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README.md

@@ -32,13 +32,13 @@ MOSS-VL-Instruct-0408 is the instruction-tuned checkpoint of the MOSS-VL series,
 
 Built on top of MOSS-VL-Base-0408 through supervised fine-tuning (SFT), this checkpoint is designed as a high-performance offline multimodal engine. It delivers strong, well-rounded performance across the full spectrum of vision-language tasks — including image understanding, OCR, document parsing, visual reasoning, and instruction following — and is particularly outstanding at video understanding, from long-form comprehension to fine-grained temporal reasoning and action recognition.
 
-### Highlights
+### ✨ Highlights
 
 - 🎬 **Outstanding Video Understanding** — A core strength of MOSS-VL. The model excels at long-form video comprehension, temporal reasoning, action recognition, and second-level event localization, delivering top-tier results on benchmarks such as VideoMME, MLVU, and EgoSchema.
 - 🖼️ **Strong General Multimodal Perception** — Robust image understanding, fine-grained object recognition, OCR, and document parsing.
 - 💬 **Reliable Instruction Following** — Substantially improved alignment with user intent through supervised fine-tuning on diverse multimodal instruction data.
 
-### Note on Variants
+### 📝 Note on Variants
 
 > [!IMPORTANT]
 > **This is the offline instruction-tuned checkpoint.** It is not the streaming variant. If you are looking for low-latency, real-time interactive video understanding, please refer to the upcoming **MOSS-VL-RealTime** release.
@@ -76,7 +76,7 @@ MOSS-VL utilizes Cross-attention Rotary Position Embedding (XRoPE), tailored to
 
 We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four key dimensions: Multimodal Perception, Document/OCR, Multimodal Reasoning, and Video Understanding. The results demonstrate that MOSS-VL achieves outstanding performance, particularly excelling in **general multimodal perception** and **complex video analysis**.
 
-### Key Highlights
+### 🌟 Key Highlights
 
 *   **🚀 Leading Video Intelligence**: MOSS-VL achieves a score of **65.8** in Video Understanding, significantly outperforming Qwen3-VL (+2pts). It shows exceptional temporal consistency and action recognition capabilities across benchmarks like `VideoMME`, `MLVU`, `EgoSchema`, and `VSI-bench` (where it outperforms **Qwen3-VL-8B-Instruct** by **8.3 points**).
 *   **👁️ Outstanding Multimodal Perception**: With a score of **75.1**, MOSS-VL delivers excellent general image-text understanding, shining in fine-grained object recognition and spatial reasoning on benchmarks like `BLINK` and `MMBench`.
@@ -93,6 +93,25 @@ We conducted a comprehensive evaluation of **MOSS-VL-Instruct-0408** across four
 
 ## 🚀 Quickstart
 
+
+### 🛠️ Requirements
+
+Installation commands:
+
+```bash
+conda create -n moss_vl python=3.12 pip -y
+conda activate moss_vl
+pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt
+```
+
+Validated setup notes:
+
+- CUDA runtime used for validation: `12.8`
+- Inference loading uses `trust_remote_code=True` and `attn_implementation="flash_attention_2"`
+
+### 🏃 Run Inference
+
+
 <details>
 <summary><strong>Single-image offline inference (Python)</strong></summary>
 
@@ -276,23 +295,9 @@ texts = [item["text"] for item in result["results"]]
 </details>
 
 
-## Requirements
-
-Installation commands:
-
-```bash
-conda create -n moss_vl python=3.12 pip -y
-conda activate moss_vl
-pip install -i https://pypi.org/simple --no-build-isolation -r requirements.txt
-```
-
-Validated setup notes:
-
-- CUDA runtime used for validation: `12.8`
-- Inference loading uses `trust_remote_code=True` and `attn_implementation="flash_attention_2"`
 
 
-## Limitations and Future Work
+## 🚧 Limitations and Future Work
 
 MOSS-VL-Instruct-0408 represents an early milestone in the MOSS-VL roadmap, and we're actively working on several directions to push it further: