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README.md

@@ -174,54 +174,7 @@ print(processor.decode(output_ids[0], skip_special_tokens=False))
 
 For full environment setup (including optional FlashAttention2 extras), see the top-level repository `README.md`.
 
-## 🛠️ Key Technical Features
 
-- **Native Streaming Architecture**: Supports continuous input and processing of video frames rather than discrete frame sampling.
-- **Unified Position Encoding**: Shared synchronization mechanism for position encoding across both visual and textual modalities.
-- **Efficient Pooling Strategy**: Employs `average` pooling with `stride=4` to balance computational efficiency and feature preservation.
-- **Flash Attention 2**: Full support for FA2 acceleration to optimize memory usage during long-sequence training.
-
-## 🏗️ Model Architecture
-
-The architecture of MOSS-Video-Preview is designed for maximum scalability and efficiency in processing multimodal temporal data. For more detailed information, please refer to the official repository: [fnlp-vision/MOSS-Video-Preview](https://github.com/fnlp-vision/MOSS-Video-Preview)
-
-
-## 📥 Model Usage
-
-For detailed usage instructions, please refer to the official repository: [fnlp-vision/MOSS-Video-Preview](https://github.com/fnlp-vision/MOSS-Video-Preview)
-
-## 🛠️ Usage
-
-This repository provides inference entry points under `inference/`. For end-to-end usage examples and detailed instructions, please refer to the official repository:
-
-- [fnlp-vision/MOSS-Video-Preview](https://github.com/fnlp-vision/MOSS-Video-Preview)
-
-## 🚀 Training Stages (for context)
-
-Although this directory is a **pretrain-only base checkpoint**, the overall model family is produced via multiple stages:
-
-### 1. Stage 1: Vision-Language Alignment (PT1)
-- **Objective**: Establish initial alignment between visual features and the language model, enabling basic visual understanding of video frames.
-- **Configuration**:
-  - **Frozen Parameters**: Language Model (LLM) and Vision Tower.
-  - **Trainable Parameters**: Vision Projector.
-  - **Data**: Large-scale image-text pairs and short video clips.
-  - **Key Feature**: Introduces `mllama_add_video_position_encoding` to provide temporal position information for video frames.
-
-### 2. Stage 2: Full Spatio-Temporal Pretraining (PT2)
-- **Objective**: Enhance the model's understanding of long videos and complex temporal relationships.
-- **Configuration**:
-  - **Method**: Full Parameter Fine-tuning.
-  - **Trainable Parameters**: All modules (Vision Tower, Projector, and LLM) are unfrozen.
-  - **Data**: Video data with longer durations (supporting 256+ frames).
-  - **Key Feature**: Uses `mllama_use_full_attn` to enable full attention mechanisms, improving cross-frame modeling.
-
-### 3. Stage 3: Supervised Fine-Tuning (SFT)
-- **Objective**: Enable the model to follow complex instructions for real-time streaming video dialogue and task processing.
-- **Configuration**:
-  - **Template**: Uses the `mllama` instruction template.
-  - **Data**: High-quality video instruction-following datasets (e.g., real-time description, action recognition, video Q&A).
-  - **Optimization**: Optimized for streaming inference to produce coherent textual responses with low latency.
 
 
 ## ⚠️ Notes
@@ -233,7 +186,7 @@ Although this directory is a **pretrain-only base checkpoint**, the overall mode
 
 ```bibtex
 @misc{moss_video_2026,
-  title         = {MOSS-Video-Preview: Towards Synchronized Streaming Video Understanding},
+  title         = {MOSS-Video-Preview: Towards Real-Time Video Understanding},
   author        = {OpenMOSS Team},
   year          = {2026},
   publisher     = {GitHub},