2025年聚客大模型第三期(聚客第一第二、第三期)

2025年聚客大模型第三期课程涵盖了AI大模型的全面学习路径，从Python基础到AI及LLM基础，再到Prompt工程、LangChain、Embedding、RAG等高级主题。课程内容包括多模态大模型、Hugging Face模型微调、Llama3大模型部署与微调、LangGraph、AutoGen Studio等多方面的实战应用。学员将通过项目实战，如基于RAG的智能客服系统、多模态大模型部署、语音识别与唤醒等，掌握AI大模型的实际应用与开发技能。

类型：	AI,聚客,项目实战,语音识别,微调,Llama3,Face,Hugging,多模态,RAG,LangChain,Python,AI大模型,

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「」2025年聚客大模型第三期（聚客第一第二、第三期）

00_Python基础

1-初始Python.mp4 (9.55 MB)

2-Windows环境安装.mp4 (6.63 MB)

3-macOS环境安装.mp4 (6.84 MB)

4-VSCode安装与应用.mp4 (19.4 MB)

5-PyCharn安装与应用.mp4 (22.9 MB)

6-pip包管理工具.mp4 (28.7 MB)

7-Python工程应用-字符串.mp4 (32.3 MB)

8-Python文档化应用场景.mp4 (19.0 MB)

9-如何使用注解.mp4 (29.1 MB)

10-字符编码的处理.mp4 (64.7 MB)

11-Python程序调式和异常处理技巧.mp4 (95.6 MB)

12-JSON应用.mp4 (40.1 MB)

13-文件IO.mp4 (25.3 MB)

14-爬虫（1）.mp4 (33.2 MB)

15-爬虫（2）.mp4 (76.7 MB)

16-爬虫（3）.mp4 (63.9 MB)

17-爬虫（4）.mp4 (66.9 MB)

18-字符串处理.mp4 (50.7 MB)

19.dotenv使用.mp4 (31.3 MB)

20.FastAPI的使用.mp4 (59.6 MB)

01_AI及LLM基础

day01_AI领域基础概念

day1-demo.zip (14.3 KB)

OpenAI-HK 操作指南.pdf (151.1 KB)

OpenAI.apifox.json (244.5 KB)

【课件】AI 领域基础概念.pdf (7.56 MB)

【录播】AI 领域基础概念.mp4 (1.12 GB)

【MD】AI 领域基础概念.md (49.9 KB)

【语雀】AI 领域基础概念.txt (110.0 B)

【资料】AI 领域基础概念.pdf (1.74 MB)

day02_OpenAI 开发

day2-demo.zip (766.1 KB)

【课件】OpenAI 开发.pdf (1.64 MB)

【录播】OpenAI 开发.mp4 (601.6 MB)

【MD】OpenAI 开发.md (6.92 KB)

【语雀】OpenAI 开发.txt (99.0 B)

【资料】OpenAI 开发.pdf (642.8 KB)

day03_支持多模态输入的 AI Chatbot App

day3-demo.zip (787.7 KB)

【课件】支持多模态输入的 AI Chatbot App.pdf (839.0 KB)

【录播】支持多模态输入的 AI Chatbot App.mp4 (1.51 GB)

【MD】支持多模态输入的 AI Chatbot App.md (3.59 KB)

【语雀】支持多模态输入的 AI Chatbot App.txt (125.0 B)

【资料】支持多模态输入的 AI Chatbot App.pdf (2.85 MB)

02_Prompt基础

day04_Prompt Engineering 提示词工程

ChatGPT提示技巧工程完全指南.pdf (2.87 MB)

DALL-E-3绘图提示词大全.pdf (14.3 MB)

day4-demo.zip (106.0 KB)

实用Prompt指令大全.xlsx (6.18 MB)

【课件】Prompt Engineering 提示词工程.pdf (826.4 KB)

【录播】Prompt Engineering 提示词工程.mp4 (1.26 GB)

【MD】Prompt Engineering 提示词工程.md (25.0 KB)

【语雀】Prompt Engineering 提示词工程.txt (121.0 B)

【资料】Prompt Engineering 提示词工程.pdf (2.64 MB)

03_LangChain基础

day05_LangChain 基础

day5-demo.zip (16.1 KB)

【课件】LangChain 基础.pdf (815.1 KB)

【录播】LangChain 基础.mp4 (596.7 MB)

【MD】LangChain 基础.md (68.0 KB)

【语雀】LangChain 基础.txt (102.0 B)

【资料】LangChain 基础.pdf (3.08 MB)

day06_LangChain Chat Model

day6-demo.zip (165.1 KB)

redis-3.2.100_x64.zip (4.93 MB)

RedisDesktopManager-2022.5.zip (29.1 MB)

vs_BuildTools.exe (4.22 MB)

【课件】LangChain Chat Model.pdf (872.4 KB)

【录播】LangChain Chat Model.mp4 (793.0 MB)

【MD】LangChain Chat Model.md (57.1 KB)

【语雀】LangChain Chat Model.txt (106.0 B)

【资料】LangChain Chat Model.pdf (3.11 MB)

day07_LangChain Tools & Agent

day7-demo.zip (1.36 MB)

【课件】LangChain Tools & Agent.pdf (869.8 KB)

【录播】LangChain Tools & Agent.mp4 (1.60 GB)

【MD】LangChain Tools & Agent.md (62.0 KB)

【语雀】LangChain Tools & Agent.txt (110.0 B)

【资料】LangChain Tools & Agent.pdf (2.75 MB)

04_Embedding基础

day08_Embedding 与向量数据库

day8-demo.zip (1.23 MB)

【课件】Embedding 与向量数据库.pdf (867.5 KB)

【录播】Embedding 与向量数据库.mp4 (705.2 MB)

【MD】Embedding 与向量数据库.md (96.4 KB)

【语雀】Embedding 与向量数据库.txt (114.0 B)

【资料】Embedding 与向量数据库.pdf (6.45 MB)

05_Rag基础

day09_RAG 专题

day9-demo.zip (286.2 KB)

【课件】RAG 专题.pdf (863.5 KB)

【录播】RAG 专题.mp4 (788.8 MB)

【MD】RAG 专题.md (54.2 KB)

【语雀】RAG 专题.txt (96.0 B)

【资料】RAG 专题.pdf (2.75 MB)

06_LangChain进阶

day10_自定义组件专题

day10-demo.zip (18.7 KB)

【课件】自定义组件专题.pdf (858.0 KB)

【录播】自定义组件专题.mp4 (830.9 MB)

【MD】自定义组件专题.md (48.7 KB)

【语雀】自定义组件专题.txt (107.0 B)

【资料】自定义组件专题.pdf (2.24 MB)

07_langChain和RAG实战

day11_基于LangChain和RAG的常用案例实战

day11-demo.zip (2.14 MB)

【课件】基于LangChain和RAG的常用案例实战.pdf (863.5 KB)

【录播】基于LangChain和RAG的常用案例实战.mp4 (817.6 MB)

【MD】基于LangChain和RAG的常用案例实战.md (28.1 KB)

【语雀】基于LangChain和RAG的常用案例实战.txt (128.0 B)

【资料】基于LangChain和RAG的常用案例实战.pdf (5.41 MB)

08_LangGraph

day12_LangGraph

day12-demo.zip (127.0 KB)

【课件】LangGraph.pdf (866.0 KB)

【录播】LangGraph.mp4 (798.7 MB)

【MD】LangGraph.md (129.8 KB)

【语雀】LangGraph.txt (95.0 B)

【资料】LangGraph.pdf (2.52 MB)

09_Hugging Face

day_13Hugging Face 核心组件介绍

demo_13.zip (1.06 GB)

【课件】Hugging Face 核心组件介绍.pdf (502.4 KB)

【录播】Hugging Face 核心组件介绍.mp4 (1.74 GB)

【资料】Hugging Face 核心组件介绍.pdf (312.9 KB)

day_14Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）

【课件】Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）.pdf (503.5 KB)

【录播】Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）.mp4.mp4 (766.4 MB)

【资料】Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）.pdf (743.2 KB)

day_15Hugging Face 模型微调训练（如何处理超长文本训练问题）

model.zip (364.5 MB)

