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 PEFT methods only fine-tune a small number of (extra) model parameters, significantly decreasing computational and storage costs because fine-tuning large-scale PLMs is prohibitively costly.
 Recent state-of-the-art PEFT techniques achieve performance comparable to that of full fine-tuning.
 
-PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and [Big Model Inference](https://huggingface.co/docs/accelerate/usage_guides/big_modeling). 
+PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and [Big Model Inference](https://huggingface.co/docs/accelerate/usage_guides/big_modeling).
 
-Supported methods include:
+If you are new to PEFT, get started by reading the [Quicktour](quicktour) guide and conceptual guides for [LoRA](/conceptual_guides/lora) and [Prompting](/conceptual_guides/prompting) methods. 
+
+## Supported methods
 
 1. LoRA: [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685.pdf)
 2. Prefix Tuning: [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://aclanthology.org/2021.acl-long.353/), [P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)
 3. P-Tuning: [GPT Understands, Too](https://arxiv.org/pdf/2103.10385.pdf)
 4. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning](https://arxiv.org/pdf/2104.08691.pdf) 
+5. AdaLoRA: [Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning](https://arxiv.org/abs/2303.10512) 
+6. [LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention](https://github.com/ZrrSkywalker/LLaMA-Adapter)
+## Supported models
+
+The tables provided below list the PEFT methods and models supported for each task. To apply a particular PEFT method for 
+a task, please refer to the corresponding Task guides.
+
+### Causal Language Modeling
+
+| Model        | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  |
+|--------------| ---- | ---- | ---- | ----  |
+| GPT-2        | ✅  | ✅  | ✅  | ✅  |
+| Bloom        | ✅  | ✅  | ✅  | ✅  |
+| OPT          | ✅  | ✅  | ✅  | ✅  |
+| GPT-Neo      | ✅  | ✅  | ✅  | ✅  |
+| GPT-J        | ✅  | ✅  | ✅  | ✅  |
+| GPT-NeoX-20B | ✅  | ✅  | ✅  | ✅  |
+| LLaMA        | ✅  | ✅  | ✅  | ✅  |
+| ChatGLM      | ✅  | ✅  | ✅  | ✅  |
+
+### Conditional Generation
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ---- |
+| T5        | ✅   | ✅   | ✅   | ✅   |
+| BART      | ✅   | ✅   | ✅   | ✅   |
+
+### Sequence Classification
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| BERT           | ✅  | ✅  | ✅  | ✅  |  
+| RoBERTa        | ✅  | ✅  | ✅  | ✅  |
+| GPT-2          | ✅  | ✅  | ✅  | ✅  | 
+| Bloom          | ✅  | ✅  | ✅  | ✅  |   
+| OPT            | ✅  | ✅  | ✅  | ✅  |
+| GPT-Neo        | ✅  | ✅  | ✅  | ✅  |
+| GPT-J          | ✅  | ✅  | ✅  | ✅  |
+| Deberta        | ✅  |     | ✅  | ✅  |     
+| Deberta-v2     | ✅  |     | ✅  | ✅  |    
+
+### Token Classification
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| BERT           | ✅  | ✅  |   |   |  
+| RoBERTa        | ✅  | ✅  |   |   |
+| GPT-2          | ✅  | ✅  |   |   | 
+| Bloom          | ✅  | ✅  |   |   |   
+| OPT            | ✅  | ✅  |   |   |
+| GPT-Neo        | ✅  | ✅  |   |   |
+| GPT-J          | ✅  | ✅  |   |   |
+| Deberta        | ✅  |     |   |   | 
+| Deberta-v2     | ✅  |     |   |   |
+
+### Text-to-Image Generation
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| Stable Diffusion           | ✅  |   |   |   |  
+
+
+### Image Classification
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| ViT           | ✅  |   |   |   | 
+| Swin           | ✅  |   |   |   | 
+
+### Image to text (Multi-modal models)
+
+We have tested LoRA for [ViT](https://huggingface.co/docs/transformers/model_doc/vit) and [Swin](https://huggingface.co/docs/transformers/model_doc/swin) for fine-tuning on image classification. 
+However, it should be possible to use LoRA for any [ViT-based model](https://huggingface.co/models?pipeline_tag=image-classification&sort=downloads&search=vit) from 🤗 Transformers. 
+Check out the [Image classification](/task_guides/image_classification_lora) task guide to learn more. If you run into problems, please open an issue.
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| Blip-2           | ✅  |   |   |   |
+ 
+
+### Semantic Segmentation
+
+As with image-to-text models, you should be able to apply LoRA to any of the [segmentation models](https://huggingface.co/models?pipeline_tag=image-segmentation&sort=downloads). 
+It's worth noting that we haven't tested this with every architecture yet. Therefore, if you come across any issues, kindly create an issue report.
+
+|   Model         | LoRA | Prefix Tuning  | P-Tuning | Prompt Tuning  | 
+| --------- | ---- | ---- | ---- | ----  |
+| SegFormer           | ✅  |   |   |   | 
+