Update README.md

README.md

@@ -21,6 +21,8 @@ Our model is capable of:
 
 - Help you build a personal library and retireve book pages from a large collection of books.
 
+- It has only 2.8B parameters, and has the potential to run on your PC.
+
 - It works like human: read and comprehend with **vision** and remember **multimodal** information in hippocampus.
 
 ![Memex Archtechture](images/memex.png)
@@ -29,13 +31,13 @@ Our model is capable of:
 
 - 2024-07-14: 🤗 We released **online huggingface demo**! Try our [online demo](https://huggingface.co/spaces/bokesyo/minicpm-visual-embeeding-v0-demo)!
 
-- 2024-07-14: 😋 We released a **locally deployable Gradio demo** of `Memex`, take a look at [pipeline_gradio.py](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/blob/main/pipeline_gradio.py). You can run `pipeline_gradio.py` to build a demo on your PC.
+- 2024-07-14: 😋 We released a **locally deployable Gradio demo** of `Memex`, take a look at [pipeline_gradio.py](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/blob/main/pipeline_gradio.py). You can build a demo on your PC now!
 
-- 2024-07-13: 💻 We released a **locally deployable command-line based demo** of `Memex` for users to retireve most relavant pages from a given PDF file (could be very long), take a look at [pipeline.py](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/blob/main/pipeline.py).
+- 2024-07-13: 💻 We released a **locally deployable command-line based demo** for users to retireve most relavant pages from a given PDF file (could be very long), take a look at [pipeline.py](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0/blob/main/pipeline.py).
 
 - 2024-06-27: 🚀 We released our first visual embedding model checkpoint on [huggingface](https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0).
 
-- 2024-05-08: 🌍 We [open-sourced](https://github.com/RhapsodyAILab/minicpm-visual-embedding-v0) our training code (full-parameter tuning with GradCache and DeepSpeed, supports large batch size across multiple GPUs with zero-stage1) and eval code. 
+- 2024-05-08: 🌍 We [open-sourced](https://github.com/RhapsodyAILab/minicpm-visual-embedding-v0) our training code (full-parameter tuning with GradCache and DeepSpeed zero-stage2, supports large batch size across multiple GPUs with zero-stage1) and eval code. 
 
 # Deploy on your PC