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Ollama-based model evaluation
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@@ -56,7 +56,7 @@ Alternatively, you can view this and other files on GitHub at [https://github.co
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| Ch 4: Implementing a GPT Model from Scratch | - [ch04.ipynb](ch04/01_main-chapter-code/ch04.ipynb)<br/>- [gpt.py](ch04/01_main-chapter-code/gpt.py) (summary)<br/>- [exercise-solutions.ipynb](ch04/01_main-chapter-code/exercise-solutions.ipynb) | [./ch04](./ch04) |
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| Ch 5: Pretraining on Unlabeled Data | - [ch05.ipynb](ch05/01_main-chapter-code/ch05.ipynb)<br/>- [gpt_train.py](ch05/01_main-chapter-code/gpt_train.py) (summary) <br/>- [gpt_generate.py](ch05/01_main-chapter-code/gpt_generate.py) (summary) <br/>- [exercise-solutions.ipynb](ch05/01_main-chapter-code/exercise-solutions.ipynb) | [./ch05](./ch05) |
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| Ch 6: Finetuning for Text Classification | - [ch06.ipynb](ch06/01_main-chapter-code/ch06.ipynb) <br/>- [gpt-class-finetune.py](ch06/01_main-chapter-code/gpt-class-finetune.py) <br/>- [exercise-solutions.ipynb](ch06/01_main-chapter-code/exercise-solutions.ipynb) | [./ch06](./ch06) |
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| Ch 7: Finetuning with Human Feedback | Q2 2024 | ... |
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| Ch 7: Instruction Finetuning | Q2 2024 | ... |
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| Appendix A: Introduction to PyTorch | - [code-part1.ipynb](appendix-A/01_main-chapter-code/code-part1.ipynb)<br/>- [code-part2.ipynb](appendix-A/01_main-chapter-code/code-part2.ipynb)<br/>- [DDP-script.py](appendix-A/01_main-chapter-code/DDP-script.py)<br/>- [exercise-solutions.ipynb](appendix-A/01_main-chapter-code/exercise-solutions.ipynb) | [./appendix-A](./appendix-A) |
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| Appendix B: References and Further Reading | No code | - |
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| Appendix C: Exercise Solutions | No code | - |
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@@ -105,6 +105,7 @@ Several folders contain optional materials as a bonus for interested readers:
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- [Finetuning different models on 50k IMDB movie review dataset](ch06/03_bonus_imdb-classification)
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- **Chapter 7:**
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- [Dataset Utilities for Finding Near Duplicates and Creating Passive Voice Entries](ch07/02_dataset-utilities)
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- [Evaluating Instruction Responses Using the OpenAI API and Ollama](ch07/03_model-evaluation)
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<br>
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