2025年聚客大模型第三期(聚客第一第二、第三期) 大模型应用实战,掌握聚客大模型核心技巧 编辑点评 紧跟2025前沿技术,深入解析聚客大模型,实战性强,助你成为AI应用高手。 ⭐ 编辑推荐 2025年聚客大模型第三期课程,涵盖聚客第一、第二期精华,实战演练,提升AI应用能力。 ✨ 课程亮点 ✦ 前沿技术解析 ✦ 实战演练丰富 ✦ 提升AI应用能力 课程目…
大模型应用实战,掌握聚客大模型核心技巧
紧跟2025前沿技术,深入解析聚客大模型,实战性强,助你成为AI应用高手。
2025年聚客大模型第三期课程,涵盖聚客第一、第二期精华,实战演练,提升AI应用能力。
📁 01_AI及LLM基础 📁 📁 day02_OpenAI 开发 day2-demo.zip [766.1 KB] 【语雀】OpenAI 开发.txt [99.0 B] 【MD】OpenAI 开发.md [6.9 KB] 【资料】OpenAI 开发.pdf [642.8 KB] 【课件】OpenAI 开发.pdf [1.6 MB] 📁 📁 day01_AI领域基础概念 【语雀】AI 领域基础概念.txt [110.0 B] OpenAI.apifox.json [244.5 KB] OpenAI-HK 操作指南.pdf [151.1 KB] 【录播】AI 领域基础概念.mp4 [1.1 GB] day1-demo.zip [14.3 KB] 【资料】AI 领域基础概念.pdf [1.7 MB] 【MD】AI 领域基础概念.md [49.9 KB] 【课件】AI 领域基础概念.pdf [7.6 MB] 📁 📁 day03_支持多模态输入的 AI Chatbot App 【课件】支持多模态输入的 AI Chatbot App.pdf [839.0 KB] day3-demo.zip [787.7 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 [2.9 MB] 【录播】支持多模态输入的 AI Chatbot App.mp4 [1.5 GB] 【MD】支持多模态输入的 AI Chatbot App.md [3.6 KB] 📁 02_Prompt基础 📁 📁 day04_Prompt Engineering 提示词工程 【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] 【课件】Prompt Engineering 提示词工程.pdf [826.4 KB] 【录播】Prompt Engineering 提示词工程.mp4 [1.3 GB] DALL-E-3绘图提示词大全.pdf [14.3 MB] 【资料】Prompt Engineering 提示词工程.pdf [2.6 MB] ChatGPT提示技巧工程完全指南.pdf [2.9 MB] 实用Prompt指令大全.xlsx [6.2 MB] day4-demo.zip [106.0 KB] 02_Prompt基础资料.zip [1.8 MB] 📁 11_Llama3 📁 📁 day_19LLaMa3微调_使用 LLaMA-Factory微调Llama3 銆愬綍鎾€慙LaMA_Factory寰皟Llama3.mp4 [881.6 MB] 【课件】LLaMa3微调(使用 LLaMA-Factory 微调 LLaMA3).pdf [602.0 KB] data.zip [280.1 KB] demo_19.zip [4.3 KB] 【资料】LLaMa3微调(使用 LLaMA-Factory 微调 LLaMA3).pdf [140.9 KB] 📁 📁 day_21LLaMa3打包部署(LLaMA-Factory模型评估与量化) 【课件】LLaMa3打包部署(LLaMA-Factory模型评估与量化).pdf [497.1 KB] 【资料】LLaMa3打包部署(LLaMA-Factory模型评估与量化).pdf [997.2 KB] day_21LLaMa3打包部署(LLaMA-Factory模型评估与量化)说明.zip [1.8 MB] 【录播】LLaMa3打包部署(LLaMA-Factory模型评估与量化).mp4 [899.0 MB] 📁 📁 day_20LLaMa3打包部署教程 (Lora 微调与模型合并) 【课件】LLaMa3打包部署(Lora微调与模型合并部署).pdf [493.1 KB] 【资料】LLaMa3 打包部署教程 (Lora 微调与模型合并部署).pdf [782.6 KB] 【录播】LLaMa3打包部署教程 (Lora 微调与模型合并).mp4 [1.8 GB] demo_20.zip [4.3 KB] 📁 📁 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] 【资料】Llama3大模型本地部署与调用(1).pdf [1.5 MB] 【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.pdf [1.2 MB] 📁 📁 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-150 trainer_state.json [6.0 KB] README.md [5.0 KB] optimizer.pt [160.4 MB] tokenizer.json [16.4 MB] training_args.bin [5.3 KB] special_tokens_map.json [325.0 B] adapter_model.safetensors [80.1 MB] tokenizer_config.json [49.9 KB] scheduler.pt [1.0 KB] rng_state.pth [13.9 KB] adapter_config.json [763.0 B] 📁 📁 checkpoint-100 optimizer.pt [160.4 MB] training_args.bin [5.3 KB] README.md [5.0 KB] tokenizer_config.json [49.9 KB] tokenizer.json [16.4 MB] special_tokens_map.json [325.0 B] adapter_config.json [763.0 B] adapter_model.safetensors [80.1 MB] trainer_state.json [4.3 KB] scheduler.pt [1.0 KB] rng_state.pth [13.9 KB] tokenizer.json [16.4 MB] README.md [1.6 KB] trainer_log.jsonl [5.9 KB] adapter_config.json [763.0 B] all_results.json [359.0 B] training_eval_loss.png [23.1 KB] tokenizer_config.json [49.9 KB] train_results.json [220.0 B] training_args.yaml [844.0 B] trainer_state.json [6.2 KB] llamaboard_config.yaml [1.7 KB] training_args.bin [5.3 KB] running_log.txt [15.7 KB] special_tokens_map.json [325.0 B] training_loss.png [43.9 KB] eval_results.json [174.0 B] adapter_model.safetensors [80.1 MB] 【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] 【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] 【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] 【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] 11_Llama3资料.zip [1.8 MB] 📁 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) 【录播】多模态大模型的概念与本地部署调用.mp4 [1.2 GB] 【资料】多模态(多模态大模型的概念与本地部署调用).pdf [4.9 MB] day_23多模态(多模态大模型的概念与本地部署调用)说明.zip [1.8 MB] 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] 📁 08_LangGraph 📁 📁 day12_LangGraph 銆愬綍鎾€慙angGraph.mp4 [798.7 MB] 【资料】LangGraph.pdf [2.5 MB] 【MD】LangGraph.md [129.8 KB] 【课件】LangGraph.pdf [866.0 KB] 【语雀】LangGraph.txt [95.0 B] 📁 16_项目实战(聚客第三期_最新) 📁 📁 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] 【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] 【课件】AutoGen_Studio搭建多智能体应用.pdf [599.8 KB] 📁 📁 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] rag_law.py [8.0 KB] llama_index_llm.py [583.0 B] llama_index_vllm.py [873.0 B] 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 [1.5 GB] 📁 📁 day26_基于本地大模型的AI试题系统(方案篇) 📁 📁 数据 数据示例.xls [19.5 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] 2023年高考生物选择题专练(8套)含答案及解析.docx [37.5 KB] 2022年高考生物选择题专项训练(共6份).docx [2.5 MB] 高考生物常识选择题单选题100道及答案.docx [27.3 KB] 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] 【录播】基于本地大模型的AI试题系统(方案篇).mp4 [1.3 GB] AI题库项目分析.png [245.3 KB] 📁 📁 day27_基于本地大模型的AI试题系统(实现篇) 📁 📁 数据转换代码 test_data.py [1.1 KB] data_utils.py [2.0 KB] 📁 📁 标注后的数据 高考生物选择题02.csv [35.0 KB] 高考生物选择题01.csv [77.1 KB] 📁 📁 转换后的训练集与测试集 test.json [51.6 KB] train.json [162.2 KB] 📁 📁 Lora模型与训练日志 📁 📁 checkpoint-1300 scheduler.pt [1.0 KB] README.md [5.0 KB] training_args.bin [5.6 KB] rng_state.pth [13.9 KB] tokenizer.json [10.9 MB] tokenizer_config.json [6.7 KB] adapter_model.safetensors [140.9 MB] adapter_config.json [762.0 B] special_tokens_map.json [485.0 B] trainer_state.json [55.4 KB] optimizer.pt [282.1 MB] nohup.out [262.2 KB] training_args.yaml [766.0 B] 【录播】基于本地大模型的AI试题系统(实现篇).mp4 [1.6 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 [1.6 KB] data_test02.py [1.6 KB] 【录播】基于RAG的法律条文智能助手【方案篇】.mp4 [1.1 GB] 【课件】基于RAG的法律条文智能助手(方案篇).pdf [495.8 KB] RAG项目需求.png [125.3 KB] R1思维链与微调.png [109.1 KB] 📁 📁 day07_如何处理超长文本训练问题 📁 📁 demo_7 📁 📁 data 📁 📁 news news_data_info.json [1.8 KB] test.csv [24.1 MB] train.csv [120.2 MB] validation.csv [12.2 MB] 📁 📁 Weibo dataset_info.json [2.3 KB] train.csv [4.0 MB] new_test.csv [18.3 KB] train.py [4.3 KB] MyData.py [581.0 B] data_test02.py [915.0 B] data_test.py [300.0 B] validation.csv [18.4 KB] data.py [171.0 B] net.py [1.3 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] 📁 📁 day19_OpenCompass大模型评估 📁 📁 OpenCompassData-core-20240207 📁 📁 data 📁 📁 tydiqa 📁 