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| Structured Generation |[`SFTTrainer`]| Fine-tuning Llama-2-7B to generate Persian product catalogs in JSON using QLoRA and PEFT |[Mohammadreza Esmaeilian](https://huggingface.co/Mohammadreza)|[Link](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format)|[](https://colab.research.google.com/github/huggingface/cookbook/blob/main/notebooks/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format.ipynb)|
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| Preference Optimization |[`DPOTrainer`]| Align Mistral-7b using Direct Preference Optimization for human preference alignment |[Maxime Labonne](https://huggingface.co/mlabonne)|[Link](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html)|[](https://colab.research.google.com/github/mlabonne/llm-course/blob/main/Fine_tune_a_Mistral_7b_model_with_DPO.ipynb)|
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| Preference Optimization |[`ORPOTrainer`]| Fine-tuning Llama 3 with ORPO combining instruction tuning and preference alignment |[Maxime Labonne](https://huggingface.co/mlabonne)|[Link](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html)|[](https://colab.research.google.com/drive/1eHNWg9gnaXErdAa8_mcvjMupbSS6rDvi)|
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| Instruction tuning |[`SFTTrainer`]| How to fine-tune open LLMs in 2025 with Hugging Face |[Philipp Schmid](https://huggingface.co/philschmid)|[Link](https://www.philschmid.de/fine-tune-llms-in-2025)|[](https://colab.research.google.com/github/philschmid/deep-learning-pytorch-huggingface/blob/main/training/fine-tune-llms-in-2025.ipynb)|
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<Youtubeid="cnGyyM0vOes" />
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## Contributing
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If you have a tutorial that you would like to add to this list, please open a PR to add it. We will review it and merge it if it is relevant to the community.
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If you have a tutorial that you would like to add to this list, please open a PR to add it. We will review it and merge it if it is relevant to the community.
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