Update README.md

README.md

@@ -50,19 +50,8 @@ ckpt_path/
 ```
 
 ### Host Local Demo
-Execute the following command for demo hosting:
-``` bash
-cd LLaMA2-Accessory/accessory
-python demos/multi_turn_mm.py --n_gpus=2 \
---tokenizer_path=/path/to/tokenizer.model --llama_type=llama_ens \
---pretrained_path ckpt_path/
-```
-Explanation of each argument:
+Please follow the instructions [here](https://github.com/Alpha-VLLM/LLaMA2-Accessory/tree/main/SPHINX#host-local-demo) to see the instruction and complete the use of the model.
 
-+ `--n_gpus`: Number of gpus to use. Utilizing more GPUs will alleviate memory usage on each GPU through model parallelism. Currently, this argument should be set to either 1 or 2, as support for *consolidated ckpt num < gpu num* is not yet available.
-+ `--tokenizer_path`: Path to the official LLaMA2 tokenizer. Note that the tokenizer file is the same for both LLaMA and LLaMA2. You may download it from [here](https://huggingface.co/Alpha-VLLM/LLaMA2-Accessory/blob/main/config/tokenizer.model).
-+ `--llama_type`: The model architecture of SPHINX is defined in [accessory/model/LLM/llama_ens.py](../accessory/model/LLM/llama_ens.py),  and specifying `--llama_type=llama_ens ` tells the demo program to use this architecture.
-+ `--pretrained_path`: The path to pre-trained checkpoint.
 
 
 ## Result
@@ -105,3 +94,13 @@ Our evaluation encompasses both **quantitative metrics** and **qualitative asses
 * The SPHINX model and baseline models on REC benchmarks results on table4.
 * SPHINX exhibits robust performance in visual grounding tasks such as RefCOCO, RefCOCO+, and RefCOCOg, **surpassing other vision-language generalist models**.
 * Notably, SPHINX outperforms specialist models G-DINO-L by **more than 1.54%** in accuracy across all tasks within RefCOCO/RefCOCO+/RefCOCOg. 
+
+
+## Frequently Asked Questions (FAQ)
+
+❓ Encountering issues or have further questions? Find answers to common inquiries [here](https://llama2-accessory.readthedocs.io/en/latest/faq.html). We're here to assist you!
+
+## License
+
+Llama 2 is licensed under the [LLAMA 2 Community License](LICENSE_llama2), Copyright (c) Meta Platforms, Inc. All Rights Reserved.
+