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Category:
Language:
English
Total Size:
1.8 GB
Info Hash:
F59A7AB8A353750371BF1F139AF5B0A92032B223
Added By:
Added:
March 21, 2026, 2:40 p.m.
Stats:
|
(Last updated: Aug. 4, 2026, 3:24 a.m.)
| File | Size |
|---|---|
| Get Bonus Downloads Here.url | 180 bytes |
| 1. Introduction.mp4 | 11.4 MB |
| 2. Course Content Introduction.mp4 | 47.7 MB |
| 3. Jupyter Notebooks.html | 5.4 KB |
| Bolum_(Section)_1.ipynb | 465.1 KB |
| Bolum_(Section)_3_DPO.ipynb | 259.4 KB |
| Bolum_(Section)_4_GRPO_.ipynb | 624.2 KB |
| Bolum_(Section)__2.ipynb | 207.9 KB |
| DS_Store | 6.0 KB |
| Quantization.ipynb | 81.9 KB |
| Thinking__(REASONING)_model.ipynb | 54.8 KB |
| _.DS_Store | 120 bytes |
| _Bolum_(Section)_1.ipynb | 696 bytes |
| _Bolum_(Section)_3_DPO.ipynb | 411 bytes |
| _Bolum_(Section)_4_GRPO_.ipynb | 497 bytes |
| _Bolum_(Section)__2.ipynb | 212 bytes |
| _Quantization.ipynb | 392 bytes |
| _Thinking__(REASONING)_model.ipynb | 212 bytes |
| 10. Preparing Dataset, Chat Template, and Integrating Custom Tokens.en_US.srt | 13.3 KB |
| 10. Preparing Dataset, Chat Template, and Integrating Custom Tokens.mp4 | 145.9 MB |
| 11. Continuing Dataset Preparation and Tokenization.en_US.srt | 5.6 KB |
| 11. Continuing Dataset Preparation and Tokenization.mp4 | 47.0 MB |
| 12. What is a Data Collator How Does It Work Practical Example.en_US.srt | 9.1 KB |
| 12. What is a Data Collator How Does It Work Practical Example.mp4 | 84.6 MB |
| 13. What is LoRA Why Use It.en_US.srt | 3.4 KB |
| 13. What is LoRA Why Use It.mp4 | 17.0 MB |
| 14. Integrating LoRA Matrices into the Model.en_US.srt | 7.6 KB |
| 14. Integrating LoRA Matrices into the Model.mp4 | 37.6 MB |
| 15. Setting Training Arguments (Training Hyperparameters).en_US.srt | 9.8 KB |
| 15. Setting Training Arguments (Training Hyperparameters).mp4 | 32.1 MB |
| 16. Setting Trainer, Starting Training, and Evaluating Results.en_US.srt | 3.9 KB |
| 16. Setting Trainer, Starting Training, and Evaluating Results.mp4 | 21.4 MB |
| 17. Merging Trained LoRA Matrices with the Model.en_US.srt | 6.8 KB |
| 17. Merging Trained LoRA Matrices with the Model.mp4 | 51.0 MB |
| 18. Uploading Model on Hugging Face and Using it.en_US.srt | 5.7 KB |
| 18. Uploading Model on Hugging Face and Using it.mp4 | 49.4 MB |
| 19. Hyperparameters Affecting the Outputs.en_US.srt | 6.5 KB |
| 19. Hyperparameters Affecting the Outputs.mp4 | 30.3 MB |
| 4. Quantization.ipynb.bin | 81.9 KB |
| 4. What is Quantization How does it affect model size and parameters.en_US.srt | 4.9 KB |
| 4. What is Quantization How does it affect model size and parameters.mp4 | 40.2 MB |
| 5. Create a Hugging Face Account and Get a Token.en_US.srt | 5.0 KB |
| 5. Create a Hugging Face Account and Get a Token.mp4 | 35.1 MB |
| 6. Create a Colab Notebook and Get Familiar with the Libraries.en_US.srt | 4.7 KB |
| 6. Create a Colab Notebook and Get Familiar with the Libraries.mp4 | 14.7 MB |
| 7. Bolum_(Section)_1.ipynb.bin | 465.1 KB |
| 7. Download the Model with Quantization.en_US.srt | 6.8 KB |
| 7. Download the Model with Quantization.mp4 | 27.5 MB |
| 8. Bolum_(Section)_1.ipynb.bin | 465.0 KB |
| 8. Differences Between Base and Instruct Models.en_US.srt | 8.5 KB |
| 8. Differences Between Base and Instruct Models.mp4 | 78.0 MB |
| 9. Download and Examine the Dataset.en_US.srt | 4.7 KB |
| 9. Download and Examine the Dataset.mp4 | 18.9 MB |
| 20. Bolum_(Section)__2.ipynb.bin | 207.9 KB |
| 20. Download the Model and Tokenizer.en_US.srt | 4.6 KB |
| 20. Download the Model and Tokenizer.mp4 | 37.0 MB |
| 21. Adding New Custom Tokens to the Tokenizer.en_US.srt | 8.0 KB |
