[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fGiD0bMNcDOI_yjoPEgK-gYxRO4oqJmgUVEkaiL0DE1Q":3},{"id":4,"name":5,"category_id":6,"subcategory_id":7,"uploaded_by":8,"downloads":9,"size":10,"seeders":11,"leechers":12,"infohash":13,"language":14,"title":15,"slogan":15,"poster_alt":16,"description":15,"cover_image":15,"magnet_link":17,"stream_link":16,"content":18,"files":19,"comments_count":20,"tracker_list":21,"date_uploaded":22,"last_checked":22,"last_checked_at":16,"user_id":16,"submit_flag":20,"uploaded_at":23,"created_at":24,"updated_at":24,"slugged_title":25,"category_name":26,"subcategory_name":27,"uploaded_ago":28,"category":29,"subcategory":30,"comments":32},6608805,"Udemy - LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO",9,34,"freecoursewb",36177,"1.8 GB",25477,9631,"F59A7AB8A353750371BF1F139AF5B0A92032B223","English","",null,"magnet:?xt=urn:btih:F59A7AB8A353750371BF1F139AF5B0A92032B223&dn=Udemy+-+LLM+Reinforcement+Learning+Fine-Tuning+DeepSeek+Method+GRPO&tr=udp%3A%2F%2Ftracker.torrent.eu.org%3A451%2Fannounce&tr=udp%3A%2F%2Ftracker.tiny-vps.com%3A6969%2Fannounce&tr=http%3A%2F%2Ftracker.foreverpirates.co%3A80%2Fannounce&tr=udp%3A%2F%2Ftracker.cyberia.is%3A6969%2Fannounce&tr=udp%3A%2F%2Fexodus.desync.com%3A6969%2Fannounce&tr=udp%3A%2F%2Fexplodie.org%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=udp%3A%2F%2F9.rarbg.to%3A2780%2Fannounce&tr=udp%3A%2F%2Ftracker.internetwarriors.net%3A1337%2Fannounce&tr=udp%3A%2F%2Fipv4.tracker.harry.lu%3A80%2Fannounce&tr=udp%3A%2F%2Fopen.stealth.si%3A80%2Fannounce&tr=udp%3A%2F%2F9.rarbg.to%3A2900%2Fannounce&tr=udp%3A%2F%2F9.rarbg.me%3A2720%2Fannounce&tr=udp%3A%2F%2Fopentor.org%3A2710%2Fannounce&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce&tr=http%3A%2F%2Ftracker.openbittorrent.com%3A80%2Fannounce&tr=udp%3A%2F%2Fopentracker.i2p.rocks%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.internetwarriors.net%3A1337%2Fannounce&tr=udp%3A%2F%2Ftracker.leechers-paradise.org%3A6969%2Fannounce&tr=udp%3A%2F%2Fcoppersurfer.tk%3A6969%2Fannounce&tr=udp%3A%2F%2Ftracker.zer0day.to%3A1337%2Fannounce","\u003Cp>\u003Cstrong> LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO  \u003C/strong>\n\u003Cbr>\u003Cbr>\u003Cspan style=\"font-size: 16px\">\u003Cstrong>\u003Ca target=\"_blank\" href=\"https://WebToolTip.com\">https://WebToolTip.com\u003C/a> \u003C/strong>\u003C/span>\n\u003Cbr>\u003Cbr>Last updated 6/2025\n\u003Cbr>MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch\n\u003Cbr>Language: English | Duration: 3h 45m | Size: 1.85 GB\n\u003Cbr>\u003Cbr>[EN] LLM Fine-Tuning and Reinforcement Learning with SFT, LoRA, DPO, and GRPO Custom Data HuggingFace\n\u003Cbr>\u003Cbr>What you'll learn\n\u003Cbr>You will grasp the core principles of Large Language Models (LLMs) and the overall structure behind their training processes.\n\u003Cbr>You will learn the differences between base models and instruct models, as well as the methods for preparing data for each.\n\u003Cbr>You’ll learn data preprocessing techniques along with essential tips, how to identify special tokens required by models, understanding data formats, and methods\n\u003Cbr>You’ll gain practical, hands-on experience and detailed knowledge of how LoRA and Data Collator work.\n\u003Cbr>You’ll gain a detailed understanding of crucial hyperparameters used in training, including their purpose and how they function.\n\u003Cbr>You’ll practically learn, in detail, how trained LoRA matrices are merged with the base model, as well as key considerations and best practices to follow during\n\u003Cbr>You’ll learn what Direct Preference Optimization (DPO) is, how it works, the expected data format, and the specific scenarios in which it’s used.\n\u003Cbr>You’ll learn key considerations when preparing data for DPO, as well as understanding how the DPO data collator functions.\n\u003Cbr>You’ll learn about the specific hyperparameters used in DPO training, their roles, and how they function.\n\u003Cbr>You’ll learn how to upload your trained model to platforms like Hugging Face and manage hyperparameters effectively after training.\n\u003Cbr>You’ll learn in detail how Group Relative Policy Optimization (GRPO), a reinforcement learning method, works, including an in-depth understanding of its learnin\n\u003Cbr>You’ll learn how to prepare data specifically for Group Relative Policy Optimization (GRPO).\n\u003Cbr>You’ll learn how to create reward functions—the most critical aspect of Group Relative Policy Optimization (GRPO)—through various practical reward function exam\n\u003Cbr>In what format should data be provided to GRPO reward functions, and how can we process this data within the functions? You’ll learn these details thoroughly.