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2024黑马博学谷AI大模型训练营1期是一门专注于AI大模型领域的课程。课程内容丰富全面，从开班仪式与Python前置课程串讲出发，深入讲解大模型前置知识、基础知识、主要类别架构等理论内容。在模型介绍方面，涵盖主流大模型，并对大模型Prompt-Tuning方法进行从入门到进阶的教学，还涉及大模型提示词工程应用。项目实践是课程一大亮点，设有金融行业动态风向评估、电商领域虚拟试衣系统、物流行业信息咨询智能问答系统、大健康行业智能问诊系统、新零售行业评价决策系统、新媒体行业评论智能分类与信息抽取系统等多个项目，让学员在实践中巩固所学知识。此外，课程还包含Stable Diffusion多模态大模型应用实战，以及文心一言&百度千帆大模型平台、讯飞星火大模型+星火微调平台应用等内容，帮助学员拓宽对不同大模型平台的认知与应用能力。
星火大模型（博学谷）.pdf12.1MB
translate_in_many_style.zip79.31MB清洗emoji数据的demo数据集.zip219.67KBsample-text-dialog-unsort-jsonl.zip154.3KB03-千帆大模型的使用.zip12.77MB02-千帆大模型简介.pdf14.33MB01-文心一言的使用.zip22MB
model-plaidshirtprogrammer.ckpt1.99GBglass.safetensors36.11MB
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wechat.jpg150.95KBwebglm.jpg106.44KBweb-demo.png586.92KB
web-demo.gif2.18MBvisualglm.png247.27KBenglish-q4-old.png170.61KBenglish-q4-new.png176.96KBenglish-q3-old.png104.22KBenglish-q3-new.png98.71KBenglish-q2-old.png112.26KBenglish-q2-new.png74KBenglish-q1-old.png73.31KBenglish-q1-new.png105.43KBcli-demo.png463.04KBweb_demo.sh217Bweb_demo.py5.55KBtrain_chat.sh745Btrainer_seq2seq.py11.23KBtrainer.py181.25KBtrain.sh753BREADME_en.md11.18KBREADME.md10.55KBmain.py18.17KBevaluate_finetune.sh562Bevaluate.sh660Bds_train_finetune.sh766Bdeepspeed.json509Barguments.py8.28KBself-confusion_tencent.jpg125.37KBself-confusion_openai.jpg142.77KBself-confusion_google.jpg152.18KBmath_error.png25.18KBfactual_error.png134.51KBREADME.md3.69KBdata_sample.jsonl51.1KBtour-guide.png331.5KBsport.png291.48KBself-introduction.png231.22KBrole-play.png278.51KBinformation-extraction.png130.92KBemail-writing-2.png223.95KBemail-writing-1.png230.37KBcomments-writing.png260.1KBblog-outline.png162.27KBad-writing-2.png122.74KBweb_demo_vision.py4.52KBweb_demo_old.py1.96KBweb_demo2.py2.14KBweb_demo.py3.83KButils.py1.98KBUPDATE.md6.25KBrequirements.txt96BREADME_en.md20.17KBREADME.md22.55KBPROJECT.md4.5KBMODEL_LICENSE4.17KBLICENSE11.07KBFAQ.md940Bcli_demo_vision.py1.94KBcli_demo.py1.87KBapi.py1.82KB02-金融行业动态方向评估项目.pdf2.07MB01-大模型提示工程指南.pdf3.38MB02-大模型提示工程指南.pdf3.46MB01-大模型prompt-Tuning方法进阶.pdf3.09MB02-LLM主要架构介绍.pdf3.24MB01-ChatGPT模型原理介绍.pdf4.51MB作业.txt46B01-LLM基础知识.pdf3.24MB__init__.py0BROUGE_demo.py655BPPL_demo.py631BBLEU_demo.py939B指标解析.png732.42KB项目开发人员配置.jpg331.63KB神经网络语言模型介绍.png1.09MBPPL公式解析.png704.33KB大模型项目研发流程.pdf279.65KB01-PyTorch基本使用.pdf3.79MB00-深度学习简介.pdf1.52MB
1-9 大模型提示词工程应用.mp4944.25MB1-8 大模型Prompt-Tuning方法进阶.mp4814.26MB1-7 主流大模型介绍及大模型Prompt-Tuning方法入门.mp4663.93MB1-6 主流大模型介绍及大模型Prompt-Tuning方法入门2.mp4593.42MB1-6 主流大模型介绍及大模型Prompt-Tuning方法入门.mp4276.36MB1-5 大模型主要类别架构.mp4410.13MB1-4 大模型基础知识.mp4429.47MB1-35 综合项目与项目路演+【拓展】AI论文导读与论文撰写.mp4356.81MB1-34 讯飞星火大模型+星火微调平台应用.mp4249.28MB1-33 讯飞星火大模型+星火微调平台应用.mp4370.1MB1-32 文心一言& 百度千帆大模型平台.mp4278.41MB1-31 文心一言& 百度千帆大模型平台.mp4206.23MB1-30 Stable Diffusion多模态大模型应用实战.mp4375.83MB1-3 大模型前置知识.mp4264.63MB1-29 Stable Diffusion多模态大模型应用实战.mp4579.4MB1-28 Stable Diffusion多模态大模型应用实战.mp4240.2MB1-27 Stable Diffusion多模态大模型应用实战.mp4352.22MB1-26 Stable Diffusion多模态大模型应用实战.mp4374.41MB1-25 【项目6】新媒体行业评论智能分类与信息抽取系统.mp4950.89MB1-24 【项目6】新媒体行业评论智能分类与信息抽取系统.mp4934.08MB1-23【项目5-2】新零售行业评价决策系统【基于BERT+P-Tuning方式】.mp4629.74MB1-22【项目5-2】新零售行业评价决策系统【基于BERT+P-Tuning方式】.mp41.33GB1-21【项目5-1】新零售行业评价决策系统【基于BERT+PET方式】.mp4836.85MB1-2 大模型前置知识.mp4534.85MB1-19 20 【项目4】大健康行业智能问诊系统.mp4395.83MB1-18 【项目4】大健康行业智能问诊系统(2024.03.10).mp4909.01MB1-17 【项目3】物流行业信息咨询智能问答系统(2024.03.07).mp4783.72MB1-16 【项目3】物流行业信息咨询智能问答系统 2024.03.05.mp4794.24MB1-15 【项目3】物流行业信息咨询智能问答系统.mp4468.04MB1-14 【项目3】物流行业信息咨询智能问答系统.mp41.48GB1-13 【项目2】电商领域虚拟试衣系统.mp4528.64MB1-12 【项目2】电商领域虚拟试衣系统.mp4397.96MB1-11 【项目1】金融行业动态风向评估.mp4662.98MB1-10 【项目1】金融行业动态风向评估.mp41.43GB1-1 开班仪式+Python前置课程串讲.mp4284.13MB
