Update README.md

README.md

@@ -19,19 +19,19 @@ The model only takes images as document-side inputs and produce vectors represen
 
 # News
 
-- 2024-07-14: We released **huggingface demo**! Try our [online demo](https://huggingface.co/spaces/bokesyo/minicpm-visual-embeeding-v0-demo)!
+- 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 **Gradio demo** of `miniCPM-visual-embedding-v0`, 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 `miniCPM-visual-embedding-v0`, 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.
 
-- 2024-07-13: We released a **command-line based demo** of `miniCPM-visual-embedding-v0` 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** of `miniCPM-visual-embedding-v0` 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 minicpm-visual-embedding-v0 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. 
 
-# Get started
+# Deploy on your PC
 
-Pip install all dependencies:
+1. Pip install all dependencies:
 
 ```
 Pillow==10.1.0
@@ -43,27 +43,32 @@ sentencepiece==0.1.99
 numpy==1.26.0
 ```
 
-First you are suggested to git clone this huggingface repo or download repo with `huggingface_cli`.
+2. Download the model weights and modeling file, choose one of the following:
+
+- Download with git clone.
 
 ```bash
 git lfs install
 git clone https://huggingface.co/RhapsodyAI/minicpm-visual-embedding-v0
 ```
 
-or 
+- Download with huggingface-hub.
 
 ```bash
+pip install huggingface-hub
 huggingface-cli download --resume-download RhapsodyAI/minicpm-visual-embedding-v0 --local-dir minicpm-visual-embedding-v0 --local-dir-use-symlinks False
 ```
 
-- To deploy a local demo, first check `pipeline_gradio.py`, change `model path` to your local path and change `device` to your device (for users with Nvidia card, use `cuda`, for users with apple silicon, use `mps`). then launch the demo:
+3. To deploy a local demo, first check `pipeline_gradio.py`, change `model_path` to your local path and change `device` to your device (for users with Nvidia card, use `cuda`, for users with apple silicon, use `mps`, for users with only x86 cpu, please use `cpu`). then launch the demo:
 
 ```bash
 pip install gradio
 python pipeline_gradio.py
 ```
 
-- To run the model for research purpose, please refer the following code:
+# For research purpose
+
+To run the model for research purpose, please refer the following code:
 
 ```python
 from transformers import AutoModel
@@ -105,11 +110,11 @@ print(scores)
 
 # Todos
 
-- [x] Release huggingface space demo.
+[x] Release huggingface space demo.
 
-- Release the evaluation results.
+[] Release the evaluation results.
 
-- Release technical report.
+[] Release technical report.
 
 # Limitations