| 21. Adding New Custom Tokens to the Tokenizer.mp4 | 30.9 MB |
| 22. Creating Templates with New Custom Tokens and Integrating Them into the Dataset.en_US.srt | 7.7 KB |
| 22. Creating Templates with New Custom Tokens and Integrating Them into the Dataset.mp4 | 28.7 MB |
| 23. Bolum_(Section)_3_DPO.ipynb.bin | 259.4 KB |
| 23. What is DPO What Data Format Does It Expect.en_US.srt | 7.5 KB |
| 23. What is DPO What Data Format Does It Expect.mp4 | 43.4 MB |
| 24. Bolum_(Section)_3_DPO.ipynb.bin | 259.4 KB |
| 24. Downloading Model & Understanding How the DPO Data Collator do Padding.en_US.srt | 7.1 KB |
| 24. Downloading Model & Understanding How the DPO Data Collator do Padding.mp4 | 45.4 MB |
| 25. Preparing the Dataset for DPO.en_US.srt | 10.9 KB |
| 25. Preparing the Dataset for DPO.mp4 | 84.4 MB |
| 26. Adding LoRA Matrices to the Model.en_US.srt | 3.8 KB |
| 26. Adding LoRA Matrices to the Model.mp4 | 19.1 MB |
| 27. Setting Training Arguments (with DPOConfig).en_US.srt | 5.4 KB |
| 27. Setting Training Arguments (with DPOConfig).mp4 | 13.3 MB |
| 28. Training the Model and Merging the LoRA Matrices.en_US.srt | 6.9 KB |
| 28. Training the Model and Merging the LoRA Matrices.mp4 | 49.7 MB |
| 29. Bolum_(Section)_4_GRPO_.ipynb.bin | 624.2 KB |
| 29. Thinking__(REASONING)_model.ipynb.bin | 54.8 KB |
| 29. What is a “Reasoning” Model How Does It Work.en_US.srt | 5.0 KB |
| 29. What is a “Reasoning” Model How Does It Work.mp4 | 56.5 MB |
| 30. What is GRPO How Is It Applied.en_US.srt | 4.9 KB |
| 30. What is GRPO How Is It Applied.mp4 | 21.4 MB |
| 31. Bolum_(Section)_4_GRPO_.ipynb.bin | 624.2 KB |
| 31. What are Unsloth and VLLM + Download the Model.en_US.srt | 6.9 KB |
| 31. What are Unsloth and VLLM + Download the Model.mp4 | 62.7 MB |
| 32. Examining the Dataset and Initial Preparation Steps.en_US.srt | 7.6 KB |
| 32. Examining the Dataset and Initial Preparation Steps.mp4 | 54.0 MB |
| 33. Extracting Specific Parts of Data Regex and Group Operations.en_US.srt | 13.5 KB |
| 33. Extracting Specific Parts of Data Regex and Group Operations.mp4 | 49.5 MB |
| 34. In Which Format is Data Sent to Reward Functions.en_US.srt | 7.0 KB |
| 34. In Which Format is Data Sent to Reward Functions.mp4 | 88.9 MB |
| 35. 1st Reward Function.en_US.srt | 13.1 KB |
| 35. 1st Reward Function.mp4 | 64.4 MB |
| 36. 2nd Reward Function.en_US.srt | 12.3 KB |
| 36. 2nd Reward Function.mp4 | 73.2 MB |
| 37. 3rd Reward Function.en_US.srt | 11.1 KB |
| 37. 3rd Reward Function.mp4 | 77.9 MB |
| 38. 4th Reward Function.en_US.srt | 7.2 KB |
| 38. 4th Reward Function.mp4 | 26.6 MB |
| 39. Training Hyperparameters (with GRPO Config).en_US.srt | 8.3 KB |
| 39. Training Hyperparameters (with GRPO Config).mp4 | 61.3 MB |
| 40. Trainer Object and Training Process.en_US.srt | 2.6 KB |
| 40. Trainer Object and Training Process.mp4 | 12.0 MB |
| 41. Results Table Rewards and Sample Outputs.en_US.srt | 4.3 KB |
| 41. Results Table Rewards and Sample Outputs.mp4 | 78.4 MB |
| 42. BONUS_New_GRPO_Notebook.html | 7.1 KB |
| 42. SFT_GRPO_Training.ipynb.bin | 10.0 MB |
| 43. BONUS_New_GRPO_Notebook.html | 7.1 KB |
| 43. SFT_GRPO_Training.ipynb.bin | 10.0 MB |
| Bonus Resources.txt | 70 bytes |
Name
DL
Uploader
Size
S/L
Added
-
3.6 GB
[23
/
12]
2025-05-08
| Uploaded by freecoursewb | Size 3.6 GB | Health [ 23 /12 ] | Added 2025-05-08 |
-
437.7 MB
[41
/
32]
2024-11-25
| Uploaded by DHost11 | Size 437.7 MB | Health [ 41 /32 ] | Added 2024-11-25 |
-
540.7 MB
[2
/
16]
2025-10-24
| Uploaded by freecoursewb | Size 540.7 MB | Health [ 2 /16 ] | Added 2025-10-24 |
NOTE
SOURCE: Udemy LLM Reinforcement Learning Fine Tuning DeepSeek Method GR
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