📁 dev finnish-dev.jsonl [726.5 KB] arabic-dev.jsonl [1.2 MB] bengali-dev.jsonl [248.2 KB] thai-dev.jsonl [1.4 MB] swahili-dev.jsonl [330.4 KB] english-dev.jsonl [437.6 KB] telugu-dev.jsonl [1.1 MB] russian-dev.jsonl [1.2 MB] swahili-dev(1).jsonl [330.4 KB] indonesian-dev.jsonl [547.9 KB] korean-dev.jsonl [277.6 KB] 📁 📁 BBH 📁 📁 data web_of_lies.json [57.1 KB] boolean_expressions.json [18.2 KB] snarks.json [45.5 KB] navigate.json [59.3 KB] penguins_in_a_table.json [75.9 KB] movie_recommendation.json [61.7 KB] README.md [3.9 KB] logical_deduction_five_objects.json [157.2 KB] date_understanding.json [65.7 KB] object_counting.json [40.4 KB] ruin_names.json [57.3 KB] causal_judgement.json [202.2 KB] tracking_shuffled_objects_three_objects.json [131.1 KB] multistep_arithmetic_two.json [19.7 KB] hyperbaton.json [49.0 KB] disambiguation_qa.json [88.6 KB] tracking_shuffled_objects_seven_objects.json [215.3 KB] logical_deduction_three_objects.json [115.0 KB] reasoning_about_colored_objects.json [101.8 KB] geometric_shapes.json [80.8 KB] temporal_sequences.json [150.1 KB] salient_translation_error_detection.json [285.1 KB] sports_understanding.json [33.3 KB] logical_deduction_seven_objects.json [199.1 KB] formal_fallacies.json [147.5 KB] word_sorting.json [70.1 KB] dyck_languages.json [48.2 KB] tracking_shuffled_objects_five_objects.json [171.1 KB] 📁 📁 lib_prompt tracking_shuffled_objects_three_objects.txt [2.5 KB] causal_judgement.txt [3.6 KB] hyperbaton.txt [3.0 KB] tracking_shuffled_objects_seven_objects.txt [2.5 KB] snarks.txt [3.0 KB] salient_translation_error_detection.txt [6.0 KB] dyck_languages.txt [2.3 KB] logical_deduction_five_objects.txt [2.4 KB] boolean_expressions.txt [1.7 KB] sports_understanding.txt [820.0 B] date_understanding.txt [1.1 KB] ruin_names.txt [3.4 KB] word_sorting.txt [2.1 KB] web_of_lies.txt [2.9 KB] penguins_in_a_table.txt [2.3 KB] reasoning_about_colored_objects.txt [2.2 KB] logical_deduction_three_objects.txt [2.4 KB] tracking_shuffled_objects_five_objects.txt [2.5 KB] disambiguation_qa.txt [3.5 KB] object_counting.txt [1.4 KB] multistep_arithmetic_two.txt [2.3 KB] navigate.txt [2.1 KB] logical_deduction_seven_objects.txt [2.4 KB] movie_recommendation.txt [2.1 KB] formal_fallacies.txt [4.4 KB] temporal_sequences.txt [3.0 KB] geometric_shapes.txt [4.7 KB] 📁 📁 LCSTS test.src.txt [3.2 MB] 📁 📁 SuperGLUE 📁 📁 BoolQ val.jsonl [2.2 MB] test.jsonl [2.1 MB] 📁 📁 WSC val.jsonl [30.6 KB] test.jsonl [41.7 KB] 📁 📁 WiC test.jsonl [292.1 KB] val.jsonl [144.1 KB] 📁 📁 ReCoRD test.jsonl [13.0 MB] val.jsonl [14.5 MB] 📁 📁 COPA val.jsonl [17.7 KB] test.jsonl [80.8 KB] 📁 📁 CB val.jsonl [24.0 KB] test.jsonl [97.5 KB] 📁 📁 AX-b AX-b.jsonl [346.0 KB] 📁 📁 AX-g AX-g.jsonl [73.2 KB] 📁 📁 MultiRC test.jsonl [960.8 KB] val.jsonl [537.2 KB] 📁 📁 RTE val.jsonl [102.4 KB] 📁 📁 triviaqa triviaqa-validation.jsonl [3.2 MB] trivia-dev.qa.csv [3.0 MB] triviaqa-train.jsonl [7.2 MB] trivia-test.qa.csv [3.9 MB] 📁 📁 hellaswag hellaswag_train.jsonl [45.3 MB] hellaswag.jsonl [7.9 MB] hellaswag_val_contamination_annotations.json [225.9 KB] hellaswag_train_sampled25.jsonl [9.2 KB] 📁 📁 siqa train-labels.lst [65.3 KB] dev-labels.lst [3.8 KB] train.jsonl [7.7 MB] dev.jsonl [463.3 KB] 📁 📁 summedits summedits.jsonl [26.7 MB] 📁 📁 humaneval human-eval-v2-20210705.jsonl [209.4 KB] 📁 📁 piqa train.jsonl [4.9 MB] train-labels.lst [31.5 KB] dev.jsonl [566.9 KB] dev-labels.lst [3.6 KB] 📁 📁 cmmlu 📁 📁 dev college_actuarial_science.csv [813.0 B] traditional_chinese_medicine.csv [304.0 B] elementary_mathematics.csv [357.0 B] college_law.csv [832.0 B] legal_and_moral_basis.csv [677.0 B] business_ethics.csv [424.0 B] chinese_foreign_policy.csv [1.1 KB] human_sexuality.csv [508.0 B] logical.csv [478.0 B] high_school_biology.csv [1.1 KB] college_engineering_hydrology.csv [513.0 B] machine_learning.csv [722.0 B] high_school_physics.csv [978.0 B] arts.csv [388.0 B] sports_science.csv [463.0 B] college_mathematics.csv [832.0 B] virology.csv [430.0 B] professional_medicine.csv [429.0 B] elementary_commonsense.csv [358.0 B] marketing.csv [598.0 B] computer_security.csv [654.0 B] clinical_knowledge.csv [726.0 B] genetics.csv [508.0 B] world_religions.csv [384.0 B] nutrition.csv [440.0 B] chinese_driving_rule.csv [688.0 B] ethnology.csv [429.0 B] high_school_mathematics.csv [468.0 B] professional_law.csv [805.0 B] education.csv [448.0 B] electrical_engineering.csv [442.0 B] elementary_chinese.csv [446.0 B] chinese_teacher_qualification.csv [835.0 B] chinese_literature.csv [484.0 B] professional_accounting.csv [547.0 B] sociology.csv [468.0 B] public_relations.csv [466.0 B] high_school_chemistry.csv [917.0 B] high_school_politics.csv [1.4 KB] chinese_history.csv [1.1 KB] construction_project_management.csv [556.0 B] chinese_civil_service_exam.csv [1.1 KB] agronomy.csv [421.0 B] security_study.csv [569.0 B] modern_chinese.csv [565.0 B] college_medical_statistics.csv [665.0 B] global_facts.csv [562.0 B] astronomy.csv [440.0 B] conceptual_physics.csv [1.1 KB] college_medicine.csv [432.0 B] world_history.csv [1.5 KB] food_science.csv [437.0 B] marxist_theory.csv [635.0 B] college_education.csv [593.0 B] anatomy.csv [349.0 B] economics.csv [586.0 B] philosophy.csv [511.0 B] high_school_geography.csv [613.0 B] elementary_information_and_technology.csv [436.0 B] computer_science.csv [441.0 B] international_law.csv [601.0 B] professional_psychology.csv [498.0 B] management.csv [535.0 B] chinese_food_culture.csv [439.0 B] jurisprudence.csv [472.0 B] journalism.csv [415.0 B] ancient_chinese.csv [700.0 B] 📁 📁 test international_law.csv [40.8 KB] food_science.csv [17.2 KB] security_study.csv [24.4 KB] arts.csv [18.0 KB] journalism.csv [25.0 KB] elementary_commonsense.csv [23.3 KB] genetics.csv [30.3 KB] virology.csv [26.0 KB] high_school_politics.csv [58.0 KB] traditional_chinese_medicine.csv [23.0 KB] high_school_physics.csv [29.5 KB] modern_chinese.csv [28.0 KB] college_mathematics.csv [29.8 KB] ethnology.csv [20.8 KB] sociology.csv [33.8 KB] professional_medicine.csv [54.9 KB] high_school_geography.csv [28.2 KB] chinese_history.csv [119.0 KB] chinese_teacher_qualification.csv [44.0 KB] jurisprudence.csv [124.8 KB] economics.csv [28.4 KB] college_education.csv [23.0 KB] professional_psychology.csv [34.3 KB] high_school_mathematics.csv [22.2 KB] chinese_foreign_policy.csv [40.0 KB] agronomy.csv [21.6 KB] conceptual_physics.csv [42.5 KB] college_medicine.csv [46.6 KB] global_facts.csv [24.8 KB] marketing.csv [33.1 KB] high_school_biology.csv [68.9 KB] philosophy.csv [17.5 KB] college_medical_statistics.csv [20.7 KB] public_relations.csv [27.9 KB] chinese_driving_rule.csv [22.0 KB] chinese_civil_service_exam.csv [74.6 KB] business_ethics.csv [31.7 KB] electrical_engineering.csv [29.7 KB] world_religions.csv [18.9 KB] college_law.csv [30.1 KB] professional_law.csv [66.1 KB] chinese_food_culture.csv [18.9 KB] elementary_chinese.csv [38.1 KB] high_school_chemistry.csv [46.3 KB] ancient_chinese.csv [26.5 KB] management.csv [35.3 KB] education.csv [22.1 KB] chinese_literature.csv [32.6 KB] legal_and_moral_basis.csv [52.1 KB] anatomy.csv [15.1 KB] college_engineering_hydrology.csv [18.8 KB] human_sexuality.csv [19.2 KB] professional_accounting.csv [30.1 KB] construction_project_management.csv [25.2 KB] astronomy.csv [29.0 KB] machine_learning.csv [27.7 KB] sports_science.csv [22.4 KB] marxist_theory.csv [37.8 KB] elementary_information_and_technology.csv [37.0 KB] computer_security.csv [42.8 KB] world_history.csv [62.8 KB] college_actuarial_science.csv [23.2 KB] logical.csv [21.0 KB] clinical_knowledge.csv [75.3 KB] nutrition.csv [20.8 KB] computer_science.csv [29.5 KB] elementary_mathematics.csv [30.6 KB] 📁 📁 strategyqa strategyQA_train.json [1.2 MB] 📁 📁 openbookqa 📁 📁 Main dev.tsv [150.6 KB] openbook.txt [74.1 KB] train.jsonl [1.4 MB] test.jsonl [149.7 KB] train.tsv [1.4 MB] dev.jsonl [153.3 KB] test.tsv [143.4 KB] 📁 📁 Additional crowdsourced-facts.txt [196.8 KB] dev_complete.jsonl [223.1 KB] test_complete.jsonl [218.0 KB] train_complete.jsonl [2.1 MB] 📁 📁 CLUE 📁 📁 C3 dev_0.json [794.4 KB] m-dev.json [1.1 MB] d-dev.json [794.4 KB] 📁 📁 OCNLI dev.json [954.5 KB] 📁 📁 cmnli 📁 📁 cmnli_public dev.json [2.4 MB] 📁 📁 CMRC test_public.json [3.1 MB] dev.json [3.1 MB] 📁 📁 AFQMC dev.json [538.5 KB] test_public.json [538.5 KB] 📁 📁 DRCD dev.json [3.0 MB] test_public.json [3.0 MB] 📁 📁 lambada test.jsonl [1.8 MB] 📁 📁 ARC 📁 📁 ARC-c ARC-Challenge-Test.jsonl [481.2 KB] ARC-Challenge-Dev.jsonl [123.6 KB] ARC_c_test_contamination_annotations.json [64.2 KB] 📁 📁 ARC-e ARC-Easy-Test.jsonl [874.1 KB] ARC-Easy-Dev.jsonl [209.4 KB] 📁 📁 commonsenseqa test_rand_split_no_answers.jsonl [413.2 KB] dev_rand_split.jsonl [460.6 KB] train_rand_split.jsonl [3.6 MB] 📁 📁 gsm8k test_socratic.jsonl [950.2 KB] train.jsonl [4.0 MB] test.jsonl [732.2 KB] train_socratic.jsonl [5.2 MB] 📁 📁 xstory_cloze ar_eval.jsonl [908.0 KB] ru_train.jsonl [251.3 KB] te_eval.jsonl [1.4 MB] eu_eval.jsonl [750.7 KB] te_train.jsonl [340.1 KB] hi_eval.jsonl [1.3 MB] my_eval.jsonl [1.8 MB] my_train.jsonl [425.2 KB] sw_eval.jsonl [738.8 KB] es_eval.jsonl [749.0 KB] es_train.jsonl [179.1 KB] ru_eval.jsonl [1.0 MB] eu_train.jsonl [181.5 KB] sw_train.jsonl [177.8 KB] en_train.jsonl [168.8 KB] ar_train.jsonl [220.1 KB] zh_train.jsonl [167.0 KB] zh_eval.jsonl [698.9 KB] id_train.jsonl [181.4 KB] hi_train.jsonl [324.2 KB] id_eval.jsonl [759.1 KB] en_eval.jsonl [706.8 KB] 📁 📁 FewCLUE 📁 📁 ocnli train_3.json [9.5 KB] dev_few_all.json [55.1 KB] unlabeled.json [5.5 MB] dev_3.json [10.8 KB] test_public.json [861.3 KB] test.json [445.3 KB] dev_4.json [11.1 KB] train_2.json [9.4 KB] train_1.json [10.1 KB] dev_0.json [10.7 KB] train_few_all.json [48.2 KB] train_4.json [9.6 KB] dev_1.json [11.5 KB] train_0.json [9.5 KB] dev_2.json [10.9 KB] 📁 📁 cluewsc train_1.json [10.7 KB] test.json [86.6 KB] dev_1.json [10.9 KB] test_public.json [314.2 KB] train_4.json [10.2 KB] dev_0.json [10.1 KB] dev_few_all.json [52.4 KB] unlabeled.json dev_3.json [10.9 KB] train_3.json [10.5 KB] dev_2.json [10.1 KB] label_distribution.json [407.0 B] train_few_all.json [52.1 KB] train_0.json [10.6 KB] dev_4.json [10.7 KB] train_2.json [9.9 KB] 📁 📁 csldcp dev_2.json [323.7 KB] dev_few_all.json [1.2 MB] labelDesc2label.py [2.5 KB] labels_all.txt [1.1 KB] dev_3.json [324.8 KB] train_1.json [327.1 KB] train_2.json [317.2 KB] dev_4.json [311.1 KB] dev_1.json [318.1 KB] test.json [1.7 MB] train_4.json [321.4 KB] test_public.json [1.0 MB] dev_0.json [322.0 KB] train_few_all.json [1.2 MB] train_0.json [322.2 KB] train_3.json [319.0 KB] unlabeled.json [10.1 MB] 📁 📁 chid train_few_all.json [92.8 KB] dev_few_all.json [91.8 KB] dev_0.json [19.0 KB] dev_4.json [19.2 KB] train_4.json [19.5 KB] dev_3.json [18.9 KB] train_2.json [19.3 KB] train_0.json [19.1 KB] dev_2.json [19.2 KB] train_3.json [18.9 KB] unlabeled.json [3.3 MB] test.json [891.6 KB] test_public.json [918.2 KB] train_1.json [19.6 KB] dev_1.json [19.2 KB] 📁 📁 bustm unlabeled.json [372.6 KB] train_few_all.json [16.0 KB] dev_1.json [3.1 KB] train_1.json [3.2 KB] dev_3.json [3.2 KB] train_3.json [3.2 KB] train_4.json [3.3 KB] train_2.json [3.2 KB] test_public.json [180.5 KB] test.json [175.5 KB] dev_0.json [3.2 KB] dev_4.json [3.2 KB] train_0.json [3.1 KB] dev_few_all.json [16.1 KB] dev_2.json [3.3 KB] 📁 📁 csl train_3.json [23.5 KB] train_0.json [27.0 KB] dev_3.json [29.8 KB] dev_0.json [23.8 KB] train_2.json [25.5 KB] unlabeled.json [15.9 MB] test_public.json [2.3 MB] dev_1.json [26.3 KB] dev_4.json [27.1 KB] train_few_all.json [130.0 KB] dev_few_all.json [131.8 KB] dev_2.json [24.6 KB] train_4.json [26.8 KB] train_1.json [27.2 KB] test.json [2.4 MB] 📁 📁 eprstmt dev_3.json [4.7 KB] dev_4.json [5.8 KB] dev_public.json [634.0 B] test.json [127.6 KB] test_public.json [111.9 KB] train_0.json [4.8 KB] train_3.json [5.5 KB] train_2.json [6.2 KB] dev_0.json [6.6 KB] unlabeled.json [3.1 MB] train_1.json [5.7 KB] dev_few_all.json [30.4 KB] dev_2.json [7.0 KB] train_few_all.json [27.2 KB] train_4.json [4.8 KB] dev_1.json [6.1 KB] 📁 📁 iflytek train_4.json [817.8 KB] dev_4.json [596.5 KB] train_1.json [801.1 