【课件】Hugging Face 模型微调训练（如何处理超长文本训练问题）.pdf (498.9 KB)

【录播】Hugging Face 模型微调训练（如何处理超长文本训练问题）.mp4 (801.4 MB)

【资料】Hugging Face 模型微调训练（如何处理超长文本训练问题）.pdf (361.4 KB)

day_16Hugging Face 模型微调训练（GPT2-中文生成模型定制化微调训练）

demo_16.zip (27.8 MB)

【课件】Hugging Face 模型微调训练（GPT2-中文生成模型定制化微调训练）.pdf (513.9 KB)

【录播】Hugging Face 模型微调训练（GPT2-中文生成模型定制化微调训练）.mp4 (802.8 MB)

【资料】Hugging Face 模型微调训练（GPT2-中文生成模型定制化微调训练）.pdf (323.9 KB)

10_modelScope

day_17ModeScope在线训练平台&服务器选配训练模型

demo_17.zip (27.8 MB)

【课件】ModeScope在线训练平台&服务器选配训练模型.pdf (539.7 KB)

【录播】ModeScope在线训练平台&服务器选配训练模型.mp4 (1.11 GB)

【资料】ModeScope在线训练平台&服务器选配训练模型.pdf (275.8 KB)

11_Llama3

day_18Llama3大模型本地部署与调用

【课件】llama3大模型本地部署与调用.pdf (555.2 KB)

【录播】Llama3大模型本地部署与调用.mp4 (877.9 MB)

【资料】Llama3大模型本地部署与调用(1).pdf (1.53 MB)

【资料】Llama3大模型本地部署与调用.pdf (1.19 MB)

day_19LLaMa3微调_使用 LLaMA-Factory微调Llama3

data.zip (280.1 KB)

demo_19.zip (4.31 KB)

【课件】LLaMa3微调（使用 LLaMA-Factory 微调 LLaMA3）.pdf (602.0 KB)

【录播】LLaMA_Factory微调Llama3.mp4 (881.6 MB)

【资料】LLaMa3微调（使用 LLaMA-Factory 微调 LLaMA3）.pdf (140.9 KB)

day_20LLaMa3打包部署教程 (Lora 微调与模型合并）

demo_20.zip (4.27 KB)

【课件】LLaMa3打包部署（Lora微调与模型合并部署）.pdf (493.1 KB)

【录播】LLaMa3打包部署教程 (Lora 微调与模型合并).mp4 (1.83 GB)

【资料】LLaMa3 打包部署教程 (Lora 微调与模型合并部署).pdf (782.6 KB)

day_21LLaMa3打包部署（LLaMA-Factory模型评估与量化）

Lora微调权重（Llama-3-8B-Instruct）.zip (299.2 MB)

【课件】LLaMa3打包部署（LLaMA-Factory模型评估与量化）.pdf (497.1 KB)

【录播】LLaMa3打包部署（LLaMA-Factory模型评估与量化）.mp4 (899.0 MB)

【资料】LLaMa3打包部署（LLaMA-Factory模型评估与量化）.pdf (997.2 KB)

day_22LLaMa3打包部署（大模型转换为 GGUF 以及使用 ollama 运行）

Llama-3-8B-Instruct

qlora

train_2024-11-27-21-02-24

checkpoint-100

adapter_config.json (763.0 B)

adapter_model.safetensors (80.1 MB)

optimizer.pt (160.4 MB)

README.md (5.02 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (325.0 B)

tokenizer.json (16.4 MB)

tokenizer_config.json (49.9 KB)

trainer_state.json (4.29 KB)

training_args.bin (5.30 KB)

checkpoint-150

adapter_config.json (763.0 B)

adapter_model.safetensors (80.1 MB)

optimizer.pt (160.4 MB)

README.md (5.02 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (325.0 B)

tokenizer.json (16.4 MB)

tokenizer_config.json (49.9 KB)

trainer_state.json (5.96 KB)

training_args.bin (5.30 KB)

adapter_config.json (763.0 B)

adapter_model.safetensors (80.1 MB)

all_results.json (359.0 B)

eval_results.json (174.0 B)

llamaboard_config.yaml (1.69 KB)

README.md (1.60 KB)

running_log.txt (15.7 KB)

special_tokens_map.json (325.0 B)

tokenizer.json (16.4 MB)

tokenizer_config.json (49.9 KB)

train_results.json (220.0 B)

trainer_log.jsonl (5.94 KB)

trainer_state.json (6.21 KB)

training_args.bin (5.30 KB)

training_args.yaml (844.0 B)

training_eval_loss.png (23.1 KB)

training_loss.png (43.9 KB)

【课件】LLaMa3打包部署（大模型转换为 GGUF 以及使用 ollama 运行）.pdf (568.0 KB)

【录播】LLaMa3打包部署（大模型转换为 GGUF 以及使用 ollama 运行） -笔记.PanD (93.0 B)

【录播】LLaMa3打包部署（大模型转换为 GGUF 以及使用 ollama 运行） .mp4 (862.9 MB)

【资料】LLaMa3打包部署（大模型转换为 GGUF 以及使用 ollama 运行）.pdf (219.3 KB)

12_多模态

day_23多模态（多模态大模型的概念与本地部署调用）

文生视频效果.mp4 (360.9 KB)

【课件】多模态（多模态大模型的概念与本地部署调用）.pdf (731.7 KB)

【录播】多模态大模型的概念与本地部署调用.mp4 (1.23 GB)

【资料】多模态（多模态大模型的概念与本地部署调用）.pdf (4.90 MB)

13_llamaindex

day_24Llama_Index（核心组件介绍）

demo_24.zip (4.87 MB)

llama_index0.8.3.zip (108.1 MB)

【课件】Llama_Index（核心组件介绍）.pdf (486.3 KB)

【录播】Llama_Index（核心组件介绍）.mp4 (1.51 GB)

【资料】Llama_Index（核心组件介绍）.pdf (624.0 KB)

day_25llamaindex实战（使用llamaindex构建自己的知识库）

demo_25.zip (9.00 KB)

【课件】llamaindex实战（使用llamaindex构建自己的知识库）.pdf (491.4 KB)

【录播】llamaindex实战（使用llamaindex构建自己的知识库）.mp4 (1.01 GB)

【资料】llamaindex实战（使用llamaindex构建自己的知识库）.pdf (2.23 MB)

14_AutoGen Studio

day_26AutoGen Studio调用本地大模型实现多Agent应用

【课件】AutoGen Studio入门使用.pdf (598.4 KB)

【录播】AutoGen Studio调用本地大模型实现多Agent应用.mp4 (834.2 MB)

【资料】AutoGen Studio入门使用.pdf (858.8 KB)

15 项目实战（聚客一和二期)

day33_RAG项目实战（使用llamaindex构建自己的知识库）

RAG_项目源码.zip (11.3 KB)

【课件】RAG项目实战（使用llamaindex构建自己的知识库）.pdf (492.4 KB)

【录播】RAG项目实战（使用llamaindex构建自己的知识库）.mp4 (2.29 GB)

【资料】RAG项目实战（使用llamaindex构建自己的知识库）.pdf (2.48 MB)

day34_视觉项目实战（基于yolo的骨龄识别项目_01）

yolov5-master.zip (127.0 MB)

【课件】视觉项目实战（基于yolo的骨龄识别项目_01）.pdf (761.7 KB)

【录播】视觉项目实战（基于yolo的骨龄识别项目_01）.mp4 (925.6 MB)

【资料】YOLOv5目标侦测教程.pdf (8.45 MB)

day35_视觉项目实战（基于yolo的骨龄识别项目_02）

dataset

arthrosis.zip (141.5 MB)

VOCdevkit.zip (827.4 MB)

day31_demo

hand_bone_detect

cutpictures

DIPFifth.png (33.6 KB)

DIPFirst.png (55.3 KB)

DIPThird.png (42.5 KB)

MCPFifth.png (37.7 KB)

MCPFirst.png (63.9 KB)

MCPThird.png (43.5 KB)

MIPFifth.png (25.4 KB)

MIPThird.png (32.6 KB)

PIPFifth.png (28.2 KB)

PIPFirst.png (38.1 KB)

PIPThird.png (37.1 KB)

