py [459.0 B]
05-参数初始化.py [1.5 KB]
02-激活函数-tanh.py [460.0 B]
01-激活函数-sigmoid.py [588.0 B]
12-案例-价格分类.py [4.4 KB]
07-损失函数.py [2.6 KB]
13-Transformer汉译英.py [367.0 B]
📁 01-Pytroch基本使用
02-张量类型转换.py [561.0 B]
06-张量的形状操作.py [712.0 B]
07-张量的拼接.py [205.0 B]
01-张量创建.py [849.0 B]
08-案例-线性回归模型构建.py [3.1 KB]
04-张量的运算函数.py [412.0 B]
05-张量的索引操作.py [406.0 B]
03-张量的数值计算.py [276.0 B]
📁 04阶段：配套资料
📁 10月19号
📁 01-讲义
新媒体行业评论智能分类与信息抽取系统.pdf [722.3 KB]
📁 02-代码
📁 chatglm-6b
config.json [773.0 B]
pytorch_model-00008-of-00008.bin [1019.8 MB]
pytorch_model.bin.index.json [32.6 KB]
tokenization_chatglm.py [16.6 KB]
pytorch_model-00004-of-00008.bin [1.8 GB]
pytorch_model-00002-of-00008(1).bin [1.8 GB]
test_modeling_chatglm.py [13.5 KB]
configuration_chatglm.py [4.2 KB]
pytorch_model-00007-of-00008.bin [1.0 GB]
pytorch_model-00002-of-00008.bin [1.8 GB]
pytorch_model-00003-of-00008.bin [1.8 GB]
pytorch_model-00006-of-00008.bin [1.8 GB]
ice_text.model [2.6 MB]
quantization.py [14.7 KB]
MODEL_LICENSE [4.2 KB]
README.md [6.7 KB]
LICENSE [11.1 KB]
tokenizer_config.json [441.0 B]
modeling_chatglm.py [56.3 KB]
pytorch_model-00005-of-00008.bin [1.8 GB]
📁 ptune_chatglm
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
modules.xml [285.0 B]
.gitignore [184.0 B]
ptune_chatglm.iml [480.0 B]
misc.xml [268.0 B]
workspace.xml [7.9 KB]
📁 checkpoints
📁 ptune
📁 __pycache__
glm_config.cpython-312.pyc [2.1 KB]
📁 data_handle
📁 __pycache__
data_loader.cpython-312.pyc [2.0 KB]
data_preprocess.cpython-312.pyc [5.7 KB]
__init__.cpython-312.pyc [274.0 B]
data_preprocess.py [6.2 KB]
data_loader.py [1.5 KB]
__init__.py
📁 data
dataset.jsonl [4.4 KB]
mixed_train_dataset.jsonl [496.8 KB]
mixed_dev_dataset.jsonl [64.9 KB]
📁 utils
📁 __pycache__
common_utils.cpython-312.pyc [1.8 KB]
__init__.cpython-312.pyc [268.0 B]
common_utils.py [966.0 B]
__init__.py
inference.py [2.7 KB]
train.py [7.0 KB]
__init__.py [22.0 B]
glm_config.py [1.4 KB]
📁 10月14号
📁 01-讲义
02-大模型prompt-Tuning方法进阶.pdf [1.5 MB]
📁 10月26号
📁 02-代码
📁 01-讲义
02-Assistant API的原理及应用.pdf [579.6 KB]
01-LLM基础知识.pdf [1.2 MB]
01-GPTs的介绍及应用.pdf [727.2 KB]
📁 10月15号
📁 02-代码
📁 Gpt2_Chatbot
📁 templates
index1.html [1.9 KB]
index.html [694.0 B]
📁 other_data
闲聊语料.txt [65.0 MB]
闲聊语料.pkl [68.8 MB]
📁 save_model
📁 epoch97
config.json [838.0 B]
pytorch_model.bin [378.5 MB]
📁 data_preprocess
dataset.py [2.1 KB]
preprocess.py [3.9 KB]
__init__.py [70.0 B]
dataloader.py [4.4 KB]
📁 save_model1
📁 min_ppl_model_bj
generation_config.json [119.0 B]
model.safetensors [366.5 MB]
config.json [977.0 B]
📁 gpt2
generation_config.json [130.0 B]
merges.txt [494.5 KB]
README.md [8.1 KB]