KB] dev_0.json [593.0 KB] train_3.json [819.8 KB] train_few_all.json [2.6 MB] dev_3.json [608.4 KB] dev_1.json [590.6 KB] dev_few_all.json [1.1 MB] dev_2.json [598.5 KB] test.json [2.0 MB] train_0.json [815.7 KB] label_id2des_desc2short.py [5.3 KB] train_2.json [832.6 KB] test_public.json [1.5 MB] unlabeled.json [5.8 MB] 📁 📁 tnews train_few_all.json [225.2 KB] test_public.json [177.1 KB] label_index2en2zh.json [992.0 B] train_3.json [46.0 KB] train_2.json [43.8 KB] train_0.json [46.8 KB] train_1.json [45.4 KB] dev_0.json [45.9 KB] unlabeled.json [3.0 MB] test.json [136.9 KB] dev_4.json [45.2 KB] dev_2.json [45.4 KB] dev_1.json [46.0 KB] dev_few_all.json [209.4 KB] dev_3.json [46.5 KB] train_4.json [46.1 KB] readme.md [906.0 B] 📁 📁 AGIEval 📁 📁 data 📁 📁 v1 lsat-lr.jsonl [601.2 KB] jec-qa-ca.jsonl [705.9 KB] gaokao-chemistry.jsonl [135.0 KB] math.jsonl [881.5 KB] logiqa-zh.jsonl [502.3 KB] gaokao-geography.jsonl [113.7 KB] gaokao-physics.jsonl [113.4 KB] gaokao-mathcloze.jsonl [37.8 KB] sat-en-without-passage.jsonl [229.9 KB] lsat-ar.jsonl [226.2 KB] jec-qa-kd.jsonl [510.4 KB] lsat-rc.jsonl [974.8 KB] gaokao-mathqa.jsonl [136.4 KB] gaokao-chinese.jsonl [705.5 KB] sat-math.jsonl [273.9 KB] gaokao-english.jsonl [645.4 KB] LICENSE [5.5 KB] gaokao-history.jsonl [111.3 KB] gaokao-biology.jsonl [119.3 KB] logiqa-en.jsonl [610.8 KB] aqua-rat.jsonl [138.6 KB] sat-en.jsonl [1.1 MB] few_shot_prompts.csv [136.8 KB] 📁 📁 GAOKAO-BENCH 📁 📁 data 📁 📁 Multiple-choice_Questions 2010-2022_Biology_MCQs.json [180.5 KB] 2010-2013_English_MCQs.json [76.2 KB] 2010-2022_Geography_MCQs.json [74.8 KB] 2010-2022_Chinese_Lang_and_Usage_MCQs.json [131.8 KB] 2010-2022_Math_I_MCQs.json [198.6 KB] 2010-2022_English_Fill_in_Blanks.json [255.7 KB] 2012-2022_English_Cloze_Test.json [107.0 KB] 2010-2022_Chinese_Modern_Lit.json [210.3 KB] 2010-2022_Chemistry_MCQs.json [207.4 KB] 2010-2022_History_MCQs.json [342.6 KB] 2010-2022_Math_II_MCQs.json [173.3 KB] 2010-2022_Physics_MCQs.json [89.7 KB] 2010-2022_English_Reading_Comp.json [521.9 KB] 2010-2022_Political_Science_MCQs.json [463.2 KB] 📁 📁 Fill-in-the-blank_Questions 2010-2022_Math_I_Fill-in-the-Blank.json [69.7 KB] 2010-2022_Math_II_Fill-in-the-Blank.json [65.3 KB] 2010-2022_Chinese_Language_Famous_Passages_and_Sentences_Dictation.json [28.7 KB] 2014-2022_English_Language_Cloze_Passage.json [92.6 KB] 📁 📁 Open-ended_Questions 2010-2022_Chinese_Language_Language_and_Writing_Skills_Open-ended_Questions.json [85.8 KB] 2010-2022_Physics_Open-ended_Questions.json [79.4 KB] 2010-2022_Chinese_Language_Practical_Text_Reading.json [211.8 KB] 2012-2022_English_Language_Error_Correction.json [84.0 KB] 2010-2022_Math_II_Open-ended_Questions.json [262.1 KB] 2010-2022_Chinese_Language_Classical_Chinese_Reading.json [208.4 KB] 2010-2022_Chemistry_Open-ended_Questions.json [51.1 KB] 2010-2022_Chinese_Language_Literary_Text_Reading.json [299.5 KB] 2010-2022_History_Open-ended_Questions.json [380.9 KB] 2010-2022_Geography_Open-ended_Questions.json [43.5 KB] 2010-2022_Chinese_Language_Ancient_Poetry_Reading.json [78.0 KB] 2010-2022_Biology_Open-ended_Questions.json [267.2 KB] 2010-2022_Math_I_Open-ended_Questions.json [300.0 KB] 2010-2022_Political_Science_Open-ended_Questions.json [253.5 KB] 📁 📁 mmlu 📁 📁 test nutrition_test.csv [83.9 KB] us_foreign_policy_test.csv [25.4 KB] international_law_test.csv [49.5 KB] logical_fallacies_test.csv [45.2 KB] high_school_european_history_test.csv [260.6 KB] astronomy_test.csv [42.0 KB] human_sexuality_test.csv [28.4 KB] marketing_test.csv [56.1 KB] high_school_microeconomics_test.csv [68.5 KB] econometrics_test.csv [43.0 KB] abstract_algebra_test.csv [17.0 KB] high_school_computer_science_test.csv [41.3 KB] college_physics_test.csv [27.2 KB] virology_test.csv [33.9 KB] professional_medicine_test.csv [206.6 KB] machine_learning_test.csv [31.0 KB] college_computer_science_test.csv [39.5 KB] high_school_chemistry_test.csv [52.7 KB] jurisprudence_test.csv [30.6 KB] professional_accounting_test.csv [115.7 KB] philosophy_test.csv [71.1 KB] anatomy_test.csv [29.1 KB] professional_psychology_test.csv [207.4 KB] high_school_psychology_test.csv [143.1 KB] management_test.csv [17.0 KB] moral_scenarios_test.csv [353.0 KB] electrical_engineering_test.csv [21.1 KB] elementary_mathematics_test.csv [60.2 KB] computer_security_test.csv [24.3 KB] high_school_geography_test.csv [36.5 KB] security_studies_test.csv [195.1 KB] business_ethics_test.csv [30.7 KB] college_biology_test.csv [44.3 KB] conceptual_physics_test.csv [34.2 KB] human_aging_test.csv [39.7 KB] college_chemistry_test.csv [21.8 KB] high_school_physics_test.csv [54.9 KB] high_school_biology_test.csv [100.0 KB] college_mathematics_test.csv [21.9 KB] college_medicine_test.csv [76.5 KB] sociology_test.csv [60.1 KB] world_religions_test.csv [20.7 KB] high_school_mathematics_test.csv [47.4 KB] medical_genetics_test.csv [18.1 KB] moral_disputes_test.csv [97.6 KB] MMLU_test_contamination_annotations.json [641.3 KB] high_school_world_history_test.csv [365.0 KB] high_school_us_history_test.csv [286.0 KB] high_school_macroeconomics_test.csv [105.9 KB] high_school_statistics_test.csv [103.5 KB] prehistory_test.csv [80.3 KB] high_school_government_and_politics_test.csv [60.1 KB] professional_law_test.csv [1.8 MB] formal_logic_test.csv [45.8 KB] miscellaneous_test.csv [126.2 KB] global_facts_test.csv [15.8 KB] clinical_knowledge_test.csv [55.2 KB] public_relations_test.csv [25.6 KB] 📁 📁 val high_school_european_history_val.csv [28.6 KB] clinical_knowledge_val.csv [5.8 KB] miscellaneous_val.csv [12.0 KB] nutrition_val.csv [7.5 KB] high_school_statistics_val.csv [9.3 KB] high_school_computer_science_val.csv [3.1 KB] international_law_val.csv [6.0 KB] astronomy_val.csv [4.5 KB] philosophy_val.csv [8.2 KB] professional_law_val.csv [195.9 KB] college_mathematics_val.csv [2.3 KB] high_school_psychology_val.csv [15.5 KB] professional_medicine_val.csv [22.6 KB] sociology_val.csv [6.5 KB] anatomy_val.csv [2.7 KB] high_school_physics_val.csv [6.2 KB] global_facts_val.csv [1.6 