diff --git a/README.md b/README.md
index d6db252..b0da92f 100644
--- a/README.md
+++ b/README.md
@@ -25,24 +25,28 @@ only 18\% lower than that achieved by a top-tier server-grade A100 GPU.
 This significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy.
 
 ## Feature
-PowerInfer is a fast and easy-to-use inference engine for deploying LLM locally. Interestingly, we observe that in ReLU LLM, every neuron is an expert! And a small subset of neurons consistently contributes to the output.
+PowerInfer is a high-speed and easy-to-use inference engine for deploying LLM locally. Interestingly, we observe that in ReLU LLM, every neuron is an expert! And a small subset of neurons consistently contributes to the output.
 PowerInfer is fast with:
 
-- Exploiting the high locality in LLM infernece
+- Exploiting the high locality in LLM inference
 - Neuron-aware hybrid CPU/GPU sparse operator
 - Neuron granularity offloading
 
 PowerInfer is flexible and easy to use with:
 
 - Integration with popular [ReLU-sparse models](https://huggingface.co/SparseLLM)
-- Low-latency serving locally with single consumer-grade GPU 
+- Low-latency serving locally with one single consumer-grade GPU 
 
 PowerInfer supports the following models:
 
 - Falcon-40B model
 - Llama family models
 
-The SparseLLM Team is currently converting the Mistral-7B model to a sparser version. Stay tuned!
+Now PowerInfer supports the following architectures:
+
+- Intel CPU with AVX2 instructions
+- Nvidia GPU
+  
 
 
 
@@ -55,7 +59,7 @@ The SparseLLM Team is currently converting the Mistral-7B model to a sparser ver
 ### Get the Code
 
 ```bash
-git clone https://github.com/hodlen/PowerInfer
+git clone https://github.com/SJTU-IPADS/PowerInfer
 cd PowerInfer
 ```
 ### Build
@@ -79,12 +83,13 @@ cmake --build build --config Release
 ```
 
 ## Model Weights
-
+As for now, we have not released the predictor training code, we suggest you download the sparse model from huggingface in the following link.
 | Base Model | GGUF Format Link | Original Model |
 |------------|------------------|----------------|
-| LLaMA(ReLU)-2-7B   | [PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF)    | [SparseLLM/ReluLLaMA-7B](https://huggingface.co/SparseLLM/ReluLLaMA-7B)     |
+| LLaMA(ReLU)-2-7B   | [PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-7B-PowerInfer-GGUF)    | [SparseLLM/ReluLLaMA-7B](https://huggingface.co/SparseLLM/ReluLLaMA-7B)     |
 | LLaMA(ReLU)-2-13B    | [PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF)   | [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B)  |
-| Falcon(ReLU)-40B    | [PowerInfer/ReluFalcon-40B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-13B-PowerInfer-GGUF)    | [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B)      |
+| Falcon(ReLU)-40B    | [PowerInfer/ReluFalcon-40B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluFalcon-40B-PowerInfer-GGUF)    | [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B)      |
+| LLaMA(ReLU)-2-70B    | [PowerInfer/ReluLLaMA-70B-PowerInfer-GGUF](https://huggingface.co/PowerInfer/ReluLLaMA-70B-PowerInfer-GGUF)    | [SparseLLM/ReluLLaMA-70B](https://huggingface.co/SparseLLM/ReluLLaMA-70B)      |
 
 ## Inference
 - If you just have CPU:
@@ -93,14 +98,14 @@ cmake --build build --config Release
 ```
 - If you have CPU with one GPU:
 ```bash
-./build/bin/main -m /PATH/TO/MODEL -n $(output_token_count) -t $(thread_num) -p $(prompt)
+./build/bin/main -m /PATH/TO/MODEL -n $(output_token_count) -t $(thread_num) -p $(prompt) --vram-budget $(GPU_VRAM_OFFLOADING)
 ```
 
-As for now, it requires a offline-generated "GPU index" file to split FFNs on GPU. If you want to try it, please use the following instruction to generate the GPU index file:
+As for now, it requires an offline-generated "GPU index" file to split FFNs on GPU. If you want to try it, please use the following instructions to generate the GPU index file:
 ```bash
 python scripts/export-gpu-split.py $(activation_count_path) $(output_idx_path) solver
 ```
-Then, you can use the following instruction to run PowerInfer with GPU index:
+Then, you can use the following instructions to run PowerInfer with GPU index:
 ```bash
 ./build/bin/main -m /PATH/TO/MODEL -n $(output_token_count) -t $(thread_num) -p $(prompt) --gpu-index $(split_path)
 ```
@@ -111,7 +116,7 @@ Then, you can use the following instruction to run PowerInfer with GPU index:
 
 ![github-eval-2080ti-q4](https://github.com/SJTU-IPADS/PowerInfer/assets/34213478/0fc1bfc4-aafc-4e82-a865-bec0143aff1a)
 
-PowerInfer achieves up to 11x and 8x speedup for FP16 and INT4 model!
+PowerInfer achieves up to 11.69x and 8.00x speedup for FP16 and INT4 models!
 
 ## TODOs
 We will release the code and data in the following order, please stay tuned!
@@ -119,10 +124,10 @@ We will release the code and data in the following order, please stay tuned!
 - [x] Release core code of PowerInfer, supporting Llama-2, Falcon-40B.
 - [ ] Release perplexity evaluation code
 - [ ] Support Metal for Mac
+- [ ] Release code for OPT models
 - [ ] Release predictor training code 
 - [ ] Support online split for FFN network
-- [ ] Support Multi-GPU 
-
+- [ ] Support Multi-GPU
 
 
 ## Citation