tokenizer.json [1.3 MB]
vocab.json [1017.9 KB]
📁 vocab
vocab2.txt [127.6 KB]
vocab.txt [74.3 KB]
📁 config
config.json [875.0 B]
📁 data
medical_train.pkl [9.8 MB]
medical_train.txt [9.5 MB]
medical_valid.txt [130.8 KB]
medical_valid.pkl [134.3 KB]
train.py [11.4 KB]
app.py [487.0 B]
flask_predict.py [2.6 KB]
parameter_config.py [2.6 KB]
readme [1.9 KB]
interact.py [5.5 KB]
functions_tools.py [3.3 KB]
__init__.py [72.0 B]
📁 预训练模型
📁 bert-base-chinese
pytorch_model.bin [392.5 MB]
tokenizer.json [262.6 KB]
flax_model.msgpack [390.2 MB]
config.json [624.0 B]
tokenizer_config.json [29.0 B]
README.md [21.0 B]
vocab.txt [107.0 KB]
📁 PET
📁 checkpoints
📁 model_best_old
tokenizer_config.json [1.2 KB]
tokenizer.json [428.8 KB]
generation_config.json [90.0 B]
config.json [866.0 B]
pytorch_model.bin [390.3 MB]
special_tokens_map.json [125.0 B]
vocab.txt [107.0 KB]
model.safetensors [390.2 MB]
📁 data
train.txt [9.6 KB]
dev.txt [98.7 KB]
verbalizer.txt [139.0 B]
prompt.txt [37.0 B]
📁 utils
📁 __pycache__
metirc_utils.cpython-312.pyc [6.1 KB]
verbalizer.cpython-312.pyc [9.2 KB]
__init__.cpython-312.pyc [245.0 B]
common_utils.cpython-312.pyc [3.9 KB]
__init__.py
verbalizer.py [8.0 KB]
metirc_utils.py [4.7 KB]
common_utils.py [5.1 KB]
📁 __pycache__
pet_config.cpython-312.pyc [2.3 KB]
📁 data_handle
📁 __pycache__
data_preprocess.cpython-312.pyc [4.7 KB]
template.cpython-312.pyc [5.8 KB]
data_loader.cpython-312.pyc [2.2 KB]
__init__.cpython-312.pyc [251.0 B]
template.py [5.0 KB]
data_preprocess.py [5.5 KB]
data_loader.py [1.9 KB]
__init__.py
train.py [7.8 KB]
pet_config.py [1.6 KB]
__init__.py
inference.py [3.9 KB]
📁 01-讲义
02-基于BERT+PET方式文本分类介绍.pdf [435.6 KB]
03-基于BERT+PET方式数据预处理介绍.pdf [387.3 KB]
01-项目背景介绍.pdf [1.6 MB]
📁 10月24号
📁 02-代码（同22号）
📁 01-讲义（同22号）
📁 10月17号
📁 02-代码
📁 P-Tuning
📁 data
verbalizer.txt [139.0 B]
train.txt [9.6 KB]
dev.txt [70.2 KB]
📁 utils
📁 __pycache__
verbalizer.cpython-312.pyc [9.3 KB]
metirc_utils.cpython-312.pyc [6.3 KB]
__init__.cpython-312.pyc [250.0 B]
common_utils.cpython-312.pyc [4.0 KB]
verbalizer.py [7.6 KB]
metirc_utils.py [4.6 KB]
common_utils.py [4.0 KB]
__init__.py
📁 __pycache__
ptune_config.cpython-312.pyc [2.2 KB]
📁 checkpoints
📁 model_20
generation_config.json [95.0 B]
config.json [1.1 KB]
model.safetensors [340.6 MB]
📁 model_old_best
tokenizer_config.json [338.0 B]
model.safetensors [390.2 MB]
special_tokens_map.json [125.0 B]
config.json [867.0 B]
vocab.txt [107.0 KB]
tokenizer.json [428.8 KB]
generation_config.json [90.0 B]
pytorch_model.bin [390.3 MB]
📁 data_handle
📁 __pycache__
data_loader.cpython-312.pyc [2.1 KB]
data_preprocess.cpython-312.pyc [5.0 KB]