Radius.png (81.5 KB)

Ulna.png (53.2 KB)

detect_result

detect.jpg (1.37 MB)

img

1548.png (2.26 MB)

params

DIP_best.pth (42.7 MB)

DIPFirst_best.pth (42.7 MB)

MCP_best.pth (42.7 MB)

MCPFirst_best.pth (42.7 MB)

MIP_best.pth (42.7 MB)

PIP_best.pth (42.7 MB)

PIPFirst_best.pth (42.7 MB)

Radius_best.pth (42.7 MB)

Ulna_best.pth (42.7 MB)

templates

client.html (574.0 B)

result.html (238.0 B)

test_data

example.jpg (689.3 KB)

bone_detect_ui.ui (2.30 KB)

bone_filter_utils.py (622.0 B)

common.py (6.96 KB)

detect_bone.py (2.86 KB)

detect_utils.py (3.34 KB)

flask_test_img.py (1.27 KB)

main.py (1.65 KB)

test.py (995.0 B)

hand_test

logs

DIP_loss_DIP

val_avg_losses

events.out.tfevents.1688888129.DESKTOP-BT6NS4S.11960.2 (3.68 KB)

DIP_loss_train_avg_loss

events.out.tfevents.1688888129.DESKTOP-BT6NS4S.11960.1 (3.68 KB)

DIPFirst_loss_DIPFirst

val_avg_losses

events.out.tfevents.1688787690.DESKTOP-BT6NS4S.13268.4 (4.07 KB)

DIPFirst_loss_train_avg_loss

events.out.tfevents.1688787690.DESKTOP-BT6NS4S.13268.3 (4.07 KB)

MCP_loss_MCP

val_avg_losses

events.out.tfevents.1688891547.DESKTOP-BT6NS4S.11960.4 (3.09 KB)

MCP_loss_train_avg_loss

events.out.tfevents.1688891547.DESKTOP-BT6NS4S.11960.3 (3.09 KB)

MCPFirst_loss_MCPFirst

val_avg_losses

events.out.tfevents.1688785342.DESKTOP-BT6NS4S.13268.2 (4.07 KB)

MCPFirst_loss_train_avg_loss

events.out.tfevents.1688785342.DESKTOP-BT6NS4S.13268.1 (4.07 KB)

MIP_loss_MIP

val_avg_losses

events.out.tfevents.1688792192.DESKTOP-BT6NS4S.13268.8 (3.68 KB)

MIP_loss_train_avg_loss

events.out.tfevents.1688792192.DESKTOP-BT6NS4S.13268.7 (3.68 KB)

PIP_loss_PIP

val_avg_losses

events.out.tfevents.1688801475.DESKTOP-BT6NS4S.13268.14 (2.42 KB)

PIP_loss_train_avg_loss

events.out.tfevents.1688801475.DESKTOP-BT6NS4S.13268.13 (2.42 KB)

PIPFirst_loss_PIPFirst

val_avg_losses

events.out.tfevents.1688789961.DESKTOP-BT6NS4S.13268.6 (4.07 KB)

PIPFirst_loss_train_avg_loss

events.out.tfevents.1688789961.DESKTOP-BT6NS4S.13268.5 (4.07 KB)

Radius_loss_Radius

val_avg_losses

events.out.tfevents.1688796343.DESKTOP-BT6NS4S.13268.10 (3.91 KB)

Radius_loss_train_avg_loss

events.out.tfevents.1688796343.DESKTOP-BT6NS4S.13268.9 (3.91 KB)

Ulna_loss_train_avg_loss

events.out.tfevents.1688799016.DESKTOP-BT6NS4S.13268.11 (3.76 KB)

Ulna_loss_Ulna

val_avg_losses

events.out.tfevents.1688799016.DESKTOP-BT6NS4S.13268.12 (3.76 KB)

events.out.tfevents.1688785315.DESKTOP-BT6NS4S.13268.0 (29.1 KB)

events.out.tfevents.1688888077.DESKTOP-BT6NS4S.11960.0 (7.69 KB)

params

DIP_best.pth (42.7 MB)

DIPFirst_best.pth (42.7 MB)

MCP_best.pth (42.7 MB)

MCPFirst_best.pth (42.7 MB)

MIP_best.pth (42.7 MB)

PIP_best.pth (42.7 MB)

PIPFirst_best.pth (42.7 MB)

Radius_best.pth (42.7 MB)

Ulna_best.pth (42.7 MB)

utils

data_set.py (1.64 KB)

data_utils.py (2.48 KB)

tools.py (805.0 B)

test1.py (244.0 B)

trainer.py (3.72 KB)

yolov5-bone

.github

ISSUE_TEMPLATE

bug-report.yml (2.85 KB)

config.yml (358.0 B)

feature-request.yml (1.74 KB)

question.yml (1.12 KB)

workflows

ci-testing.yml (7.52 KB)

codeql-analysis.yml (2.02 KB)

docker.yml (1.56 KB)

greetings.yml (5.38 KB)

links.yml (1.74 KB)

stale.yml (2.31 KB)

translate-readme.yml (708.0 B)

dependabot.yml (441.0 B)

PULL_REQUEST_TEMPLATE.md (774.0 B)

__pycache__

export.cpython-310.pyc (30.5 KB)

hubconf.cpython-310.pyc (5.10 KB)

val.cpython-310.pyc (13.8 KB)

classify

predict.py (11.5 KB)

train.py (16.0 KB)

tutorial.ipynb (101.2 KB)

val.py (7.89 KB)

data

hyps

hyp.no-augmentation.yaml (1.64 KB)

hyp.Objects365.yaml (674.0 B)

hyp.scratch-high.yaml (1.64 KB)

hyp.scratch-low.yaml (1.65 KB)

hyp.scratch-med.yaml (1.65 KB)

hyp.VOC.yaml (1.13 KB)

images

14732.png (584.9 KB)

1526.png (1.05 MB)

1547.png (1.32 MB)

1548.png (2.26 MB)

scripts

download_weights.sh (641.0 B)

get_coco.sh (1.53 KB)

get_coco128.sh (619.0 B)

get_imagenet.sh (1.63 KB)

Argoverse.yaml (2.67 KB)

coco.yaml (2.44 KB)

coco128-seg.yaml (1.83 KB)

coco128.yaml (1.81 KB)

GlobalWheat2020.yaml (1.84 KB)

ImageNet.yaml (18.4 KB)

mydata.yaml (600.0 B)

Objects365.yaml (8.99 KB)

SKU-110K.yaml (2.29 KB)

VisDrone.yaml (2.90 KB)

VOC.yaml (3.41 KB)

xView.yaml (5.05 KB)

models

__pycache__

__init__.cpython-310.pyc (136.0 B)

common.cpython-310.pyc (36.1 KB)

experimental.cpython-310.pyc (4.72 KB)

yolo.cpython-310.pyc (15.7 KB)

hub

anchors.yaml (3.26 KB)

yolov3-spp.yaml (1.53 KB)

yolov3-tiny.yaml (1.20 KB)

yolov3.yaml (1.52 KB)

yolov5-bifpn.yaml (1.39 KB)

yolov5-fpn.yaml (1.19 KB)

yolov5-p2.yaml (1.65 KB)

yolov5-p6.yaml (1.70 KB)

yolov5-p7.yaml (2.07 KB)

yolov5-p34.yaml (1.32 KB)

yolov5-panet.yaml (1.38 KB)

yolov5l6.yaml (1.78 KB)

yolov5m6.yaml (1.78 KB)

yolov5n6.yaml (1.78 KB)

yolov5s-ghost.yaml (1.45 KB)

yolov5s-LeakyReLU.yaml (1.46 KB)

yolov5s-transformer.yaml (1.41 KB)

yolov5s6.yaml (1.78 KB)

yolov5x6.yaml (1.78 KB)

segment

yolov5l-seg.yaml (1.38 KB)

yolov5m-seg.yaml (1.38 KB)

yolov5n-seg.yaml (1.38 KB)

yolov5s-seg.yaml (1.38 KB)

yolov5x-seg.yaml (1.38 KB)