KB] high_school_microeconomics_val.csv [6.8 KB] management_val.csv [1.5 KB] machine_learning_val.csv [2.9 KB] human_aging_val.csv [4.0 KB] high_school_world_history_val.csv [43.9 KB] public_relations_val.csv [4.2 KB] moral_scenarios_val.csv [40.0 KB] us_foreign_policy_val.csv [2.9 KB] college_physics_val.csv [3.2 KB] logical_fallacies_val.csv [4.6 KB] marketing_val.csv [6.6 KB] computer_security_val.csv [4.2 KB] professional_psychology_val.csv [26.8 KB] college_computer_science_val.csv [4.3 KB] human_sexuality_val.csv [2.1 KB] jurisprudence_val.csv [3.4 KB] abstract_algebra_val.csv [1.7 KB] econometrics_val.csv [4.6 KB] high_school_chemistry_val.csv [6.4 KB] world_religions_val.csv [2.3 KB] business_ethics_val.csv [2.7 KB] high_school_us_history_val.csv [30.5 KB] high_school_mathematics_val.csv [5.0 KB] elementary_mathematics_val.csv [7.8 KB] medical_genetics_val.csv [2.7 KB] professional_accounting_val.csv [13.4 KB] moral_disputes_val.csv [11.3 KB] high_school_macroeconomics_val.csv [11.7 KB] formal_logic_val.csv [5.8 KB] high_school_biology_val.csv [10.0 KB] conceptual_physics_val.csv [3.7 KB] security_studies_val.csv [21.6 KB] high_school_geography_val.csv [3.7 KB] electrical_engineering_val.csv [2.5 KB] virology_val.csv [4.9 KB] college_biology_val.csv [4.3 KB] college_medicine_val.csv [7.2 KB] college_chemistry_val.csv [2.1 KB] high_school_government_and_politics_val.csv [6.4 KB] prehistory_val.csv [9.3 KB] 📁 📁 dev business_ethics_dev.csv [2.0 KB] econometrics_dev.csv [1.5 KB] high_school_government_and_politics_dev.csv [1.6 KB] nutrition_dev.csv [1.9 KB] anatomy_dev.csv [830.0 B] professional_psychology_dev.csv [2.1 KB] high_school_european_history_dev.csv [11.2 KB] global_facts_dev.csv [1.1 KB] high_school_psychology_dev.csv [1.7 KB] high_school_chemistry_dev.csv [1.1 KB] college_physics_dev.csv [1.2 KB] electrical_engineering_dev.csv [851.0 B] college_computer_science_dev.csv [2.6 KB] astronomy_dev.csv [1.9 KB] medical_genetics_dev.csv [952.0 B] miscellaneous_dev.csv [562.0 B] college_biology_dev.csv [1.4 KB] clinical_knowledge_dev.csv [1.1 KB] virology_dev.csv [962.0 B] marketing_dev.csv [1.3 KB] college_chemistry_dev.csv [1.2 KB] moral_disputes_dev.csv [1.6 KB] abstract_algebra_dev.csv [719.0 B] high_school_statistics_dev.csv [2.4 KB] high_school_computer_science_dev.csv [2.7 KB] international_law_dev.csv [2.2 KB] high_school_microeconomics_dev.csv [1.1 KB] college_mathematics_dev.csv [1.3 KB] conceptual_physics_dev.csv [799.0 B] elementary_mathematics_dev.csv [1.3 KB] high_school_world_history_dev.csv [4.7 KB] formal_logic_dev.csv 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sample-submission-labels.lst [17.3 KB] train_xl.jsonl [8.3 MB] README.md [2.6 KB] train_debiased.jsonl [1.9 MB] train_m-labels.lst [5.0 KB] train_l-labels.lst [20.0 KB] train_s-labels.lst [1.3 KB] train_l.jsonl [2.1 MB] dev-labels.lst [2.5 KB] test.jsonl [350.0 KB] dev.jsonl [269.2 KB] train_m.jsonl [541.1 KB] train_debiased-labels.lst [18.1 KB] train_xs.jsonl [34.0 KB] eval.py [2.0 KB] train_xl-labels.lst [78.9 KB] train_xs-labels.lst [320.0 B] 📁 📁 Xsum dev.jsonl [25.7 MB] dev.json [25.7 MB] dev.csv [25.1 MB] 📁 📁 ceval 📁 📁 formal_ceval 📁 📁 val veterinary_medicine_val.csv [4.0 KB] middle_school_chemistry_val.csv [5.1 KB] computer_architecture_val.csv [3.6 KB] probability_and_statistics_val.csv [5.3 KB] middle_school_politics_val.csv [6.7 KB] high_school_physics_val.csv [6.7 KB] metrology_engineer_val.csv [5.5 KB] tax_accountant_val.csv [17.5 KB] college_economics_val.csv [13.0 KB] high_school_history_val.csv [6.1 KB] ceval_contamination_annotations.json [85.4 KB] education_science_val.csv [4.8 KB] modern_chinese_history_val.csv [4.6 KB] chinese_language_and_literature_val.csv [2.9 KB] middle_school_biology_val.csv [4.7 KB] marxism_val.csv [3.8 KB] law_val.csv [7.4 KB] professional_tour_guide_val.csv [3.8 KB] discrete_mathematics_val.csv [3.0 KB] high_school_geography_val.csv [3.5 KB] high_school_mathematics_val.csv [4.7 KB] high_school_politics_val.csv [8.3 KB] art_studies_val.csv [3.8 KB] urban_and_rural_planner_val.csv [11.5 KB] legal_professional_val.csv [11.5 KB] accountant_val.csv [18.1 KB] operating_system_val.csv [2.8 KB] middle_school_physics_val.csv [4.8 KB] basic_medicine_val.csv [2.2 KB] environmental_impact_assessment_engineer_val.csv [8.3 KB] clinical_medicine_val.csv [3.6 KB] college_chemistry_val.csv [3.9 KB] plant_protection_val.csv [3.1 KB] college_physics_val.csv [5.6 KB] fire_engineer_val.csv [9.1 KB] computer_network_val.csv [3.3 KB] sports_science_val.csv [3.0 KB] business_administration_val.csv [8.3 KB] electrical_engineer_val.csv [7.3 KB] physician_val.csv [7.5 KB] middle_school_history_val.csv [5.4 KB] advanced_mathematics_val.csv [4.8 KB] ideological_and_moral_cultivation_val.csv [2.8 KB] middle_school_geography_val.csv [2.3 KB] middle_school_mathematics_val.csv [4.4 KB] mao_zedong_thought_val.csv [4.9 KB] high_school_chemistry_val.csv [5.1 KB] college_programming_val.csv [8.6 KB] high_school_biology_val.csv [5.6 KB] logic_val.csv [14.7 KB] teacher_qualification_val.csv [11.0 KB] civil_servant_val.csv [19.8 KB] high_school_chinese_val.csv [9.9 KB] 📁 📁 test high_school_mathematics_test.csv [36.9 KB] operating_system_test.csv [26.5 KB] college_physics_test.csv [50.6 KB] middle_school_mathematics_test.csv [28.6 KB] advanced_mathematics_test.csv [45.2 KB] computer_architecture_test.csv [35.4 KB] college_economics_test.csv [106.2 KB] professional_tour_guide_test.csv [34.5 KB] high_school_chinese_test.csv [104.0 KB] physician_test.csv [78.1 KB] computer_network_test.csv [30.8 KB] 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README_zh-CN.md [14.5 KB] test01.py [365.0 B] test02.py [222.0 B] 模型微调与RAG.png [104.6 KB] 【资料】Llama_Index(核心组件介绍).pdf [624.0 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 [1.2 GB] 📁 📁 day08_GPT2-中文生成模型定制化微调训练 📁 📁 demo_8 📁 📁 example test04.py [844.0 B] test05.py [818.0 B] test03.py [824.0 B] test02.py [835.0 B] test01.py [864.0 B] 📁 📁 params 📁 📁 __pycache__ data.cpython-312.pyc [1.3 KB] 📁 📁 .idea 📁 📁 