__init__.cpython-312.pyc [256.0 B]
data_loader.py [1.6 KB]
data_preprocess.py [6.3 KB]
__init__.py
ptune_config.py [1.5 KB]
__init__.py
inference.py [3.2 KB]
train.py [7.5 KB]
📁 01-讲义
06-基于BERT+P-Tuning方式数据预处理介绍.pdf [390.3 KB]
07-基于BERT+P-Tuning方式文本分类模型搭建.pdf [427.3 KB]
📁 10月22号
📁 01-讲义
新媒体行业评论智能分类与信息抽取系统.pdf [722.3 KB]
📁 02-代码
📁 chatglm-6b
pytorch_model-00008-of-00008.bin [1019.8 MB]
configuration_chatglm.py [4.2 KB]
pytorch_model-00001-of-00008.bin [1.6 GB]
pytorch_model-00004-of-00008.bin [1.8 GB]
README.md [6.7 KB]
ice_text.model [2.6 MB]
quantization.py [14.7 KB]
tokenizer_config.json [441.0 B]
LICENSE [11.1 KB]
config.json [773.0 B]
pytorch_model-00006-of-00008.bin [1.8 GB]
tokenization_chatglm.py [16.6 KB]
pytorch_model.bin.index.json [32.6 KB]
test_modeling_chatglm.py [13.5 KB]
pytorch_model-00003-of-00008.bin [1.8 GB]
pytorch_model-00007-of-00008.bin [1.0 GB]
MODEL_LICENSE [4.2 KB]
pytorch_model-00005-of-00008.bin [1.8 GB]
modeling_chatglm.py [56.3 KB]
📁 ptune_chatglm
📁 data
mixed_train_dataset.jsonl [496.8 KB]
mixed_dev_dataset.jsonl [64.9 KB]
dataset.jsonl [4.4 KB]
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
ptune_chatglm.iml [480.0 B]
misc.xml [268.0 B]
workspace.xml [7.9 KB]
.gitignore [184.0 B]
modules.xml [285.0 B]
📁 __pycache__
glm_config.cpython-312.pyc [2.1 KB]
📁 checkpoints
📁 ptune
📁 utils
📁 __pycache__
common_utils.cpython-312.pyc [1.8 KB]
__init__.cpython-312.pyc [268.0 B]
__init__.py
common_utils.py [966.0 B]
📁 data_handle
📁 __pycache__
__init__.cpython-312.pyc [274.0 B]
data_preprocess.cpython-312.pyc [5.7 KB]
data_loader.cpython-312.pyc [2.0 KB]
data_preprocess.py [6.2 KB]
__init__.py
data_loader.py [1.5 KB]
inference.py [2.7 KB]
__init__.py [22.0 B]
glm_config.py [1.4 KB]
train.py [7.0 KB]
📁 10月21号
📁 02-代码
📁 P-Tuning
📁 data_handle
📁 __pycache__
data_preprocess.cpython-312.pyc [5.0 KB]
data_loader.cpython-312.pyc [2.1 KB]
__init__.cpython-312.pyc [256.0 B]
data_loader.py [1.6 KB]
data_preprocess.py [6.3 KB]
__init__.py
📁 utils
📁 __pycache__
metirc_utils.cpython-312.pyc [6.3 KB]
verbalizer.cpython-312.pyc [9.3 KB]
common_utils.cpython-312.pyc [4.0 KB]
__init__.cpython-312.pyc [250.0 B]
metirc_utils.py [4.6 KB]
__init__.py
verbalizer.py [7.6 KB]
common_utils.py [4.0 KB]
📁 data
verbalizer.txt [139.0 B]
train.txt [9.6 KB]
dev.txt [70.2 KB]
📁 __pycache__
ptune_config.cpython-312.pyc [2.2 KB]
📁 checkpoints
📁 model_old_best
pytorch_model.bin [390.3 MB]
generation_config.json [90.0 B]
config.json [867.0 B]
model.safetensors [390.2 MB]
tokenizer_config.json [338.0 B]
tokenizer.json [428.8 KB]
vocab.txt [107.0 KB]