__init__.py

common.py (40.8 KB)

experimental.py (4.22 KB)

tf.py (26.4 KB)

yolo.py (17.4 KB)

yolov5l.yaml (1.37 KB)

yolov5m.yaml (1.37 KB)

yolov5n.yaml (1.37 KB)

yolov5s.yaml (1.37 KB)

yolov5x.yaml (1.37 KB)

runs

detect

exp

1547.png (3.90 MB)

1548.png (6.35 MB)

exp2

14732.png (1.55 MB)

1547.png (3.90 MB)

1548.png (6.35 MB)

exp3

14732.png (1.55 MB)

1526.png (2.88 MB)

1547.png (3.90 MB)

1548.png (6.35 MB)

exp4

14732.png (1.55 MB)

1526.png (2.88 MB)

1547.png (3.90 MB)

1548.png (6.35 MB)

exp5

crops

DistalPhalanx

14732.jpg (4.83 KB)

147322.jpg (3.98 KB)

147323.jpg (3.17 KB)

147324.jpg (3.68 KB)

147325.jpg (3.98 KB)

1526.jpg (7.51 KB)

15262.jpg (6.63 KB)

15263.jpg (7.36 KB)

15264.jpg (10.8 KB)

15265.jpg (7.45 KB)

1547.jpg (8.51 KB)

15472.jpg (8.76 KB)

15473.jpg (10.1 KB)

15474.jpg (11.0 KB)

15475.jpg (14.9 KB)

1548.jpg (28.2 KB)

15482.jpg (30.3 KB)

15483.jpg (28.5 KB)

15484.jpg (34.7 KB)

15485.jpg (26.8 KB)

MCP

14732.jpg (4.37 KB)

147322.jpg (4.05 KB)

147323.jpg (5.02 KB)

147324.jpg (5.09 KB)

1526.jpg (7.05 KB)

15262.jpg (7.18 KB)

15263.jpg (8.14 KB)

15264.jpg (8.76 KB)

1547.jpg (11.6 KB)

15472.jpg (9.88 KB)

15473.jpg (9.98 KB)

15474.jpg (11.6 KB)

1548.jpg (28.4 KB)

15482.jpg (21.9 KB)

15483.jpg (21.5 KB)

15484.jpg (26.5 KB)

MCPFirst

14732.jpg (7.52 KB)

1526.jpg (13.2 KB)

1547.jpg (16.2 KB)

1548.jpg (39.0 KB)

MiddlePhalanx

14732.jpg (3.74 KB)

147322.jpg (3.93 KB)

147323.jpg (2.96 KB)

147324.jpg (3.51 KB)

1526.jpg (5.13 KB)

15262.jpg (6.77 KB)

15263.jpg (6.03 KB)

15264.jpg (6.77 KB)

1547.jpg (9.07 KB)

15472.jpg (8.48 KB)

15473.jpg (7.40 KB)

15474.jpg (7.77 KB)

1548.jpg (20.5 KB)

15482.jpg (16.2 KB)

15483.jpg (19.6 KB)

15484.jpg (19.6 KB)

ProximalPhalanx

14732.jpg (3.18 KB)

147322.jpg (4.71 KB)

147323.jpg (4.74 KB)

147324.jpg (4.35 KB)

147325.jpg (4.92 KB)

1526.jpg (6.80 KB)

15262.jpg (6.22 KB)

15263.jpg (7.05 KB)

15264.jpg (8.77 KB)

15265.jpg (8.70 KB)

1547.jpg (9.51 KB)

15472.jpg (9.68 KB)

15473.jpg (10.2 KB)

15474.jpg (10.4 KB)

15475.jpg (8.49 KB)

1548.jpg (18.6 KB)

15482.jpg (24.1 KB)

15483.jpg (25.3 KB)

15484.jpg (24.2 KB)

15485.jpg (22.3 KB)

Radius

14732.jpg (11.0 KB)

1526.jpg (15.3 KB)

1547.jpg (21.9 KB)

1548.jpg (57.1 KB)

Ulna

14732.jpg (7.81 KB)

1526.jpg (9.88 KB)

1547.jpg (15.3 KB)

1548.jpg (31.3 KB)

14732.png (1.55 MB)

1526.png (2.88 MB)

1547.png (3.90 MB)

1548.png (6.35 MB)

train

exp

weights

best.pt (54.2 MB)

last.pt (54.2 MB)

hyp.yaml (401.0 B)

labels.jpg (133.3 KB)

labels_correlogram.jpg (234.9 KB)

opt.yaml (1.08 KB)

results.csv (25.6 KB)

train_batch0.jpg (573.4 KB)

train_batch1.jpg (582.7 KB)

train_batch2.jpg (526.6 KB)

segment

predict.py (15.4 KB)

train.py (33.9 KB)

tutorial.ipynb (42.4 KB)

val.py (23.4 KB)

utils

__pycache__

__init__.cpython-310.pyc (2.67 KB)

augmentations.cpython-310.pyc (13.4 KB)

autoanchor.cpython-310.pyc (6.32 KB)

autobatch.cpython-310.pyc (2.46 KB)

bone_filter_utils.cpython-310.pyc (847.0 B)

bone_utils.cpython-310.pyc (837.0 B)

callbacks.cpython-310.pyc (2.65 KB)

dataloaders.cpython-310.pyc (42.3 KB)

downloads.cpython-310.pyc (4.15 KB)

general.cpython-310.pyc (36.8 KB)

loss.cpython-310.pyc (6.10 KB)

metrics.cpython-310.pyc (11.0 KB)

plots.cpython-310.pyc (21.0 KB)

torch_utils.cpython-310.pyc (16.4 KB)

aws

__init__.py

mime.sh (780.0 B)

resume.py (1.17 KB)

userdata.sh (1.22 KB)

docker

Dockerfile (2.61 KB)

Dockerfile-arm64 (1.64 KB)

Dockerfile-cpu (1.67 KB)

flask_rest_api

example_request.py (369.0 B)

README.md (1.67 KB)

restapi.py (1.41 KB)

google_app_engine

additional_requirements.txt (187.0 B)

app.yaml (174.0 B)

Dockerfile (821.0 B)

loggers

__pycache__

__init__.cpython-310.pyc (13.2 KB)

clearml

__pycache__

__init__.cpython-310.pyc (151.0 B)

clearml_utils.cpython-310.pyc (5.77 KB)

__init__.py

clearml_utils.py (7.85 KB)

hpo.py (5.15 KB)

README.md (10.6 KB)

comet

__pycache__

__init__.cpython-310.pyc (14.4 KB)

comet_utils.cpython-310.pyc (4.13 KB)

__init__.py (18.3 KB)

comet_utils.py (4.64 KB)

hpo.py (6.50 KB)

optimizer_config.json (2.95 KB)

README.md (10.5 KB)

wandb

__pycache__

__init__.cpython-310.pyc (149.0 B)

wandb_utils.cpython-310.pyc (6.78 KB)

__init__.py

wandb_utils.py (8.06 KB)

__init__.py (16.1 KB)

segment

__pycache__

__init__.cpython-310.pyc (143.0 B)

general.cpython-310.pyc (4.98 KB)

__init__.py

augmentations.py (3.67 KB)

dataloaders.py (13.5 KB)

general.py (5.68 KB)

loss.py (8.39 KB)

metrics.py (5.33 KB)

plots.py (6.24 KB)

__init__.py (2.58 KB)

activations.py (3.37 KB)

augmentations.py (16.6 KB)

autoanchor.py (7.25 KB)

autobatch.py (2.92 KB)

bone_filter_utils.py (622.0 B)

bone_utils.py (675.0 B)

callbacks.py (2.60 KB)

dataloaders.py (54.5 KB)

downloads.py (4.84 KB)

general.py (44.4 KB)

loss.py (9.69 KB)

metrics.py (14.2 KB)

plots.py (24.1 KB)

torch_utils.py (19.2 KB)

triton.py (3.55 KB)

.dockerignore (3.61 KB)

.gitattributes (75.0 B)

.gitignore (3.90 KB)

.pre-commit-config.yaml (1.71 KB)

benchmarks.py (7.82 KB)

CITATION.cff (393.0 B)