inspectionProfiles profiles_settings.xml [174.0 B] workspace.xml [7.2 KB] modules.xml [271.0 B] misc.xml [189.0 B] .gitignore [50.0 B] demo_8.iml [291.0 B] 📁 📁 data chinese_poems.txt [49.3 MB] data.py [496.0 B] train.py [4.6 KB] day08_GPT2-中文生成模型定制化微调训练必看.png [493.5 KB] 【录播】GPT2-中文生成模型定制化微调训练.mp4 [871.5 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 [510.3 KB] 📁 📁 day30_基于pytorch的语音唤醒系统 📁 📁 项目源码 📁 📁 wakeup_test 📁 📁 results errors.txt confusion_matrix.png [15.9 KB] 📁 📁 checkpoints best_model.pth [3.5 MB] 📁 📁 .idea 📁 📁 inspectionProfiles profiles_settings.xml 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KB] 【课件】Llama_index实现RAG.pdf [489.2 KB] 【录播】llama-index实现RAG.mp4 [1.3 GB] 📁 📁 day05_基于 BERT 的中文评价情感分析 📁 📁 demo_5 📁 📁 model 📁 📁 bert-base-chinese 📁 📁 models--bert-base-chinese 📁 📁 .no_exist 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f added_tokens.json special_tokens_map.json 📁 📁 blobs 📁 📁 refs main [40.0 B] 📁 📁 snapshots 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f model.safetensors [392.5 MB] tokenizer.json [262.6 KB] config.json [624.0 B] vocab.txt [107.0 KB] tokenizer_config.json [49.0 B] 📁 📁 .locks 📁 📁 models--bert-base-chinese 📁 📁 params 0_bert.pth [7.5 KB] 2_bert.pth [7.5 KB] 1_bert.pth [7.5 KB] 📁 📁 data 📁 📁 ChnSentiCorp 📁 📁 validation cache-c072f719f0b9b62a.arrow [9.6 KB] cache-9b7789b1e0e636fb.arrow [9.6 KB] dataset_info.json [1.8 KB] cache-1b273238fdadead7.arrow [9.6 KB] cache-486e66fea9239c3f.arrow [9.6 KB] cache-428974171b74f1a0.arrow [9.6 KB] cache-a1c8a8bb0a669e93.arrow [9.6 KB] state.json [261.0 B] cache-800047dd8fdede53.arrow [9.6 KB] cache-e621cb96adf0c923.arrow [10.0 KB] cache-85b0ee5d0aeacbe5.arrow [9.6 KB] dataset.arrow [376.6 KB] cache-4a46afc6455cc3f0.arrow [9.6 KB] cache-5cd00431b9d3a916.arrow [9.6 KB] cache-749efdb8f0ee42be.arrow [9.6 KB] cache-585e054403b710b3.arrow [9.6 KB] cache-805d054a96ccc48f.arrow [9.6 KB] cache-8ae76a3b52248a8f.arrow [9.6 KB] 📁 📁 test cache-7dd332715d90b654.arrow [10.0 KB] dataset_info.json [1.8 KB] dataset.arrow [372.3 KB] state.json [255.0 B] 📁 📁 train cache-b4e51936648802e2.arrow [76.7 KB] cache-a6ab41ffbc1946d5.arrow [8.0 KB] cache-eb31b953e8e788fb.arrow [73.5 KB] cache-badcf79eb9fa62a0.arrow [73.5 KB] cache-8a97cd1e06b735d2.arrow [73.5 KB] cache-3432ecc2b0d45f4c.arrow [73.5 KB] cache-a34d90b946dc58b8.arrow [73.5 KB] dataset_info.json [1.8 KB] cache-618b312c42069194.arrow [73.5 KB] cache-1e3bba9512e20e17.arrow [73.5 KB] cache-d7c6377d856b0538.arrow [73.5 KB] cache-703908ea6da8e823.arrow [73.5 KB] cache-b85c50cd434dd865.arrow [73.5 KB] cache-942e97a8804ee679.arrow [73.5 KB] cache-c5b262546ff026fd.arrow [76.7 KB] cache-7f783dce092dc384.arrow [73.5 KB] cache-b53b61aef9d859aa.arrow [73.5 KB] cache-a41fe1013beb0d46.arrow [73.5 KB] cache-fdafe12f59ab3430.arrow [73.5 KB] cache-980049a695f6628c.arrow [69.1 KB] state.json [256.0 B] cache-42a7d570466d6993.arrow [76.7 KB] cache-39dfc0aff9c238ae.arrow [73.5 KB] cache-3bdb9443d1ca0706.arrow [624.0 B] dataset.arrow [3.0 MB] cache-9f44d179cc25c59e.arrow [73.5 KB] cache-60739da7e9e626bf.arrow [73.5 KB] dataset_dict.json [43.0 B] hermes-function-calling-v1.csv [14.7 MB] 📁 📁 __pycache__ MyData.cpython-312.pyc [1.5 KB] net.cpython-312.pyc [1.7 KB] 📁 📁 .idea 📁 📁 inspectionProfiles profiles_settings.xml [174.0 B] .gitignore [50.0 B] workspace.xml [7.2 KB] misc.xml [189.0 B] modules.xml [271.0 B] demo_5.iml [291.0 B] run.py [1.7 KB] train.py [2.6 KB] data_test.py [658.0 B] net.py [989.0 B] token_test.py [1.5 KB] MyData.py [856.0 B] 【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] 【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] 📁 📁 day10_llama3大模型本地调用 📁 📁 demo_10 📁 📁 Llama3_test test02.py [1.2 KB] test01.py [165.0 B] data.py [501.0 B] net.pt [389.4 MB] detect.py [709.0 B] train.py [3.2 KB] detect02.py [4.5 KB] 【录播】llama3大模型本地调用.mp4 [912.7 MB] 【课件】llama3大模型本地调用.pdf [555.9 KB] 📁 📁 1_开班典礼-241216 📁 📁 day12_Lora模型合并与推理测试 📁 📁 checkpoint-800 training_args.bin [5.5 KB] tokenizer.json [16.4 MB] optimizer.pt [43.2 MB] README.md [5.0 KB] special_tokens_map.json [650.0 B] trainer_state.json [29.3 KB] rng_state.pth [13.9 KB] scheduler.pt [1.0 KB] adapter_config.json [754.0 B] tokenizer_config.json [53.4 KB] adapter_model.safetensors [21.5 MB] 📁 📁 data ruozhiba_qaswift.json [588.5 KB] 【录播】Lora模型合并与推理测试.mp4 [906.6 MB] day12_Lora模型合并与推理测试文档.zip [1.8 MB] 📁 📁 day18_LMDeploy部署大模型 📁 📁 demo_18 test02.py [440.0 B] test01.py [457.0 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] 📁 📁 day04_Hugging Face 核心组件介绍 📁 📁 demo_4 📁 📁 dataset dataset_test.py [425.0 B] 📁 📁 data 📁 📁 ChnSentiCorp 📁 📁 test dataset.arrow [372.3 KB] cache-7dd332715d90b654.arrow [10.0 KB] state.json [255.0 B] dataset_info.json [1.8 KB] 📁 📁 validation cache-8ae76a3b52248a8f.arrow [9.6 KB] cache-585e054403b710b3.arrow [9.6 KB] cache-e621cb96adf0c923.arrow [10.0 KB] cache-5cd00431b9d3a916.arrow [9.6 KB] dataset.arrow [376.6 KB] cache-486e66fea9239c3f.arrow [9.6 KB] cache-85b0ee5d0aeacbe5.arrow [9.6 KB] cache-805d054a96ccc48f.arrow [9.6 KB] cache-4a46afc6455cc3f0.arrow [9.6 KB] cache-749efdb8f0ee42be.arrow [9.6 KB] cache-1b273238fdadead7.arrow [9.6 KB] state.json [261.0 B] cache-9b7789b1e0e636fb.arrow [9.6 KB] cache-a1c8a8bb0a669e93.arrow [9.6 KB] dataset_info.json [1.8 KB] cache-428974171b74f1a0.arrow [9.6 KB] cache-c072f719f0b9b62a.arrow [9.6 KB] cache-800047dd8fdede53.arrow [9.6 KB] 📁 📁 train cache-eb31b953e8e788fb.arrow [73.5 KB] cache-60739da7e9e626bf.arrow [73.5 KB] cache-3bdb9443d1ca0706.arrow [624.0 B] cache-8a97cd1e06b735d2.arrow [73.5 KB] cache-39dfc0aff9c238ae.arrow [73.5 KB] cache-a41fe1013beb0d46.arrow [73.5 KB] cache-a34d90b946dc58b8.arrow [73.5 KB] cache-d7c6377d856b0538.arrow [73.5 KB] cache-3432ecc2b0d45f4c.arrow [73.5 KB] cache-980049a695f6628c.arrow [69.1 KB] cache-b4e51936648802e2.arrow [76.7 KB] cache-fdafe12f59ab3430.arrow [73.5 KB] dataset_info.json [1.8 KB] cache-618b312c42069194.arrow [73.5 KB] cache-9f44d179cc25c59e.arrow [73.5 KB] cache-7f783dce092dc384.arrow [73.5 KB] cache-c5b262546ff026fd.arrow [76.7 KB] cache-a6ab41ffbc1946d5.arrow [8.0 