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🌐 Community Tutorials #2411

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Expand Up @@ -6,17 +6,17 @@ Community tutorials are made by active members of the Hugging Face community tha

| Task | Class | Description | Author | Tutorial |
|------|--------|-------------|---------|----------|
| Instruction tuning | SFTTrainer | Fine-tuning Google Gemma LLMs using ChatML format with QLoRA | [Philipp Schmid](https://github.com/philschmid) | [Link](https://www.philschmid.de/fine-tune-google-gemma) |
| Structured Generation | SFTTrainer | Fine-tuning Llama-2-7B to generate Persian product catalogs in JSON using QLoRA and PEFT | [Mohammadreza Esmaeilian](https://github.com/Mrzesma) | [Link](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format) |
| Preference Optimization | DPOTrainer | Align Mistral-7b using Direct Preference Optimization for human preference alignment | [Maxime Labonne](https://github.com/mlabonne) | [Link](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html) |
| Preference Optimization | ORPOTrainer | Fine-tuning Llama 3 with ORPO combining instruction tuning and preference alignment | [Maxime Labonne](https://github.com/mlabonne) | [Link](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) |
| Instruction tuning | [`SFTTrainer`] | Fine-tuning Google Gemma LLMs using ChatML format with QLoRA | [Philipp Schmid](https://github.com/philschmid) | [Link](https://www.philschmid.de/fine-tune-google-gemma) |
| Structured Generation | [`SFTTrainer`] | Fine-tuning Llama-2-7B to generate Persian product catalogs in JSON using QLoRA and PEFT | [Mohammadreza Esmaeilian](https://github.com/Mrzesma) | [Link](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format) |
| Preference Optimization | [`DPOTrainer`] | Align Mistral-7b using Direct Preference Optimization for human preference alignment | [Maxime Labonne](https://github.com/mlabonne) | [Link](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html) |
| Preference Optimization | [`ORPOTrainer`] | Fine-tuning Llama 3 with ORPO combining instruction tuning and preference alignment | [Maxime Labonne](https://github.com/mlabonne) | [Link](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) |

# Vision Language Models

| Task | Class | Description | Author | Tutorial |
|------|--------|-------------|---------|----------|
| Visual QA | SFTTrainer | Fine-tuning Qwen2-VL-7B for visual question answering on ChartQA dataset | [Sergio Paniego](https://github.com/sergiopaniego) | [Link](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) |
| SEO Description | SFTTrainer | Fine-tuning Qwen2-VL-7B for generating SEO-friendly descriptions from images | [Philipp Schmid](https://github.com/philschmid) | [Link](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl) |
| Visual QA | [`SFTTrainer`] | Fine-tuning Qwen2-VL-7B for visual question answering on ChartQA dataset | [Sergio Paniego](https://github.com/sergiopaniego) | [Link](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) |
| SEO Description | [`SFTTrainer`] | Fine-tuning Qwen2-VL-7B for generating SEO-friendly descriptions from images | [Philipp Schmid](https://github.com/philschmid) | [Link](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl) |

## Contributing

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