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[Docs] Update project names and links in awesome-transformers (#28878)

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Update project names and repository links in awesome-transformers
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@ -21,7 +21,7 @@ This repository contains examples and best practices for building recommendation
Keywords: Recommender systems, AzureML Keywords: Recommender systems, AzureML
## [lama-cleaner](https://github.com/Sanster/lama-cleaner) ## [IOPaint](https://github.com/Sanster/IOPaint)
Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures. Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures.
@ -105,9 +105,9 @@ An open-source Implementation of Imagen, Google's closed-source Text-to-Image Ne
Keywords: Imagen, Text-to-image Keywords: Imagen, Text-to-image
## [adapter-transformers](https://github.com/adapter-hub/adapter-transformers) ## [adapters](https://github.com/adapter-hub/adapters)
[adapter-transformers](https://github.com/adapter-hub/adapter-transformers) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers. [adapters](https://github.com/adapter-hub/adapters) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers.
Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub
@ -601,9 +601,9 @@ All Hugging Face models and pipelines can be seamlessly integrated into BentoML
Keywords: BentoML, Framework, Deployment, AI Applications Keywords: BentoML, Framework, Deployment, AI Applications
## [LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning) ## [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory)
[LLaMA-Efficient-Tuning](https://github.com/hiyouga/LLaMA-Efficient-Tuning) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning). [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory) offers a user-friendly fine-tuning framework that incorporates PEFT. The repository includes training(fine-tuning) and inference examples for LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, and other LLMs. A ChatGLM version is also available in [ChatGLM-Efficient-Tuning](https://github.com/hiyouga/ChatGLM-Efficient-Tuning).
Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen Keywords: PEFT, fine-tuning, LLaMA-2, ChatGLM, Qwen