fix

python/llm/example/GPU/HuggingFace/README.md

@@ -1,6 +1,6 @@
-# Running HuggingFace `transformers` model using IPEX-LLM on Intel GPU
+# Running HuggingFace models using IPEX-LLM on Intel GPU
 
-This folder contains examples of running any HuggingFace `transformers` model on IPEX-LLM:
+This folder contains examples of running any HuggingFace model on IPEX-LLM:
 
 - [LLM](LLM): examples of running large language models (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, etc.) using IPEX-LLM optimizations
 - [Multimodal](Multimodal): examples of running large multimodal models (StableDiffusion models, Qwen-VL-Chat, glm-4v, etc.) using IPEX-LLM optimizations

python/llm/example/GPU/README.md

@@ -3,7 +3,7 @@
 This folder contains examples of running IPEX-LLM on Intel GPU:
 
 - [Applications](Applications): running LLM applications (such as autogen) on IPEX-LLM
-- [HuggingFace](HuggingFace): running any ***Hugging Face Transformers*** model on IPEX-LLM (using the standard AutoModel APIs), including language models and multimodal models.
+- [HuggingFace](HuggingFace): running ***HuggingFace*** models on IPEX-LLM (using the standard AutoModel APIs), including language models and multimodal models.
 - [LLM-Finetuning](LLM-Finetuning): running ***finetuning*** (such as LoRA, QLoRA, QA-LoRA, etc) using IPEX-LLM on Intel GPUs
 - [vLLM-Serving](vLLM-Serving): running ***vLLM*** serving framework on intel GPUs (with IPEX-LLM low-bit optimized models)
 - [Deepspeed-AutoTP](Deepspeed-AutoTP): running distributed inference using ***DeepSpeed AutoTP*** (with IPEX-LLM low-bit optimized models) on Intel GPUs