special_tokens_map.json [125.0 B]
📁 model_20
model.safetensors [340.6 MB]
config.json [1.1 KB]
generation_config.json [95.0 B]
ptune_config.py [1.5 KB]
__init__.py
inference.py [3.2 KB]
train.py [7.5 KB]
📁 01-讲义
07-基于BERT+P-Tuning方式文本分类模型搭建.pdf [427.3 KB]
06-基于BERT+P-Tuning方式数据预处理介绍.pdf [390.3 KB]
📁 06阶段：配套资料
简历优化及面试注意事项.txt [740.0 B]
大模型训练营-大模型时代 .pdf [2.9 MB]
论文导读.zip [54.9 MB]
大模型训练营—简历优化 .pdf [680.0 KB]
人工智能-求职自我介绍以及项目描述参考模板.docx [20.8 KB]
📁 05阶段：配套资料
📁 项目资料
📁 01-讲义
03-stableDiffusion详解.pdf [4.9 MB]
04-StableDiffusion实践.pdf [2.3 MB]
05-腾讯云AI绘画.pdf [13.5 MB]
📁 02-代码
📁 img_Plaidshirtprogrammer
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📁 img-glasses
00072-2356360199.txt [24.0 B]
00077-2356360204.txt [24.0 B]
00065-4096775221.txt [24.0 B]
00063-4096775219.png [372.7 KB]
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00071-2356360198.txt [24.0 B]
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00079-2356360206.txt [24.0 B]
00076-2356360203.txt [24.0 B]
00083-2356360210.txt [24.0 B]
00084-2356360211.txt [24.0 B]
00083-2356360210.png [329.9 KB]
00082-2356360209.txt [24.0 B]
00070-2356360197.txt [24.0 B]
00079-2356360206.png [313.7 KB]
00064-4096775220.txt [24.0 B]
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00081-2356360208.txt [24.0 B]
00065-4096775221.png [378.1 KB]
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00061-4096775217.txt [24.0 B]
00077-2356360204.png [346.9 KB]
00061-4096775217.png [327.0 KB]
00062-4096775218.png [365.1 KB]
00072-2356360199.png [305.4 KB]
00063-4096775219.txt [24.0 B]
00081-2356360208.png [263.7 KB]
00086-2356360213.txt [24.0 B]
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00074-2356360201.txt [24.0 B]
📁 weights
glass.safetensors [36.1 MB]
model-plaidshirtprogrammer.ckpt [2.0 GB]
aigc_demo_origin.zip [6.4 MB]
📁 AI大模型 赠送资料
简历模板.zip [2.0 MB]
11本AI大模型相关电子书.zip [309.5 MB]
📁 02阶段：配套资料
📁 9月7号
📁 01-讲义
01-LLM主流开源大模型介绍.pdf [11.4 MB]
📁 9月5号
📁 01-讲义
02-ChatGPT模型原理介绍.pdf [14.3 MB]
01-LLM主要架构介绍.pdf [7.7 MB]
📁 9月10号
📁 01-讲义
02-大模型prompt-Tuning方法进阶.pdf [9.2 MB]
01-大模型prompt-Tuning方法入门.pdf [8.2 MB]
📁 8月31日
📁 1.讲义
大模型应用工具实战02.pptx [19.9 MB]
📁 9月12号
📁 01-讲义
01-大模型提示工程指南.pdf [1.3 MB]
📁 9月14号
📁 01-讲义
02-金融行业动态方向评估项目.pdf [583.3 KB]
03-LLM实现金融文本分类.pdf [360.8 KB]
📁 02-代码
finance_text_matching.py [3.1 KB]
finance_ie.py [4.9 KB]
finance_classify.py [4.4 KB]
📁 03-weights
📁 chatglm2-6b-int4
configuration_chatglm.py [2.2 KB]
tokenizer.model [994.5 KB]
MODEL_LICENSE [2.3 KB]
quantization.py [2.5 MB]
modeling_chatglm.py [53.6 KB]
README.md [7.5 KB]
📁 chatglm2-6b-int4
config.json [1.1 KB]
📁 chatglm2-6b-int4
tokenization_chatglm.py [9.8 KB]
📁 chatglm2-6b-int4
tokenizer_config.json [243.0 B]
📁 01-讲义_0915135902
02-金融行业动态方向评估项目.pdf [583.3 KB]
趋动云使用《补充》.pdf [3.1 MB]
1.环境要求.pdf [110.8 KB]
03-LLM实现金融文本分类.pdf [360.8 KB]
📁 9月15号
📁 01-讲义
04-LLM实现金融信息抽取.pdf [330.9 KB]
05-LLM实现金融信息匹配.pdf [303.2 KB]
📁 03-视频
01-淇℃伅鎶藉彇_ev.mp4 [57.8 MB]
02-淇℃伅鎶藉彇2_ev.mp4 [18.9 MB]
03-鏂囨湰鍖归厤_ev.mp4 [32.3 MB]
📁 02-代码在9月14号
📁 8月30日
📁 1.讲义
大模型应用工具实战01.pptx [36.3 MB]
📁 9月18号
📁 01-讲义
星火大模型（博学谷）.pdf [12.1 MB]
📁 02-代码
📁 Dataset-of-financial-news-classification
Fiance_train_data.csv [912.4 KB]
Fiance_test_data.csv [159.9 KB]
translate_in_many_style.zip [79.3 MB]
📁 9月4号
📁 02-代码
03-PPL.py [365.0 B]
02-rouge.py [178.0 B]
01-bleu.py [545.0 B]
📁 01-讲义
02-LLM主要架构介绍.pdf [7.7 MB]
01-LLM基础知识.pdf [11.4 MB]
大模型.xmind [214.3 KB]
大语言模型的背景.xmind [159.6 KB]
📁 03阶段：配套资料
📁 10月13号
📁 01-讲义
01-大模型prompt-Tuning方法入门.pdf [1.9 MB]
📁 9月24号
📁 02-code
📁 ChatGLM3_FunctionCall
📁 __pycache__
📁 sql
📁 __pycache__
sql_function_tools.cpython-310.pyc [3.1 KB]
sql_zhipu.py [2.1 KB]
sql_function_tools.py [3.9 KB]
📁 weather
📁 __pycache__
tools.cpython-38.pyc [1.6 KB]
tools.cpython-310.pyc [1.6 KB]
cityCode_use.json [117.0 B]
tools.py [2.2 KB]
weather_zhipu.py [2.2 KB]
📁 airplane
📁 __pycache__
muti_utils.cpython-38.pyc [1.1 KB]
airplane_function_tools.cpython-38.pyc [742.0 B]
airplane_function_tools.cpython-310.pyc [732.0 B]
muti_utils.cpython-310.pyc [1.1 KB]
airplane_function_tools.py [1.4 KB]
muti_function_zhipu.py [2.9 KB]
muti_utils.py [1.1 KB]
📁 01-讲义(1)
01-Function Call的原理及应用.pdf [797.7 KB]
SQL.pdf [29.0 KB]
📁 9月28日
📁 02-代码
📁 Agent_Email_Generate
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [268.0 B]
.gitignore [184.0 B]
modules.xml [299.0 B]
Agent_Email_Generate.iml [317.0 B]
workspace.xml [6.2 KB]
📁 tools
📁 __pycache__
custom_tools.cpython-310.pyc [2.0 KB]
__init__.cpython-310.pyc [170.0 B]
custom_tools.cpython-312.pyc [2.8 KB]
__init__.cpython-312.pyc [244.0 B]
custom_tools.py [2.9 KB]
__init__(1).py
__init__.py
test.py [124.0 B]
main.py [4.2 KB]
email_category.txt [943.0 B]
__init__.py
poie.txt [35.0 B]
📁 longchain
📁 Agents_module
demo_agent.py [1000.0 B]
📁 Models_module
demo_embedding_models.py [622.0 B]
demo_llms.py [313.0 B]
demo_chat_models.py [428.0 B]
📁 Memory_module
demo_memory.py [225.0 B]
demo_message_dict.py [428.0 B]
demo_up_memory.py [675.0 B]
📁 Chains_module
demo_use_simpleChain.py [1.1 KB]
demo_use_LLMChain.py [615.0 B]
📁 Indexes_module
demo_vector.py [818.0 B]
衣服属性.txt [819.0 B]
demo_retriver.py [940.0 B]
pku.txt [4.2 KB]
demo_dataloader.py [506.0 B]
demo_text_split.py [661.0 B]
📁 Prompts_module