KB] cache-b85c50cd434dd865.arrow [73.5 KB] cache-42a7d570466d6993.arrow [76.7 KB] cache-badcf79eb9fa62a0.arrow [73.5 KB] cache-942e97a8804ee679.arrow [73.5 KB] dataset.arrow [3.0 MB] state.json [256.0 B] cache-1e3bba9512e20e17.arrow [73.5 KB] cache-703908ea6da8e823.arrow [73.5 KB] cache-b53b61aef9d859aa.arrow [73.5 KB] dataset_dict.json [43.0 B] hermes-function-calling-v1.csv [14.7 MB] 📁 📁 API_test api_test01.py [243.0 B] api_test02.py [361.0 B] 📁 📁 trasnFormers_test 📁 📁 model 📁 📁 bert-base-chinese 📁 📁 .locks 📁 📁 models--bert-base-chinese 📁 📁 models--bert-base-chinese 📁 📁 snapshots 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f tokenizer.json [262.6 KB] model.safetensors [392.5 MB] config.json [624.0 B] vocab.txt [107.0 KB] tokenizer_config.json [49.0 B] 📁 📁 .no_exist 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f special_tokens_map.json added_tokens.json 📁 📁 refs main [40.0 B] 📁 📁 blobs 📁 📁 uer 📁 📁 gpt2-chinese-cluecorpussmall 📁 📁 models--uer--gpt2-chinese-cluecorpussmall 📁 📁 blobs 📁 📁 snapshots 📁 📁 refs main [40.0 B] 📁 📁 .no_exist 📁 📁 .locks 📁 📁 models--uer--gpt2-chinese-cluecorpussmall test01.py [489.0 B] test03.py [658.0 B] test02.py [2.7 KB] 【课件】Hugging Face 核心组件介绍.pdf [501.5 KB] 【资料】Hugging Face 核心组件介绍.pdf [243.1 KB] 【录播】Hugging Face 核心组件介绍.mp4 [762.9 MB] 📁 📁 day14_LLaMA-Factory模型评估与QLora微调 【资料】LLama-Factory模型评估.pdf [295.4 KB] 【课件】LLama-Factory模型评估与QLora微调.pdf [512.7 KB] AI技术路线.pdf [107.4 KB] 【录播】LLama-Factory模型评估与QLora微调.mp4 [763.6 MB] 📁 📁 day06_自定义vocab 📁 📁 demo_6 📁 📁 model 📁 📁 bert-base-chinese 📁 📁 models--bert-base-chinese 📁 📁 blobs_20241231_024804 📁 📁 refs main [40.0 B] 📁 📁 snapshots 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f vocab.txt [107.0 KB] tokenizer_config.json [49.0 B] tokenizer.json [262.6 KB] model.safetensors [392.5 MB] config.json [624.0 B] 📁 📁 .no_exist 📁 📁 c30a6ed22ab4564dc1e3b2ecbf6e766b0611a33f added_tokens.json special_tokens_map.json 📁 📁 .locks 📁 📁 models--bert-base-chinese 📁 📁 __pycache__ MyData.cpython-312.pyc [1.5 KB] net.cpython-312.pyc [1.7 KB] 📁 📁 params 3_bert.pth [7.5 KB] 1_bert.pth [7.5 KB] 0_bert.pth [7.5 KB] 2_bert.pth [7.5 KB] 📁 📁 data 📁 📁 Weibo dataset_info.json [2.3 KB] train.csv [4.0 MB] 📁 📁 ChnSentiCorp 📁 📁 train cache-60739da7e9e626bf.arrow [73.5 KB] dataset.arrow [3.0 MB] dataset_info.json [1.8 KB] cache-a41fe1013beb0d46.arrow [73.5 KB] cache-fdafe12f59ab3430.arrow [73.5 KB] cache-b4e51936648802e2.arrow [76.7 KB] cache-618b312c42069194.arrow [73.5 KB] cache-b85c50cd434dd865.arrow [73.5 KB] cache-7f783dce092dc384.arrow [73.5 KB] cache-9f44d179cc25c59e.arrow [73.5 KB] cache-b53b61aef9d859aa.arrow [73.5 KB] cache-badcf79eb9fa62a0.arrow [73.5 KB] cache-eb31b953e8e788fb.arrow [73.5 KB] cache-d7c6377d856b0538.arrow [73.5 KB] cache-3bdb9443d1ca0706.arrow [624.0 B] cache-c5b262546ff026fd.arrow [76.7 KB] cache-942e97a8804ee679.arrow [73.5 KB] cache-42a7d570466d6993.arrow [76.7 KB] cache-1e3bba9512e20e17.arrow [73.5 KB] cache-980049a695f6628c.arrow [69.1 KB] cache-703908ea6da8e823.arrow [73.5 KB] state.json [256.0 B] cache-8a97cd1e06b735d2.arrow [73.5 KB] cache-3432ecc2b0d45f4c.arrow [73.5 KB] cache-a6ab41ffbc1946d5.arrow [8.0 KB] cache-a34d90b946dc58b8.arrow [73.5 KB] cache-39dfc0aff9c238ae.arrow [73.5 KB] 📁 📁 test state.json [255.0 B] dataset_info.json [1.8 KB] dataset.arrow [372.3 KB] cache-7dd332715d90b654.arrow [10.0 KB] 📁 📁 validation cache-8ae76a3b52248a8f.arrow [9.6 KB] cache-4a46afc6455cc3f0.arrow [9.6 KB] cache-a1c8a8bb0a669e93.arrow [9.6 KB] cache-805d054a96ccc48f.arrow [9.6 KB] cache-e621cb96adf0c923.arrow [10.0 KB] cache-800047dd8fdede53.arrow [9.6 KB] cache-c072f719f0b9b62a.arrow [9.6 KB] cache-585e054403b710b3.arrow [9.6 KB] cache-5cd00431b9d3a916.arrow [9.6 KB] state.json [261.0 B] cache-486e66fea9239c3f.arrow [9.6 KB] cache-749efdb8f0ee42be.arrow [9.6 KB] dataset.arrow [376.6 KB] cache-1b273238fdadead7.arrow [9.6 KB] cache-85b0ee5d0aeacbe5.arrow [9.6 KB] dataset_info.json [1.8 KB] cache-9b7789b1e0e636fb.arrow [9.6 KB] cache-428974171b74f1a0.arrow [9.6 KB] dataset_dict.json [43.0 B] 📁 📁 .idea 📁 📁 inspectionProfiles profiles_settings.xml [174.0 B] modules.xml [271.0 B] .gitignore [50.0 B] encodings.xml [290.0 B] demo_6.iml [291.0 B] workspace.xml [7.7 KB] misc.xml [189.0 B] token_test.py [1.5 KB] test.py [2.1 KB] run.py [1.7 KB] train.py [4.3 KB] vocab_test.py [1.1 KB] MyData02.py [589.0 B] MyData.py [856.0 B] net.py [989.0 B] data_test.py [665.0 B] 銆愬綍鎾€戣嚜瀹氫箟vocab.mp4 [921.7 MB] 【课件】Hugging Face 模型微调训练(自定义vocab).pdf [509.3 KB] 📁 📁 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 [1.3 MB] 📁 📁 模型推理代码 detect.py [724.0 B] detect02.py [4.5 KB] GPU服务器配置与使用.pdf [697.1 KB] 未命名文档.PanD [93.0 B] 📁 📁 day15_Qwen模型打包部署(Lora模型合并&转GGUF模型部署) 📁 📁 Lora 📁 📁 checkpoint-400 optimizer.pt [457.8 MB] README.md [5.0 KB] tokenizer_config.json [1.3 KB] scheduler.pt [1.0 KB] added_tokens.json [80.0 B] trainer_state.json [15.1 KB] rng_state.pth [13.9 KB] special_tokens_map.json [367.0 B] training_args.bin [5.6 KB] merges.txt [1.6 MB] adapter_config.json [744.0 B] tokenizer.json [10.9 MB] adapter_model.safetensors [228.8 MB] vocab.json [2.6 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 [1.1 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 [1.8 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] 📁 📁 day21_llama-index入门实操 📁 📁 demo_21 📁 📁 data README_zh-CN.md [14.5 KB] test02.py [1.6 KB] test01.py [534.0 B] download_hf.py [177.0 B] test03.py [746.0 B] 【录播】Llama_index入门实操.mp4 [934.0 MB] 【课件】Llama_index入门实操.pdf [496.1 KB] 📁 📁 day24_多模态大模型 📁 📁 笔记 多模态02.png [127.3 KB] 多模态01.png [86.8 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] 【资料】多模态(多模态大模型的概念与本地部署调用).pdf [4.9 MB] 【录播】多模态大模型的概念与本地部署调用.mp4 [704.8 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 [1.0 GB] 📁 📁 day13_LLaMA-Factory模型导出量化 📁 📁 checkpoint-3700 adapter_model.safetensors [21.5 MB] training_args.bin [5.4 KB] tokenizer.json [16.4 MB] README.md [5.0 KB] optimizer.pt [43.2 MB] tokenizer_config.json [53.3 KB] rng_state.pth [13.9 KB] adapter_config.json [754.0 B] scheduler.pt [1.0 KB] trainer_state.json [133.8 