CONTRIBUTING.md (4.89 KB)

detect.py (14.0 KB)

export.py (40.2 KB)

hubconf.py (7.59 KB)

images_tag.py (1.43 KB)

LICENSE (33.7 KB)

README.md (40.5 KB)

README.zh-CN.md (39.6 KB)

requirements.txt (1.52 KB)

setup.cfg (1.69 KB)

test1.py (1.08 KB)

test08.py (900.0 B)

train.py (33.2 KB)

tutorial.ipynb (40.0 KB)

val.py (20.0 KB)

voc_to_yolo.py (2.63 KB)

yolov5s.pt (14.1 MB)

【课件】视觉项目实战（基于yolo的骨龄识别项目_02）.pdf (805.9 KB)

【录播】视觉项目实战（基于yolo的骨龄识别项目_02）.mp4 (920.7 MB)

day_27基于本地大模型的在线心理问诊系统（训练篇）

data.zip (17.0 MB)

demo_27.zip (2.56 KB)

项目流程.png (103.1 KB)

【课件】基于本地大模型的在线心理问诊系统（训练篇）.pdf (497.8 KB)

【录播】基于本地大模型的在线心理问诊系统（训练篇01）.mp4 (1.17 GB)

【资料】xtuner微调大模型教程.pdf (173.0 KB)

day_28基于本地大模型的在线心理问诊系统（训练篇）

data

llama_factory_data.zip (5.54 MB)

output_conversations.csv (18.1 MB)

xtuner_data.zip (5.78 MB)

llamafactory数据集转换代码

data_utils.py (2.29 KB)

xtuner环境

requirements.txt (4.33 KB)

xtuner模型训练配置文件

internlm2_5_chat_7b_qlora_oasst1_e3.py (8.04 KB)

qwen1_5_1_8b_chat_qlora_alpaca_e3.py (7.53 KB)

【录播】基于本地大模型的在线心理问诊系统（训练篇02）.mp4 (990.3 MB)

day_29基于本地大模型的在线心理问诊系统(部署篇)

项目模型

Qwen1.5-1.8B-Chat_cusm

added_tokens.json (80.0 B)

config.json (748.0 B)

generation_config.json (205.0 B)

merges.txt (1.59 MB)

model.safetensors (3.42 GB)

special_tokens_map.json (367.0 B)

tokenizer.json (10.9 MB)

tokenizer_config.json (1.29 KB)

vocab.json (2.65 MB)

trainer_log.jsonl (42.0 KB)

training_eval_loss.png (40.1 KB)

training_loss.png (55.3 KB)

【课件】基于本地大模型的在线心理问诊系统（部署篇）.pdf (496.7 KB)

【录播】基于本地大模型的在线心理问诊系统(部署篇).mp4 (1.65 GB)

【资料】基于本地大模型的在线心理问诊系统（部署篇）.pdf (271.1 KB)

day_30基于RAG的线上智能客服系统（微调篇）

data.zip (2.19 MB)

demo_30.zip (1.70 KB)

项目背景.png (149.3 KB)

【课件】基于RAG的线上智能客服系统（微调篇）.pdf (494.6 KB)

【录播】基于RAG的线上智能客服系统（微调篇）.mp4 (1.15 GB)

day_31基于RAG的线上智能客服系统（部署篇）

lora模型

Qwen2.5-3B-Instruct-lora.zip (2.69 GB)

demo_31.zip (30.9 KB)

【课件】基于RAG的线上智能客服系统（部署篇）.pdf (495.8 KB)

【录播】基于RAG的线上智能客服系统（部署篇）.mp4 (1.40 GB)

【资料】OpenCompass文档.md (22.1 KB)

day_32基于pytorch的语音识别与语音唤醒

本地存储index的RAG

data

data.csv (29.2 KB)

【资料】OpenCompass文档.md (22.1 KB)

storage

default__vector_store.json (361.0 KB)

docstore.json (171.4 KB)

graph_store.json (18.0 B)

image__vector_store.json (72.0 B)

index_store.json (3.60 KB)

rag.py (2.68 KB)

demo_32.zip (204.4 MB)

语音应用场景.png (67.8 KB)

【课件】扩展项目（基于pytorch实现的语音识别）.pdf (491.6 KB)

【录播】扩展项目（基于pytorch的语音识别与语音唤醒）.mp4 (1.79 GB)

16_项目实战（聚客第三期_最新）

1_开班典礼-241216

2024-12-16 开班典礼.mp4 (277.3 MB)

2_RAG-Embedding-Vector

day01

RAG-Embeddings

assets

embeddings.png (48.8 KB)

GraphRAG.png (752.8 KB)

mteb.png (23.6 KB)

RAG.mp4 (1.87 MB)

rag.png (81.7 KB)

sbert-rerank.png (71.8 KB)

sbert.png (20.8 KB)

sim.png (26.8 KB)

table_rag.png (420.4 KB)

vector.png (23.6 KB)

vectordb.png (16.6 KB)

.env (80.0 B)

chinese_utils.py (979.0 B)

index.ipynb (56.2 KB)

llama2.pdf (276.7 KB)

llama2_page8.pdf (176.4 KB)

rank.py (3.14 KB)

Python语法入门教程.md (49.3 KB)

RAG搭建流程和文本向量.mp4 (469.0 MB)

day02

RAG-Embeddings

assets

chroma.svg (1.20 MB)

embeddings.png (48.8 KB)

GraphRAG.png (752.8 KB)

mteb.png (23.6 KB)

RAG.mp4 (1.87 MB)

rag.png (81.7 KB)

sbert-rerank.png (71.8 KB)

sbert.png (20.8 KB)

sim.png (26.8 KB)

table_rag.png (420.4 KB)

vector.png (23.6 KB)

vectordb.png (16.6 KB)

llama2_page8

table_images

page_1_0.png (25.1 KB)

page_1_1.png (16.3 KB)

page_1.png (156.4 KB)

.env (80.0 B)

chinese_utils.py (979.0 B)

index.ipynb (319.4 KB)

llama2.pdf (276.7 KB)

llama2_page8.pdf (176.4 KB)

rank.py (3.14 KB)

向量数据库和RAG高级进阶.mp4 (634.7 MB)

3_LangChain

LangChain

assets

data_connection.jpg (42.3 KB)

langchain.png (54.6 KB)

model_io.jpg (643.3 KB)

serve

joke_client.py (136.0 B)

joke_server.py (573.0 B)

example_prompt_template.txt (31.0 B)

index.ipynb (63.7 KB)

llama2.pdf (276.7 KB)

memory.db (8.00 KB)

LangChain.mp4 (618.5 MB)

day04_Hugging Face 核心组件介绍

demo_4

API_test

api_test01.py (243.0 B)

api_test02.py (361.0 B)

data

ChnSentiCorp

test

cache-7dd332715d90b654.arrow (9.97 KB)

dataset.arrow (372.3 KB)

dataset_info.json (1.79 KB)

state.json (255.0 B)

train

cache-1e3bba9512e20e17.arrow (73.5 KB)

cache-3bdb9443d1ca0706.arrow (624.0 B)

cache-7f783dce092dc384.arrow (73.5 KB)

cache-8a97cd1e06b735d2.arrow (73.5 KB)

cache-9f44d179cc25c59e.arrow (73.5 KB)

cache-39dfc0aff9c238ae.arrow (73.5 KB)

cache-42a7d570466d6993.arrow (76.7 KB)

cache-618b312c42069194.arrow (73.5 KB)

cache-942e97a8804ee679.arrow (73.5 KB)

cache-3432ecc2b0d45f4c.arrow (73.5 KB)

cache-60739da7e9e626bf.arrow (73.5 KB)

cache-703908ea6da8e823.arrow (73.5 KB)

cache-980049a695f6628c.arrow (69.1 KB)

cache-a6ab41ffbc1946d5.arrow (7.95 KB)

cache-a34d90b946dc58b8.arrow (73.5 KB)

cache-a41fe1013beb0d46.arrow (73.5 KB)

cache-b4e51936648802e2.arrow (76.7 KB)

cache-b53b61aef9d859aa.arrow (73.5 KB)

cache-b85c50cd434dd865.arrow (73.5 KB)

cache-badcf79eb9fa62a0.arrow (73.5 KB)