demo_zero_shot.py [624.0 B]
demo_few_shot.py [1.3 KB]
📁 01-讲义
01-AI Agents的开发应用.pdf [1.1 MB]
01-LangChain基础知识入门.pdf [851.8 KB]
📁 10月8号
📁 01-讲义
02-基于LangChain+ChatGLM-6B实现物流行业信息咨询.pdf [554.5 KB]
📁 01-code
📁 RAG
📁 __pycache__
get_vector.cpython-311.pyc [1.4 KB]
get_vector.cpython-38.pyc [939.0 B]
get_vector.cpython-312.pyc [1.3 KB]
get_vector.cpython-310.pyc [977.0 B]
model.cpython-311.pyc [2.8 KB]
model.cpython-310.pyc [1.8 KB]
model.cpython-38.pyc [1.8 KB]
model.cpython-312.pyc [2.6 KB]
📁 faiss
📁 logistics
index.faiss [9.0 KB]
index.pkl [1.1 KB]
📁 camp
index.pkl [973.0 B]
index.faiss [6.0 KB]
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
RAG.iml [317.0 B]
workspace.xml [10.6 KB]
.gitignore [184.0 B]
modules.xml [265.0 B]
misc.xml [268.0 B]
📁 chatglm2-6b-int4
configuration_chatglm.py [2.2 KB]
tokenization_chatglm.py [9.8 KB]
tokenizer_config.json [243.0 B]
quantization.py [2.5 MB]
pytorch_model.bin [3.7 GB]
tokenizer.model [994.5 KB]
modeling_chatglm.py [53.6 KB]
MODEL_LICENSE [2.3 KB]
config.json [1.1 KB]
README.md [7.5 KB]
📁 m3e-base
📁 1_Pooling
config.json [190.0 B]
vocab.txt [107.0 KB]
gitattributes [1.5 KB]
sentence_bert_config.json [53.0 B]
modules.json [229.0 B]
model.safetensors [390.1 MB]
tokenizer_config.json [342.0 B]
tokenizer.json [428.8 KB]
config.json [932.0 B]
special_tokens_map.json [125.0 B]
README.md [26.0 KB]
pytorch_model.bin [390.2 MB]
main.py [1.5 KB]
new_demo.py [3.1 KB]
物流信息.txt [550.0 B]
test.py [33.0 B]
get_vector.py [1.4 KB]
model.py [1.5 KB]
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
.gitignore [184.0 B]
modules.xml [273.0 B]
misc.xml [268.0 B]
workspace.xml [15.5 KB]
01-code.iml [317.0 B]
📁 10月10号
📁 02-讲义
基于GPT2搭建医疗问诊机器人.pdf [612.9 KB]
📁 01-code
📁 Gpt2_Chatbot
📁 other_data
闲聊语料.txt [65.0 MB]
闲聊语料.pkl [68.8 MB]
📁 save_model1
📁 min_ppl_model_bj
generation_config.json [119.0 B]
config.json [977.0 B]
model.safetensors [366.5 MB]
📁 config
config.json [875.0 B]
📁 data_preprocess
dataloader.py [4.4 KB]
preprocess.py [3.9 KB]
dataset.py [2.1 KB]
__init__.py [70.0 B]
📁 vocab
vocab2.txt [127.6 KB]
vocab.txt [74.3 KB]
📁 save_model
📁 epoch97
pytorch_model.bin [378.5 MB]
config.json [838.0 B]
📁 templates
index.html [694.0 B]
index1.html [1.9 KB]
📁 gpt2
README.md [8.1 KB]
generation_config.json [130.0 B]
merges.txt [494.5 KB]
vocab.json [1017.9 KB]
tokenizer.json [1.3 MB]
📁 data
medical_train.txt [9.5 MB]
medical_train.pkl [9.8 MB]
medical_valid.pkl [134.3 KB]
medical_valid.txt [130.8 KB]
functions_tools.py [3.3 KB]
app.py [487.0 B]
__init__.py [72.0 B]
interact.py [5.5 KB]
parameter_config.py [2.6 KB]
flask_predict.py [2.6 KB]