\n\u003Cbr>You’ll learn how to define rewards within functions and establish clear reward templates for GRPO.\n\u003Cbr>You’ll practically learn numerous details, such as extracting reward-worthy parts from raw responses and defining rewards based on these extracted segments.\n\u003Cbr>You’ll learn how to transform an Instruct model into one capable of generating “Chain of Thought” reasoning through GRPO (Group Relative Policy Optimization).\n\u003Cbr>\u003Cbr>Requirements\n\u003Cbr>Basic knowledge of Python programming.\n\u003Cbr>Introductory-level familiarity with artificial intelligence and machine learning concepts.\n\u003Cbr>Ideally, prior experience with Jupyter Notebook or Google Colab.\u003C/p>","\u003Ch2>Files: \u003C/h2>\n                            \u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>[ WebToolTip.com ] Udemy - LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Get Bonus Downloads Here.url (0.2 KB)\u003C/li>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>~Get Your Files Here !\u003C/span>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>1 - Introduction\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>1. Introduction.mp4 (11.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>2. Course Content Introduction.mp4 (47.7 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-script\">\u003C/i>3. Jupyter Notebooks.html (5.4 KB)\u003C/li>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>Notebooks 2\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Bolum_(Section)_1.ipynb (465.1 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Bolum_(Section)_3_DPO.ipynb (259.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Bolum_(Section)_4_GRPO_.ipynb (624.2 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Bolum_(Section)__2.ipynb (207.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>DS_Store (6.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Quantization.ipynb (81.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>Thinking__(REASONING)_model.ipynb (54.8 KB)\u003C/li>\n\u003C/ul>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>__MACOSX\u003C/span>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>Notebooks 2\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_.DS_Store (0.1 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Bolum_(Section)_1.ipynb (0.7 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Bolum_(Section)_3_DPO.ipynb (0.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Bolum_(Section)_4_GRPO_.ipynb (0.5 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Bolum_(Section)__2.ipynb (0.2 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Quantization.ipynb (0.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>_Thinking__(REASONING)_model.ipynb (0.2 KB)\u003C/li>\n\u003C/ul>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>2 - Quantization, LoRA, SFT, Data Collator, Data Preparation…\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>10. Preparing Dataset, Chat Template, and Integrating Custom Tokens.en_US.srt (13.3 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>10. Preparing Dataset, Chat Template, and Integrating Custom Tokens.mp4 (145.9 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>11. Continuing Dataset Preparation and Tokenization.en_US.srt (5.6 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>11. Continuing Dataset Preparation and Tokenization.mp4 (47.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>12. What is a Data Collator How Does It Work Practical Example.en_US.srt (9.1 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>12. What is a Data Collator How Does It Work Practical Example.mp4 (84.6 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>13. What is LoRA Why Use It.en_US.srt (3.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>13. What is LoRA Why Use It.mp4 (17.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>14. Integrating LoRA Matrices into the Model.en_US.srt (7.6 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>14. Integrating LoRA Matrices into the Model.mp4 (37.6 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>15. Setting Training Arguments (Training Hyperparameters).en_US.srt (9.8 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>15. Setting Training Arguments (Training Hyperparameters).mp4 (32.1 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>16. Setting Trainer, Starting Training, and Evaluating Results.en_US.srt (3.