Update README.md

README.md

@@ -13,7 +13,7 @@ tags:
 Omnivision is a compact, sub-billion (968M) multimodal model for processing both visual and text inputs, optimized for edge devices. Improved on LLaVA's architecture, it features:
 
 - **9x Token Reduction**: Reduces image tokens from 729 to 81, cutting latency and computational cost.
-- **Trustworthy result**: Reduces hallucinations using **DPO** training from trustworthy data.
+- **Trustworthy Result**: Reduces hallucinations using **DPO** training from trustworthy data.
   
 **Quick Links:**
 1. Interactive Demo in our [Hugging Face Space](https://huggingface.co/spaces/NexaAIDev/omnivlm-dpo-demo).
@@ -70,7 +70,7 @@ Omnivision's architecture consists of three key components:
 
 - Base Language Model: Qwen2.5-0.5B-Instruct functions as the base model to process text inputs
 - Vision Encoder: SigLIP-400M operates at 384 resolution with 14×14 patch size to generate image embeddings
-- Projection Layer: Multi-Layer Perceptron (MLP) aligns the vision encoder's embeddings with the language model's token space
+- Projection Layer: Multi-Layer Perceptron (MLP) aligns the vision encoder's embeddings with the language model's token space. Compared to vanilla Llava architecture, we designed a projector that reduce 9X image tokens.
 
 The vision encoder first transforms input images into embeddings, which are then processed by the projection layer to match the token space of Qwen2.5-0.5B-Instruct, enabling end-to-end visual-language understanding.