train.py [11.4 KB]
readme [1.9 KB]
📁 9月26号
📁 01-讲义
01-GPTs的介绍及应用.pdf [727.2 KB]
01-LLM基础知识.pdf [1.2 MB]
02-Assistant API的原理及应用.pdf [579.6 KB]
📁 03-code
📁 MiniMax_Assistant
📁 .idea
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
MiniMax_Assistant.iml [291.0 B]
modules.xml [293.0 B]
misc.xml [310.0 B]
.gitignore [184.0 B]
workspace.xml [5.1 KB]
fruit_price.txt [250.0 B]
minmax_assistant.py [5.3 KB]
📁 9月19号
📁 02-数据
sample-text-dialog-unsort-jsonl.zip [154.3 KB]
清洗emoji数据的demo数据集.zip [219.7 KB]
📁 01-讲义
01-千帆大模型.pdf [6.3 MB]
图表分析数据.md [4.7 KB]
01-阿里百炼平台.pdf [3.7 MB]
📁 9月21号
📁 03-代码
📁 ChatGLM3_FunctionCall
📁 .idea
📁 dataSources
📁 inspectionProfiles
profiles_settings.xml [174.0 B]
ChatGLM3_FunctionCall.iml [478.0 B]
modules.xml [301.0 B]
workspace.xml [11.7 KB]
.gitignore [184.0 B]
dataSources.xml [530.0 B]
dataSources.local.xml [486.0 B]
misc.xml [268.0 B]
📁 sql
📁 __pycache__
sql_function_tools.cpython-312.pyc [3.9 KB]
sql_function_tools.cpython-310.pyc [3.1 KB]
sql_zhipu.py [2.1 KB]
sql_function_tools.py [3.9 KB]
📁 weather
📁 __pycache__
tools.cpython-310.pyc [1.6 KB]
tools.cpython-312.pyc [2.5 KB]
tools.cpython-38.pyc [1.6 KB]
cityCode_use.json [117.0 B]
weather_zhipu.py [2.2 KB]
tools.py [2.3 KB]
📁 airplane
📁 __pycache__
airplane_function_tools.cpython-38.pyc [742.0 B]
muti_utils.cpython-312.pyc [1.8 KB]
airplane_function_tools.cpython-312.pyc [885.0 B]
airplane_function_tools.cpython-310.pyc [732.0 B]
muti_utils.cpython-310.pyc [1.1 KB]
muti_utils.cpython-38.pyc [1.1 KB]
muti_utils.py [1.1 KB]
airplane_function_tools.py [1.4 KB]
muti_function_zhipu.py [3.0 KB]
📁 __pycache__
📁 01-讲义
02-阿里PAI平台.pdf [2.8 MB]
06-资源清理.pdf [1.5 MB]
PAI平台开通指南.pdf [3.8 MB]
01-Function Call的原理及简单应用.pdf [2.2 MB]
01-虚拟试衣背景.pdf [1.8 MB]
05-虚拟试衣实践.pdf [5.2 MB]
03-阿里云注册及开通PAI.pdf [2.0 MB]
04-PAI_DSW的环境搭建.pdf [2.0 MB]
📁 04阶段：⼤模型⾼级项目开发
📁 day06 【项目】新零售行业评价决策系统
02-主标签找子标签_ev.mp4 [66.9 MB]
03-子标签找主标签_ev.mp4 [26.0 MB]
06-璇勪环鎸囨爣_ev.mp4 [21.9 MB]
📁 day04【项目】新零售行业评价决策系统
01-模型结构_ev.mp4 [49.5 MB]
03-模型训练过程_ev.mp4 [121.6 MB]
📁 day02【项目】大健康行业智能问诊系统
01-涓婁笅鏂囧涔燺ev.mp4 [36.6 MB]
04-lora微调思想（重点）_ev.mp4 [40.8 MB]
05-lora浼唬鐮乢ev.mp4 [9.9 MB]
📁 day01 【项目】基于知识库RAG的物流行业信息问答系统
06-寰皟鏂规硶_ev.mp4 [89.3 MB]
02-环境配置_ev.mp4 [11.7 MB]
01-项目介绍_ev.mp4 [27.2 MB]
📁 day05【项目】新零售行业评价决策系统
11-数据获取_ev.mp4 [41.4 MB]
08-项目架构_ev.mp4 [17.7 MB]
09-数据集介绍_ev.mp4 [25.1 MB]
10-配置信息_ev.mp4 [15.9 MB]
03-预测实现_ev.mp4 [10.6 MB]
04-预测实现2_ev.mp4 [92.6 MB]
02-棰勬祴娴佺▼_ev.mp4 [15.7 MB]
📁 day07 【项目】新媒体行业评论智能分类与信息抽取系统
03-数据处理_ev.mp4 [22.4 MB]
02-项目介绍_ev.mp4 [33.2 MB]
04-数据处理实现_ev.mp4 [72.3 MB]
📁 day09 【项目】新媒体行业评论智能分类与信息抽取系统
01-数据处理_ev.mp4 [109.2 MB]