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>16. Setting Trainer, Starting Training, and Evaluating Results.mp4 (21.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>17. Merging Trained LoRA Matrices with the Model.en_US.srt (6.8 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>17. Merging Trained LoRA Matrices with the Model.mp4 (51.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>18. Uploading Model on Hugging Face and Using it.en_US.srt (5.7 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>18. Uploading Model on Hugging Face and Using it.mp4 (49.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>19. Hyperparameters Affecting the Outputs.en_US.srt (6.5 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>19. Hyperparameters Affecting the Outputs.mp4 (30.3 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>4. Quantization.ipynb.bin (81.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>4. What is Quantization How does it affect model size and parameters.en_US.srt (4.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>4. What is Quantization How does it affect model size and parameters.mp4 (40.2 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>5. Create a Hugging Face Account and Get a Token.en_US.srt (5.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>5. Create a Hugging Face Account and Get a Token.mp4 (35.1 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>6. Create a Colab Notebook and Get Familiar with the Libraries.en_US.srt (4.7 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>6. Create a Colab Notebook and Get Familiar with the Libraries.mp4 (14.7 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>7. Bolum_(Section)_1.ipynb.bin (465.1 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>7. Download the Model with Quantization.en_US.srt (6.8 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>7. Download the Model with Quantization.mp4 (27.5 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>8. Bolum_(Section)_1.ipynb.bin (465.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>8. Differences Between Base and Instruct Models.en_US.srt (8.5 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>8. Differences Between Base and Instruct Models.mp4 (78.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>9. Download and Examine the Dataset.en_US.srt (4.7 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>9. Download and Examine the Dataset.mp4 (18.9 MB)\u003C/li>\n\u003C/ul>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>3 - Adding New Tokens and Creating Templates for the Tokenizer\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>20. Bolum_(Section)__2.ipynb.bin (207.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>20. Download the Model and Tokenizer.en_US.srt (4.6 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>20. Download the Model and Tokenizer.mp4 (37.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>21. Adding New Custom Tokens to the Tokenizer.en_US.srt (8.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>21. Adding New Custom Tokens to the Tokenizer.mp4 (30.9 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>22. Creating Templates with New Custom Tokens and Integrating Them into the Dataset.en_US.srt (7.7 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>22. Creating Templates with New Custom Tokens and Integrating Them into the Dataset.mp4 (28.7 MB)\u003C/li>\n\u003C/ul>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>4 - DPO (Direct Preference Optimization)\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>23. Bolum_(Section)_3_DPO.ipynb.bin (259.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>23. What is DPO What Data Format Does It Expect.en_US.srt (7.5 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>23. What is DPO What Data Format Does It Expect.mp4 (43.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>24. Bolum_(Section)_3_DPO.ipynb.bin (259.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>24. Downloading Model & Understanding How the DPO Data Collator do Padding.en_US.srt (7.1 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>24. Downloading Model & Understanding How the DPO Data Collator do Padding.mp4 (45.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>25. Preparing the Dataset for DPO.en_US.srt (10.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>25. Preparing the Dataset for DPO.mp4 (84.