cache-c5b262546ff026fd.arrow (76.7 KB)

cache-d7c6377d856b0538.arrow (73.5 KB)

cache-eb31b953e8e788fb.arrow (73.5 KB)

cache-fdafe12f59ab3430.arrow (73.5 KB)

dataset.arrow (2.96 MB)

dataset_info.json (1.79 KB)

state.json (256.0 B)

validation

cache-1b273238fdadead7.arrow (9.57 KB)

cache-4a46afc6455cc3f0.arrow (9.57 KB)

cache-5cd00431b9d3a916.arrow (9.57 KB)

cache-8ae76a3b52248a8f.arrow (9.57 KB)

cache-9b7789b1e0e636fb.arrow (9.57 KB)

cache-85b0ee5d0aeacbe5.arrow (9.57 KB)

cache-486e66fea9239c3f.arrow (9.57 KB)

cache-585e054403b710b3.arrow (9.57 KB)

cache-749efdb8f0ee42be.arrow (9.57 KB)

cache-805d054a96ccc48f.arrow (9.57 KB)

cache-428974171b74f1a0.arrow (9.57 KB)

cache-800047dd8fdede53.arrow (9.57 KB)

cache-a1c8a8bb0a669e93.arrow (9.57 KB)

cache-c072f719f0b9b62a.arrow (9.57 KB)

cache-e621cb96adf0c923.arrow (9.97 KB)

dataset.arrow (376.6 KB)

dataset_info.json (1.79 KB)

state.json (261.0 B)

dataset_dict.json (43.0 B)

hermes-function-calling-v1.csv (14.7 MB)

dataset

dataset_test.py (425.0 B)

trasnFormers_test

model

bert-base-chinese

.locks

models--bert-base-chinese

.no_exist

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

added_tokens.json

special_tokens_map.json

blobs

refs

main (40.0 B)

snapshots

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

config.json (624.0 B)

model.safetensors (392.5 MB)

tokenizer.json (262.6 KB)

tokenizer_config.json (49.0 B)

vocab.txt (107.0 KB)

uer

gpt2-chinese-cluecorpussmall

.locks

models--uer--gpt2-chinese-cluecorpussmall

.no_exist

blobs

refs

main (40.0 B)

snapshots

test01.py (489.0 B)

test02.py (2.67 KB)

test03.py (658.0 B)

【课件】Hugging Face 核心组件介绍.pdf (501.5 KB)

【录播】Hugging Face 核心组件介绍.mp4 (762.9 MB)

【资料】Hugging Face 核心组件介绍.pdf (243.1 KB)

day05_基于 BERT 的中文评价情感分析

demo_5

.idea

inspectionProfiles

profiles_settings.xml (174.0 B)

.gitignore (50.0 B)

demo_5.iml (291.0 B)

misc.xml (189.0 B)

modules.xml (271.0 B)

workspace.xml (7.20 KB)

__pycache__

MyData.cpython-312.pyc (1.48 KB)

net.cpython-312.pyc (1.75 KB)

data

ChnSentiCorp

test

cache-7dd332715d90b654.arrow (9.97 KB)

dataset.arrow (372.3 KB)

dataset_info.json (1.79 KB)

state.json (255.0 B)

train

cache-1e3bba9512e20e17.arrow (73.5 KB)

cache-3bdb9443d1ca0706.arrow (624.0 B)

cache-7f783dce092dc384.arrow (73.5 KB)

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cache-60739da7e9e626bf.arrow (73.5 KB)

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cache-980049a695f6628c.arrow (69.1 KB)

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cache-a34d90b946dc58b8.arrow (73.5 KB)

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cache-c5b262546ff026fd.arrow (76.7 KB)

cache-d7c6377d856b0538.arrow (73.5 KB)

cache-eb31b953e8e788fb.arrow (73.5 KB)

cache-fdafe12f59ab3430.arrow (73.5 KB)

dataset.arrow (2.96 MB)

dataset_info.json (1.79 KB)

state.json (256.0 B)

validation

cache-1b273238fdadead7.arrow (9.57 KB)

cache-4a46afc6455cc3f0.arrow (9.57 KB)

cache-5cd00431b9d3a916.arrow (9.57 KB)

cache-8ae76a3b52248a8f.arrow (9.57 KB)

cache-9b7789b1e0e636fb.arrow (9.57 KB)

cache-85b0ee5d0aeacbe5.arrow (9.57 KB)

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cache-585e054403b710b3.arrow (9.57 KB)

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cache-800047dd8fdede53.arrow (9.57 KB)

cache-a1c8a8bb0a669e93.arrow (9.57 KB)

cache-c072f719f0b9b62a.arrow (9.57 KB)

cache-e621cb96adf0c923.arrow (9.97 KB)

dataset.arrow (376.6 KB)

dataset_info.json (1.79 KB)

state.json (261.0 B)

dataset_dict.json (43.0 B)

hermes-function-calling-v1.csv (14.7 MB)

model

bert-base-chinese

.locks

models--bert-base-chinese

.no_exist

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

added_tokens.json

special_tokens_map.json

blobs

refs

main (40.0 B)

snapshots

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

config.json (624.0 B)

model.safetensors (392.5 MB)

tokenizer.json (262.6 KB)

tokenizer_config.json (49.0 B)

vocab.txt (107.0 KB)

params

0_bert.pth (7.46 KB)

1_bert.pth (7.46 KB)

2_bert.pth (7.46 KB)

data_test.py (658.0 B)

MyData.py (856.0 B)

net.py (989.0 B)

run.py (1.66 KB)

token_test.py (1.47 KB)

train.py (2.65 KB)

【课件】Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）.pdf (500.6 KB)

【录播】基于 BERT 的中文评价情感分析.mp4 (754.7 MB)

【资料】Hugging Face 模型微调训练（基于 BERT 的中文评价情感分析）.pdf (304.7 KB)

day06_自定义vocab

demo_6

.idea

inspectionProfiles

profiles_settings.xml (174.0 B)

.gitignore (50.0 B)

demo_6.iml (291.0 B)

encodings.xml (290.0 B)

misc.xml (189.0 B)

modules.xml (271.0 B)

workspace.xml (7.68 KB)

__pycache__

MyData.cpython-312.pyc (1.48 KB)

net.cpython-312.pyc (1.75 KB)

data

ChnSentiCorp

test

cache-7dd332715d90b654.arrow (9.97 KB)

dataset.arrow (372.3 KB)

dataset_info.json (1.79 KB)

state.json (255.0 B)

train

cache-1e3bba9512e20e17.arrow (73.5 KB)

cache-3bdb9443d1ca0706.arrow (624.0 B)

cache-7f783dce092dc384.arrow (73.5 KB)

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cache-9f44d179cc25c59e.arrow (73.5 KB)

cache-39dfc0aff9c238ae.arrow (73.5 KB)

cache-42a7d570466d6993.arrow (76.7 KB)

cache-618b312c42069194.arrow (73.5 KB)

cache-942e97a8804ee679.arrow (73.5 KB)

cache-3432ecc2b0d45f4c.arrow (73.5 KB)

cache-60739da7e9e626bf.arrow (73.5 KB)

cache-703908ea6da8e823.arrow (73.5 KB)

cache-980049a695f6628c.arrow (69.1 KB)

cache-a6ab41ffbc1946d5.arrow (7.95 KB)

cache-a34d90b946dc58b8.arrow (73.5 KB)

cache-a41fe1013beb0d46.arrow (73.5 KB)

cache-b4e51936648802e2.arrow (76.7 KB)

cache-b53b61aef9d859aa.arrow (73.5 KB)

cache-b85c50cd434dd865.arrow (73.5 KB)

cache-badcf79eb9fa62a0.arrow (73.5 KB)

cache-c5b262546ff026fd.arrow (76.7 KB)

cache-d7c6377d856b0538.arrow (73.5 KB)

cache-eb31b953e8e788fb.arrow (73.5 KB)

cache-fdafe12f59ab3430.arrow (73.5 KB)

dataset.arrow (2.96 MB)

dataset_info.json (1.79 KB)

state.json (256.0 B)

validation

cache-1b273238fdadead7.arrow (9.57 KB)

cache-4a46afc6455cc3f0.arrow (9.57 KB)

cache-5cd00431b9d3a916.arrow (9.57 KB)

cache-8ae76a3b52248a8f.arrow (9.57 KB)

cache-9b7789b1e0e636fb.arrow (9.57 KB)

cache-85b0ee5d0aeacbe5.arrow (9.57 KB)

cache-486e66fea9239c3f.arrow (9.57 KB)

cache-585e054403b710b3.arrow (9.57 KB)

cache-749efdb8f0ee42be.arrow (9.57 KB)

cache-805d054a96ccc48f.arrow (9.57 KB)

cache-428974171b74f1a0.arrow (9.57 KB)

cache-800047dd8fdede53.arrow (9.57 KB)

cache-a1c8a8bb0a669e93.arrow (9.57 KB)

cache-c072f719f0b9b62a.arrow (9.57 KB)

cache-e621cb96adf0c923.arrow (9.97 KB)

dataset.arrow (376.6 KB)

dataset_info.json (1.79 KB)

state.json (261.0 B)

dataset_dict.json (43.0 B)