KB] special_tokens_map.json [439.0 B] 📁 📁 demo_13 📁 📁 data ruozhiba_qaswift_train.json [632.3 KB] ruozhiba_qaswift.json [588.5 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 [1.1 GB] 【资料】LLaMa3导出量化(LLaMA-Factory模型导出量化).pdf [769.0 KB] 📁 📁 day17_Xtuner微调大模型 📁 📁 xtuner数据集转换代码 📁 📁 data target_data.json [714.2 KB] ruozhiba_qaswift.json [588.5 KB] data_utils.py [742.0 B] 📁 📁 xtuner微调配置文件 qwen1_5_1_8b_chat_qlora_alpaca_e3.py [7.6 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] 📁 📁 3_LangChain 📁 📁 LangChain 📁 📁 serve joke_server.py [573.0 B] joke_client.py [136.0 B] 📁 📁 assets data_connection.jpg [42.3 KB] model_io.jpg [643.3 KB] langchain.png [54.6 KB] example_prompt_template.txt [31.0 B] index.ipynb [63.7 KB] llama2.pdf [276.7 KB] memory.db [8.0 KB] 📁 06_LangChain进阶 📁 📁 day10_自定义组件专题 【语雀】自定义组件专题.txt [107.0 B] 【MD】自定义组件专题.md [48.7 KB] 【录播】自定义组件专题.mp4 [830.9 MB] 【资料】自定义组件专题.pdf [2.2 MB] 【课件】自定义组件专题.pdf [858.0 KB] 06_LangChain进阶说明.zip [1.8 MB] 📁 10_modelScope 📁 📁 day_17ModeScope在线训练平台&服务器选配训练模型 【录播】ModeScope在线训练平台&服务器选配训练模型.mp4 [1.1 GB] 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] 【资料】ModeScope在线训练平台&服务器选配训练模型.pdf [275.8 KB] 📁 03_LangChain基础 📁 📁 day07_LangChain Tools & Agent 銆愬綍鎾€慙angChain Tools & Agent.mp4 [1.6 GB] 【MD】LangChain Tools & Agent.md [62.0 KB] 【语雀】LangChain Tools & Agent.txt [110.0 B] day7-demo.zip [1.4 MB] 【资料】LangChain Tools & Agent.pdf [2.8 MB] 【课件】LangChain Tools & Agent.pdf [869.8 KB] 📁 📁 day05_LangChain 基础 【语雀】LangChain 基础.txt [102.0 B] 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] 【资料】LangChain 基础.pdf [3.1 MB] 📁 📁 day06_LangChain Chat Model day6-demo.zip [165.1 KB] redis-3.2.100_x64.zip [4.9 MB] 【语雀】LangChain Chat Model.txt [106.0 B] vs_BuildTools.exe [4.2 MB] 【课件】LangChain Chat Model.pdf [872.4 KB] 【MD】LangChain Chat Model.md [57.1 KB] 【资料】LangChain Chat Model.pdf [3.1 MB] 銆愬綍鎾€慙angChain Chat Model.mp4 [793.0 MB] RedisDesktopManager-2022.5.zip [29.1 MB] day06_LangChain Chat Model说明.png [493.5 KB] 📁 13_llamaindex 📁 📁 day_24Llama_Index(核心组件介绍) 【课件】Llama_Index(核心组件介绍).pdf [486.3 KB] demo_24.zip [4.9 MB] 【录播】Llama_Index(核心组件介绍).mp4 [1.5 GB] llama_index0.8.3.zip [108.1 MB] 【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 [9.0 KB] 【录播】llamaindex实战(使用llamaindex构建自己的知识库).mp4 [1.0 GB] 【资料】llamaindex实战(使用llamaindex构建自己的知识库).pdf [2.2 MB] 【课件】llamaindex实战(使用llamaindex构建自己的知识库).pdf [491.4 KB] 📁 00_Python基础 13-文件IO.mp4 [25.3 MB] 7-Python工程应用-字符串.mp4 [32.3 MB] 19.dotenv使用.mp4 [31.3 MB] 6-pip包管理工具.mp4 [28.7 MB] 5-PyCharn安装与应用.mp4 [22.9 MB] 10-字符编码的处理.mp4 [64.7 MB] 9-如何使用注解.mp4 [29.1 MB] 20.FastAPI的使用.mp4 [59.6 MB] 2-Windows环境安装.mp4 [6.6 MB] 3-macOS环境安装.mp4 [6.8 MB] 8-Python文档化应用场景.mp4 [19.0 MB] 11-Python程序调式和异常处理技巧.mp4 [95.6 MB] 4-VSCode安装与应用.mp4 [19.4 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] 【资料】AutoGen Studio入门使用.pdf [858.8 KB] 【录播】AutoGen Studio调用本地大模型实现多Agent应用.mp4 [834.2 MB] 📁 09_Hugging Face 📁 📁 day_15Hugging Face 模型微调训练(如何处理超长文本训练问题) 【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] 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] 📁 📁 day_16Hugging Face 模型微调训练(GPT2-中文生成模型定制化微调训练) 【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] demo_16.zip [27.8 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] 【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] 📁 📁 day_14Hugging Face 模型微调训练(基于 BERT 的中文评价情感分析) 【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] 【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] 【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_13Hugging Face 核心组件介绍 【资料】Hugging Face 核心组件介绍.pdf [312.9 KB] 【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 [1.7 GB] demo_13.zip [1.1 GB] 📁 05_Rag基础 📁 📁 day09_RAG 专题 day9-demo.zip [286.2 KB] 【课件】RAG 专题.pdf [863.5 KB] 【资料】RAG 专题.pdf [2.7 MB] 銆愬綍鎾€慠AG 涓撻.mp4 [788.8 MB] 【MD】RAG 专题.md [54.2 KB] 【语雀】RAG 专题.txt [96.0 B] 📁 15 项目实战(聚客一和二期) 📁 📁 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 model.safetensors [3.4 GB] config.json [748.0 B] vocab.json [2.6 MB] added_tokens.json [80.0 B] tokenizer.json [10.9 MB] merges.txt [1.6 MB] tokenizer_config.json [1.3 KB] special_tokens_map.json [367.0 B] generation_config.json [205.0 B] training_eval_loss.png [40.1 KB] training_loss.png [55.3 KB] trainer_log.jsonl [42.0 KB] 【录播】基于本地大模型的在线心理问诊系统(部署篇).mp4 [1.7 GB] day_29基于本地大模型的在线心理问诊系统(部署篇)必看.png [493.5 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] 【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_27基于本地大模型的在线心理问诊系统(训练篇) 项目流程.png [103.1 KB] demo_27.zip [2.6 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] data.zip [17.0 MB] 【录播】基于本地大模型的在线心理问诊系统(训练篇01).mp4 [1.2 GB] 【资料】xtuner微调大模型教程.pdf [173.0 KB] 📁 📁 day33_RAG项目实战(使用llamaindex构建自己的知识库) 【资料】RAG项目实战(使用llamaindex构建自己的知识库).pdf [2.5 MB] 【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 [2.3 GB] RAG_项目源码.zip [11.3 KB] 📁 📁 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 [2.7 GB] day_31基于RAG的线上智能客服系统(部署篇)必看.zip [1.8 MB] 【录播】基于RAG的线上智能客服系统(部署篇).mp4 [1.4 GB] 【课件】基于RAG的线上智能客服系统(部署篇).pdf [495.8 KB] demo_31.zip [30.9 KB] 【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 【资料】OpenCompass文档.md [22.1 KB] data.csv [29.2 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 [3.6 KB] rag.py [2.7 KB] 语音应用场景.png [67.8 KB] 【录播】扩展项目(基于pytorch的语音识别与语音唤醒).mp4 [1.8 GB] 【课件】扩展项目(基于pytorch实现的语音识别).pdf [491.6 KB] 📁 📁 day_28基于本地大模型的在线心理问诊系统(训练篇) 📁 📁 xtuner模型训练配置文件 internlm2_5_chat_7b_qlora_oasst1_e3.py [8.0 KB] qwen1_5_1_8b_chat_qlora_alpaca_e3.py [7.5 KB] 📁 📁 xtuner环境 requirements.txt [4.3 KB] 📁 📁 llamafactory数据集转换代码 data_utils.py [2.3 KB] 📁 📁 data xtuner_data.zip [5.8 MB] llama_factory_data.zip [5.5 MB] output_conversations.csv [18.1 MB] 【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] 📁 📁 day34_视觉项目实战(基于yolo的骨龄识别项目_01) 【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 [8.5 MB] 【课件】视觉项目实战(基于yolo的骨龄识别项目_01).pdf [761.7 KB] 📁 📁 day_30基于RAG的线上智能客服系统(微调篇) demo_30.zip [1.7 KB] data.zip [2.2 MB] 【录播】基于RAG的线上智能客服系统(微调篇).mp4 [1.2 GB] …(已达 2500 条上限,后续省略) …(已达 2500 条上限,后续省略) …(已达 2500 条上限,后续省略)
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