Improve language and use collapsable toggle to avoid displaying long …

…code snippets in readme

README.md

@@ -35,7 +35,6 @@
 
 ## Contents
 - [Install](#install)
-- [Quick Start With HuggingFace](#quick-start-with-huggingface)
 - [LLaVA Weights](#llava-weights)
 - [Demo](#Demo)
 - [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)
@@ -73,15 +72,16 @@ pip uninstall transformers
 pip install -e .
 ```
 
-## Quick Start With HuggingFace
-You can make a quick start with our model on HugginFace.
-Look the detail in "llava/model/builder.py" and "load_pretrained_model" function.
-Here is a example code:
+### Quick Start With HuggingFace
+
+<details>
+<summary>Example Code</summary>
+
 ```Python
 from llava.model.builder import load_pretrained_model
 from llava.mm_utils import get_model_name_from_path
 
-model_path = "liuhaotian/LLaVA-Lightning-MPT-7B-preview"
+model_path = "liuhaotian/llava-v1.5-7b"
 model_name = get_model_name_from_path(model_path)
 model_base = None
 
@@ -92,12 +92,14 @@ tokenizer, model, image_processor, context_len = load_pretrained_model(
 )
 ```
 
-You can also use our "eval_model" function in "llava > eval > run_llava.py" to get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.
+Check out the details wth the `load_pretrained_model` function in `llava/model/builder.py`.
+
+You can also use the `eval_model` function in `llava/eval/run_llava.py` to get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.
 
 ``` python
 # import the file
 
-model_path = "liuhaotian/LLaVA-Lightning-MPT-7B-preview"
+model_path = "liuhaotian/llava-v1.5-7b"
 model_name = get_model_name_from_path(model_path)
 model_base = None
 prompt = "Give me a short description of this image."
@@ -114,7 +116,8 @@ args = type('Args', (), {
 
 output = eval_model(args)
 print(output)
-``` 
+```
+</details>
 
 ## LLaVA Weights
 Please check out our [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) for all public LLaVA checkpoints, and the instructions of how to use the weights.