Weibo

dataset_info.json (2.27 KB)

model

bert-base-chinese

.locks

models--bert-base-chinese

.no_exist

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

added_tokens.json

special_tokens_map.json

blobs_20241231_024804

refs

main (40.0 B)

snapshots

c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f

config.json (624.0 B)

model.safetensors (392.5 MB)

tokenizer.json (262.6 KB)

tokenizer_config.json (49.0 B)

vocab.txt (107.0 KB)

params

0_bert.pth (7.46 KB)

1_bert.pth (7.46 KB)

2_bert.pth (7.46 KB)

3_bert.pth (7.46 KB)

data_test.py (665.0 B)

MyData.py (856.0 B)

MyData02.py (589.0 B)

net.py (989.0 B)

run.py (1.66 KB)

test.py (2.14 KB)

token_test.py (1.47 KB)

train.py (4.34 KB)

vocab_test.py (1.13 KB)

【课件】Hugging Face 模型微调训练（自定义vocab）.pdf (509.3 KB)

【录播】自定义vocab.mp4 (921.7 MB)

day07_如何处理超长文本训练问题

demo_7

data

news

news_data_info.json (1.80 KB)

test.csv (24.1 MB)

train.csv (120.2 MB)

validation.csv (12.2 MB)

Weibo

dataset_info.json (2.27 KB)

data.py (171.0 B)

data_test.py (300.0 B)

data_test02.py (915.0 B)

MyData.py (581.0 B)

net.py (1.33 KB)

new_test.csv (18.3 KB)

train.py (4.33 KB)

validation.csv (18.4 KB)

model.zip (364.5 MB)

【课件】Hugging Face 模型微调训练（如何处理超长文本训练问题）.pdf (511.4 KB)

【录播】如何处理超长文本训练问题.mp4 (754.4 MB)

day08_GPT2-中文生成模型定制化微调训练

demo_8

.idea

inspectionProfiles

profiles_settings.xml (174.0 B)

.gitignore (50.0 B)

demo_8.iml (291.0 B)

misc.xml (189.0 B)

modules.xml (271.0 B)

workspace.xml (7.23 KB)

__pycache__

data.cpython-312.pyc (1.30 KB)

data

chinese_poems.txt (49.3 MB)

example

test01.py (864.0 B)

test02.py (835.0 B)

test03.py (824.0 B)

test04.py (844.0 B)

test05.py (818.0 B)

params

data.py (496.0 B)

train.py (4.58 KB)

【课件】Hugging Face 模型微调训练（GPT2-中文生成模型定制化微调训练）.pdf (510.3 KB)

【录播】GPT2-中文生成模型定制化微调训练.mp4 (871.5 MB)

day09_远程GPU服务器

代码与资料

GPT2训练日志及权重

net.pt (389.4 MB)

output.log (1.28 MB)

模型推理代码

detect.py (724.0 B)

detect02.py (4.50 KB)

GPU服务器配置与使用.pdf (697.1 KB)

1月8日.mp4 (749.4 MB)

未命名文档.PanD (93.0 B)

day10_llama3大模型本地调用

demo_10

Llama3_test

test01.py (165.0 B)

test02.py (1.24 KB)

data.py (501.0 B)

detect.py (709.0 B)

detect02.py (4.54 KB)

net.pt (389.4 MB)

train.py (3.17 KB)

【课件】llama3大模型本地调用.pdf (555.9 KB)

【录播】llama3大模型本地调用.mp4 (912.7 MB)

day11_Llama3.2模型微调

demo_11

.idea

inspectionProfiles

profiles_settings.xml (174.0 B)

.gitignore (50.0 B)

demo_11.iml (325.0 B)

misc.xml (168.0 B)

modules.xml (273.0 B)

workspace.xml (2.04 KB)

test01.py (336.0 B)

test02.py (1.14 KB)

data.zip (280.1 KB)

【课件】LLaMa3微调（使用 LLaMA-Factory 微调 LLaMA3）.pdf (602.0 KB)

【录播】llama3.2模型微调.mp4 (865.3 MB)

【资料】LLaMa3微调（使用 LLaMA-Factory 微调 LLaMA3）.pdf (140.9 KB)

day12_Lora模型合并与推理测试

checkpoint-800

adapter_config.json (754.0 B)

adapter_model.safetensors (21.5 MB)

optimizer.pt (43.2 MB)

README.md (5.01 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (650.0 B)

tokenizer.json (16.4 MB)

tokenizer_config.json (53.4 KB)

trainer_state.json (29.3 KB)

training_args.bin (5.49 KB)

data

ruozhiba_qaswift.json (588.5 KB)

【录播】Lora模型合并与推理测试.mp4 (906.6 MB)

day13_LLaMA-Factory模型导出量化

checkpoint-3700

adapter_config.json (754.0 B)

adapter_model.safetensors (21.5 MB)

optimizer.pt (43.2 MB)

README.md (5.01 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (439.0 B)

tokenizer.json (16.4 MB)

tokenizer_config.json (53.3 KB)

trainer_state.json (133.8 KB)

training_args.bin (5.43 KB)

demo_13

data

ruozhiba_qaswift.json (588.5 KB)

ruozhiba_qaswift_train.json (632.3 KB)

test01.py (724.0 B)

【课件】LLaMa3导出量化（LLaMA-Factory模型导出量化）.pdf (497.3 KB)

【录播】LLaMA-Factory模型导出量化.mp4 (1.06 GB)

【资料】LLaMa3导出量化（LLaMA-Factory模型导出量化）.pdf (769.0 KB)

day14_LLaMA-Factory模型评估与QLora微调

AI技术路线.pdf (107.4 KB)

【课件】LLama-Factory模型评估与QLora微调.pdf (512.7 KB)

【录播】LLama-Factory模型评估与QLora微调.mp4 (763.6 MB)

【资料】LLama-Factory模型评估.pdf (295.4 KB)

day15_Qwen模型打包部署（Lora模型合并&转GGUF模型部署）

Lora

checkpoint-400

adapter_config.json (744.0 B)

adapter_model.safetensors (228.8 MB)

added_tokens.json (80.0 B)

merges.txt (1.59 MB)

optimizer.pt (457.8 MB)

README.md (5.00 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (367.0 B)

tokenizer.json (10.9 MB)

tokenizer_config.json (1.29 KB)

trainer_state.json (15.1 KB)

training_args.bin (5.55 KB)

vocab.json (2.65 MB)

【课件】Qwen模型打包部署（Lora模型合并&转GGUF模型部署）.pdf (555.3 KB)

【录播】HF模型转GGUF以及使用ollama部署.mp4 (1.11 GB)

【资料】Qwen模型打包部署（Lora模型合并&转GGUF模型部署）.pdf (395.7 KB)

day16_Qwen模型打包部署（HF转GGUF&ollama open_webui部署）

Qwen1___5-1___8B-Chat-merged-q8.gguf (1.82 GB)

【课件】Qwen模型打包部署（Lora模型合并&转GGUF模型部署）.pdf (555.3 KB)

【录播】Qwen模型打包部署（HF转GGUF&ollama open_webui部署）.mp4 (848.7 MB)