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>26. Adding LoRA Matrices to the Model.en_US.srt (3.8 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>26. Adding LoRA Matrices to the Model.mp4 (19.1 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>27. Setting Training Arguments (with DPOConfig).en_US.srt (5.4 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>27. Setting Training Arguments (with DPOConfig).mp4 (13.3 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>28. Training the Model and Merging the LoRA Matrices.en_US.srt (6.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>28. Training the Model and Merging the LoRA Matrices.mp4 (49.7 MB)\u003C/li>\n\u003C/ul>\n\u003Cspan class=\"head\">\u003Ci class=\"flaticon-folder\">\u003C/i>5 - GRPO (Group Relative Policy Optimization) Reinforcement Learning\u003C/span>\n\u003Cul>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>29. Bolum_(Section)_4_GRPO_.ipynb.bin (624.2 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>29. Thinking__(REASONING)_model.ipynb.bin (54.8 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>29. What is a “Reasoning” Model How Does It Work.en_US.srt (5.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>29. What is a “Reasoning” Model How Does It Work.mp4 (56.5 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>30. What is GRPO How Is It Applied.en_US.srt (4.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>30. What is GRPO How Is It Applied.mp4 (21.4 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-other-file\">\u003C/i>31. Bolum_(Section)_4_GRPO_.ipynb.bin (624.2 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>31. What are Unsloth and VLLM + Download the Model.en_US.srt (6.9 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>31. What are Unsloth and VLLM + Download the Model.mp4 (62.7 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>32. Examining the Dataset and Initial Preparation Steps.en_US.srt (7.6 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>32. Examining the Dataset and Initial Preparation Steps.mp4 (54.0 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>33. Extracting Specific Parts of Data Regex and Group Operations.en_US.srt (13.5 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>33. Extracting Specific Parts of Data Regex and Group Operations.mp4 (49.5 MB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>34. In Which Format is Data Sent to Reward Functions.en_US.srt (7.0 KB)\u003C/li>\n\u003Cli>\n\u003Ci class=\"flaticon-movies\">\u003C/i>34. In Which Format is Data Sen                        \u003C/li>\u003C/ul>\u003C/ul>\u003C/ul>",0,"\u003Ch3>\n                                Code:\n                            \u003C/h3>\n                            \u003Cul>\n                                \u003Cli>\n                                        \u003Cspan class=\"icon\">\n                                            \u003Ci class=\"flaticon-href-link\">\n                                            \u003C/i>\n                                        \u003C/span>\n                                        udp://tracker.torrent.eu.org:451/announce                                    \u003C/li>\u003Cli>\n                                        \u003Cspan class=\"icon\">\n                                            \u003Ci class=\"flaticon-href-link\">\n                                            \u003C/i>\n                                        \u003C/span>\n                                        udp://tracker.tiny-vps.com:6969/announce                                    \u003C/li>\u003Cli>\n                                        \u003Cspan class=\"icon\">\n                                            \u003Ci class=\"flaticon-href-link\">\n                                            \u003C/i>\n                                        \u003C/span>\n                                        http://tracker.foreverpirates.co:80/announce                                    \u003C/li>\u003Cli>\n                                        \u003Cspan class=\"icon\">\n                                            \u003Ci class=\"flaticon-href-link\">\n                                            \u003C/i>\n                                        \u003C/span>\n                                        udp://tracker.cyberia.is:6969/announce                                    \u003C/li>\u003Cli>\n                                        \u003Cspan class=\"icon\">\n                                        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06:14:10","2026-03-24T06:14:10.000000Z","udemy-llm-reinforcement-learning-fine-tuning-deepseek-method-grpo","Other","Tutorials","Mar. 24th '26",{"id":6,"name":26},{"id":7,"name":27,"icon":31},"flaticon-tutorial",[]]