Fix README.md (#10156)

README.md

@@ -12,7 +12,7 @@
 > *It is built on the excellent work of [llama.cpp](https://github.com/ggerganov/llama.cpp), [bitsandbytes](https://github.com/TimDettmers/bitsandbytes), [qlora](https://github.com/artidoro/qlora), [gptq](https://github.com/IST-DASLab/gptq), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [awq](https://github.com/mit-han-lab/llm-awq), [AutoAWQ](https://github.com/casper-hansen/AutoAWQ), [vLLM](https://github.com/vllm-project/vllm), [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), [gptq_for_llama](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [chatglm.cpp](https://github.com/li-plus/chatglm.cpp), [redpajama.cpp](https://github.com/togethercomputer/redpajama.cpp), [gptneox.cpp](https://github.com/byroneverson/gptneox.cpp), [bloomz.cpp](https://github.com/NouamaneTazi/bloomz.cpp/), etc.*
 
 ### Latest update 🔥 
-- [2024/02] `bigdl-llm` now supports *[Self-Speculative Decoding](https://bigdl.readthedocs.io/en/main/doc/LLM/Inference/Self_Speculative_Decoding.html)*, which in practice brings **~30% speedup** for FP16 and BF16 inference latency on Intel [GPU](python/llm/example/GPU/Speculative-Decoding) and [CPU](python/llm/example/CPU/Speculative-Decoding) respectively.
+- [2024/02] `bigdl-llm` now supports *[Self-Speculative Decoding](https://bigdl.readthedocs.io/en/latest/doc/LLM/Inference/Self_Speculative_Decoding.html)*, which in practice brings **~30% speedup** for FP16 and BF16 inference latency on Intel [GPU](python/llm/example/GPU/Speculative-Decoding) and [CPU](python/llm/example/CPU/Speculative-Decoding) respectively.
 - [2024/02] `bigdl-llm` now supports a comprehensive list of LLM finetuning on Intel GPU (including [LoRA](python/llm/example/GPU/LLM-Finetuning/LoRA), [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), [DPO](python/llm/example/GPU/LLM-Finetuning/DPO), [QA-LoRA](python/llm/example/GPU/LLM-Finetuning/QA-LoRA) and [ReLoRA](python/llm/example/GPU/LLM-Finetuning/ReLora)).
 - [2024/01] Using `bigdl-llm` [QLoRA](python/llm/example/GPU/LLM-Finetuning/QLoRA), we managed to finetune LLaMA2-7B in **21 minutes** and LLaMA2-70B in **3.14 hours** on 8 Intel Max 1550 GPU for [Standford-Alpaca](python/llm/example/GPU/LLM-Finetuning/QLoRA/alpaca-qlora) (see the blog [here](https://www.intel.com/content/www/us/en/developer/articles/technical/finetuning-llms-on-intel-gpus-using-bigdl-llm.html)).
 - [2024/01] 🔔🔔🔔 ***The default `bigdl-llm` GPU Linux installation has switched from PyTorch 2.0 to PyTorch 2.1, which requires new oneAPI and GPU driver versions. (See the [GPU installation guide](https://bigdl.readthedocs.io/en/latest/doc/LLM/Overview/install_gpu.html) for more details.)***
@@ -138,7 +138,7 @@ model = AutoModelForCausalLM.from_pretrained('/path/to/model/', load_in_low_bit=
 *See the complete example [here](python/llm/example/CPU/HF-Transformers-AutoModels/More-Data-Types).*
 
 #### Verified Models
-Over 20 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Baichuan/Baichuan2, InternLM, QWen* and more; see the example list below.
+Over 40 models have been optimized/verified on `bigdl-llm`, including *LLaMA/LLaMA2, ChatGLM/ChatGLM2, Mistral, Falcon, MPT, Baichuan/Baichuan2, InternLM, QWen* and more; see the example list below.
   
 | Model      | CPU Example                                                    | GPU Example                                                     |
 |------------|----------------------------------------------------------------|-----------------------------------------------------------------|