【资料】Qwen模型打包部署（Lora模型合并&转GGUF模型部署）.pdf (395.7 KB)

day17_Xtuner微调大模型

xtuner数据集转换代码

data

ruozhiba_qaswift.json (588.5 KB)

target_data.json (714.2 KB)

data_utils.py (742.0 B)

xtuner微调配置文件

qwen1_5_1_8b_chat_qlora_alpaca_e3.py (7.56 KB)

【录播】Xtuner微调大模型（QLora与Lora）.mp4 (923.5 MB)

【资料】xtuner微调大模型教程.pdf (184.9 KB)

day18_LMDeploy部署大模型

demo_18

test01.py (457.0 B)

test02.py (440.0 B)

【录播】LMDeploy部署大模型.mp4 (952.8 MB)

【资料】LMDeploy部署大模型.pdf (327.0 KB)

day19_OpenCompass大模型评估

ptb

ptb_train

data-00000-of-00001.arrow (4.91 MB)

dataset_info.json (2.13 KB)

state.json (262.0 B)

ptb_val

data-00000-of-00001.arrow (394.6 KB)

dataset_info.json (2.13 KB)

state.json (267.0 B)

如果OpenCompassData-core-20240207.zip压缩包下载解压有问题就用当前目录对应的解压包

OpenCompassData-core-20240207.zip (148.9 MB)

【课件】OpenCompass模型评估.pdf (490.0 KB)

【录播】OpenCompass大模型评估.mp4 (850.4 MB)

【资料】OpenCompass模型评估.pdf (197.9 KB)

day20_llama-index核心组件

demo_20

data

pdf内容研报.pdf (189.1 KB)

README_zh-CN.md (14.5 KB)

requirements.txt (4.33 KB)

test01.py (365.0 B)

test02.py (222.0 B)

模型微调与RAG.png (104.6 KB)

【课件】Llama_Index（核心组件介绍）.pdf (486.3 KB)

【录播】Llama_Index核心组件介绍.mp4 (1.15 GB)

【资料】Llama_Index（核心组件介绍）.pdf (624.0 KB)

day21_llama-index入门实操

demo_21

data

README_zh-CN.md (14.5 KB)

download_hf.py (177.0 B)

test01.py (534.0 B)

test02.py (1.60 KB)

test03.py (746.0 B)

【课件】Llama_index入门实操.pdf (496.1 KB)

【录播】Llama_index入门实操.mp4 (934.0 MB)

day22_llama-index实现RAG

demo_22

data

pdf内容研报.pdf (189.1 KB)

README_zh-CN.md (12.7 KB)

storage

default__vector_store.json (175.9 KB)

docstore.json (74.5 KB)

graph_store.json (18.0 B)

image__vector_store.json (72.0 B)

index_store.json (1.88 KB)

app.py (2.70 KB)

download_hf.py (177.0 B)

test01.py (534.0 B)

test02.py (1.62 KB)

test03.py (956.0 B)

test04.py (2.14 KB)

【课件】Llama_index实现RAG.pdf (489.2 KB)

【录播】llama-index实现RAG.mp4 (1.31 GB)

day23_AutoGen_Studio搭建多智能体应用

图像资料

Agent01.png (121.1 KB)

Agent02.png (113.4 KB)

Agent03.png (31.7 KB)

【课件】AutoGen_Studio搭建多智能体应用.pdf (599.8 KB)

【录播】AutoGen_Studio搭建多智能体应用.mp4 (753.1 MB)

【资料】AutoGen_Studio搭建多智能体应用.pdf (859.3 KB)

day24_多模态大模型

笔记

多模态01.png (86.8 KB)

多模态02.png (127.3 KB)

【课件】多模态（多模态大模型的概念与本地部署调用）.pdf (731.9 KB)

【录播】多模态大模型的概念与本地部署调用.mp4 (704.8 MB)

【资料】多模态（多模态大模型的概念与本地部署调用）.pdf (4.90 MB)

day25_deep-seek与多卡训练

课堂笔记

deepseek.png (152.9 KB)

【课件】deepseek与分布式训练.pdf (491.8 KB)

【录播】deep_seek与多卡训练.mp4 (1.02 GB)

day26_基于本地大模型的AI试题系统（方案篇）

数据

2020年高考生物选择题专项训练11-15套Word版含答案及解析.docx (42.0 KB)

2020年高考生物选择题专项训练20套附答案及解析.docx (454.6 KB)

2022年高考生物选择题专项训练(共6份).docx (2.49 MB)

2023年高考生物选择题专练(8套)含答案及解析.docx (37.5 KB)

高考生物常识选择题单选题100道及答案.docx (27.3 KB)

数据示例.xls (19.5 KB)

AI题库项目分析.png (245.3 KB)

【录播】基于本地大模型的AI试题系统（方案篇）.mp4 (1.27 GB)

day27_基于本地大模型的AI试题系统（实现篇）

标注后的数据

高考生物选择题01.csv (77.1 KB)

高考生物选择题02.csv (35.0 KB)

Lora模型与训练日志

checkpoint-1300

adapter_config.json (762.0 B)

adapter_model.safetensors (140.9 MB)

optimizer.pt (282.1 MB)

README.md (5.02 KB)

rng_state.pth (13.9 KB)

scheduler.pt (1.04 KB)

special_tokens_map.json (485.0 B)

tokenizer.json (10.9 MB)

tokenizer_config.json (6.67 KB)

trainer_state.json (55.4 KB)

training_args.bin (5.62 KB)

nohup.out (262.2 KB)

training_args.yaml (766.0 B)

数据转换代码

data_utils.py (2.03 KB)

test_data.py (1.12 KB)

转换后的训练集与测试集

test.json (51.6 KB)

train.json (162.2 KB)

【录播】基于本地大模型的AI试题系统（实现篇）.mp4 (1.60 GB)

day28_基于RAG的法律条文智能助手（方案篇）

llama_factory对话模板导出

mytest.py (829.0 B)

文件位置.jpg (19.6 KB)

模型微调数据集

train_data.json (20.1 KB)

RAG知识库数据获取

data_test01.py (1.60 KB)

data_test02.py (1.59 KB)

R1思维链与微调.png (109.1 KB)

RAG项目需求.png (125.3 KB)

【课件】基于RAG的法律条文智能助手（方案篇）.pdf (495.8 KB)

【录播】基于RAG的法律条文智能助手【方案篇】.mp4 (1.06 GB)

day29_基于RAG的法律条文助手（实现篇）

项目源码

rag_law

data

data1.json (64.4 KB)

llama_index_llm.py (583.0 B)

llama_index_vllm.py (873.0 B)

rag_law.py (7.97 KB)

read_json.py (781.0 B)

【课件】基于RAG的法律条文智能助手（实现篇）.pdf (491.2 KB)

【录播】基于RAG的法律条文智能助手【实现篇】.mp4 (1.49 GB)

day30_基于pytorch的语音唤醒系统

项目源码

wakeup_test

.idea

inspectionProfiles

profiles_settings.xml (174.0 B)

.gitignore (50.0 B)

misc.xml (189.0 B)

modules.xml (281.0 B)

wakeup_test.iml (291.0 B)

workspace.xml (6.42 KB)

__pycache__

audio_processor.cpython-312.pyc (3.87 KB)

crnn.cpython-312.pyc (2.46 KB)

dataset.cpython-312.pyc (4.78 KB)

checkpoints

best_model.pth (3.45 MB)

dataset

split_dataset

train

not_wake

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val

not_wake

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wake

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train

not_wake

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wake

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results

confusion_matrix.png (15.9 KB)

errors.txt

audio_processor.py (2.45 KB)

crnn.py (1.48 KB)

data_splite.py (1.40 KB)

dataset.py (3.31 KB)

get_voice.py (2.04 KB)

realtime_test.py (3.36 KB)

test.py (2.24 KB)

train.py (4.20 KB)

语音唤醒.png (171.2 KB)

【课件】扩展项目（基于pytorch实现的语音识别）.pdf (487.9 KB)

【录播】扩展项目（基于pytorch的语音唤醒系统）.mp4 (1.67 GB)

AI大模型学习路径.pdf (676.5 KB)

大神指南.docx (1.44 MB)

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