07-图像生成算法_ev.mp4 [21.0 MB]
📁 day08 【项目】新媒体行业评论智能分类与信息抽取系统
04-数据集介绍_ev.mp4 [76.0 MB]
01-模型训练与推理_ev.mp4 [81.9 MB]
02-lora微调项目介绍_ev.mp4 [29.2 MB]
📁 day03 【项目】大健康行业智能问诊系统2
02-数据集介绍_ev.mp4 [21.2 MB]
01-项目介绍-1730813282_ev.mp4 [37.9 MB]
📁 02阶段：⼤模型应⽤初体验
📁 day01 大模型应用工具实战1
05-（重点）通义智文_ev.mp4 [65.1 MB]
06-（重点）通义听悟_ev.mp4 [43.1 MB]
07-（重点）通义法睿_ev.mp4 [54.8 MB]
08-（重点）讯飞星火_ev.mp4 [102.4 MB]
03-（重点）通义千问大模型使用_ev.mp4 [151.3 MB]
01-（了解）AI工具学习目标_ev.mp4 [6.1 MB]
02-（重点）传智星云网_ev.mp4 [72.5 MB]
04-（重点）通义万象_ev.mp4 [138.6 MB]
📁 day05 大模型Prompt-Tuning方法进阶
01-GPT原理_ev.mp4 [29.1 MB]
06-主流的开源大模型_ev.mp4 [71.6 MB]
📁 day04 主流大模型介绍及大模型Prompt-Tuning方法入
02-大语言模型的主要类别_ev.mp4 [43.7 MB]
04-主流的模型架构_ev.mp4 [16.9 MB]
01-语言模型的评估指标_ev.mp4 [102.2 MB]
📁 day08 企业大模型定制平台1
03-鏂囨湰鍒嗙被_ev.mp4 [60.5 MB]
01-项目说明_ev.mp4 [45.6 MB]
📁 day09 企业大模型定制平台2
02-淇℃伅鎶藉彇2_ev.mp4 [18.7 MB]
03-鏂囨湰鍖归厤_ev.mp4 [31.9 MB]
01-淇℃伅鎶藉彇_ev.mp4 [57.2 MB]
📁 day10 【项目】电商领域虚拟试衣系统
04-大模型定制平台_ev.mp4 [36.0 MB]
📁 day02 大模型应用工具实战2
04-（重点）通义灵码的使用_ev.mp4 [77.5 MB]
03-（重点）基于IFlyCode编写后端代码_ev.mp4 [49.3 MB]
13-（重点）Kimi大模型工具_ev.mp4 [26.0 MB]
07-（重点）Pika文生视频及图生视频效果_ev.mp4 [45.0 MB]
01-（重点）讯飞智文_ev.mp4 [97.5 MB]
12-（重点）AI运营极虎漫剪_ev.mp4 [173.5 MB]
09-（重点）可灵AI工具使用说明_ev.mp4 [207.4 MB]
08-（重点）Luma文生视频以及图生视频_ev.mp4 [87.1 MB]
05-（重点）AIGC堆友实现文生图以及图生图_ev.mp4 [92.8 MB]
14-（重点）智谱清言_ev.mp4 [52.8 MB]
06-（重点）哩布哩布AIGC生图工具使用_ev.mp4 [143.1 MB]
11-（重点）腾讯智影_ev.mp4 [50.2 MB]
02-（重点）VSCode集成IFlyCode实现前端页面编写_ev.mp4 [72.8 MB]
10-（重点）元分身数字人_ev.mp4 [180.2 MB]
📁 day06 大模型提示词工程应用
02-纭ā鐗堝井璋僟ev.mp4 [28.9 MB]
01-寰皟鏂规硶_ev.mp4 [75.2 MB]
📁 day03 大模型开发入门
04-语言模型的发展_ev.mp4 [137.5 MB]
01-课程内容说明_ev.mp4 [13.3 MB]
02-大语言模型的背景_ev.mp4 [61.4 MB]
03-语言模型理解_ev.mp4 [16.8 MB]
📁 day07 【项目】金融行业动态风向评估
03-鍏呰冻鐨勬€濊€僟ev.mp4 [47.7 MB]
02-娓呮櫚鐨勬弿杩癬ev.mp4 [30.6 MB]
01-鎻愮ず璇嶅伐绋媉ev.mp4 [88.9 MB]
04-杩唬浼樺寲_ev.mp4 [95.0 MB]
📁 05阶段：多模态大模型应用实战
📁 day02 【项目】Stable Diffusion多模态大模型应用实战2
05-hai平台使用_ev.mp4 [41.5 MB]
04-澶勭悊娴佺▼_ev.mp4 [60.9 MB]
02-stablediffusion鐨勫熀鏈蹇礯ev.mp4 [36.9 MB]
03-模型结构_ev.mp4 [15.4 MB]
📁 day01 【项目】Stable Diffusion多模态大模型应用实
📁 03阶段：⼤模型开发新增技术
📁 day03 大模型Agent的原理及实践
02-function_call数据库查询_ev.mp4 [70.9 MB]
📁 day02 【项目】财务助手
03-澶╂皵鑾峰彇_ev.mp4 [106.5 MB]
02-闃块噷鐧剧偧_ev.mp4 [113.6 MB]
📁 day08 大模型开发工具Langchain详解3
02-向量数据库_ev.mp4 [30.0 MB]
📁 day04 基于阿里魔搭社区的Agent应用
📁 day07 大模型开发工具Langchain详解2
📁 day06 大模型开发工具Langchain详解1
📁 day05 大模型Agent应用
03-閭欢妗堜緥_ev.mp4 [109.0 MB]
02-搴旂敤鍦烘櫙_ev.mp4 [90.7 MB]
📁 day01 大模型开发工具Function Call的原理及实践
02-百度千帆大模型使用_ev.mp4 [119.0 MB]
01-百度千帆大模型介绍_ev.mp4 [82.6 MB]

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• 掌握大模型基础知识
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