respond to feedback - fix links and split table

docs/source/community_notebooks.md

@@ -2,14 +2,21 @@
 
 Community tutorials are made by active members of the Hugging Face community that want to share their knowledge and expertise with others. They are a great way to learn about the library and its features, and to get started with core classes and modalities.
 
-| Task | Class | Modality | Description | Author | Tutorial |
-|------|--------|----------|-------------|---------|----------|
-| Instruction tuning | SFTTrainer | Text | Fine-tuning Google Gemma LLMs using ChatML format with QLoRA | [Philipp Schmid](https://www.philschmid.de/fine-tune-google-gemma) | [Link](https://www.philschmid.de/fine-tune-google-gemma) |
-| Structured Generation | SFTTrainer | Text | Fine-tuning Llama-2-7B to generate Persian product catalogs in JSON using QLoRA and PEFT | [Mohammadreza Esmaeilian](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format) | [Link](https://huggingface.co/learn/cookbook/en/fine_tuning_llm_to_generate_persian_product_catalogs_in_json_format) |
-| Preference Optimization | DPOTrainer | Text | Align Mistral-7b using Direct Preference Optimization for human preference alignment | [Maxime Labonne](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html) | [Link](https://mlabonne.github.io/blog/posts/Fine_tune_Mistral_7b_with_DPO.html) |
-| Preference Optimization | ORPOTrainer | Text | Fine-tuning Llama 3 with ORPO combining instruction tuning and preference alignment | [Maxime Labonne](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) | [Link](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) |
-| Visual QA | SFTTrainer | Vision-Text | Fine-tuning Qwen2-VL-7B for visual question answering on ChartQA dataset | [Sergio Paniego](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) | [Link](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) |
-| SEO Description | SFTTrainer | Vision-Text | Fine-tuning Qwen2-VL-7B for generating SEO-friendly descriptions from images | [Philipp Schmid](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl) | [Link](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl) |
+# Language Models
+
+| 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) |
+
+# 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) |
 
 ## Contributing