Update README.md

README.md

@@ -3,8 +3,8 @@ pipeline_tag: image-text-to-text
 tags:
 - NPU
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-# Qwen3-VL-4B-Thinking
-Run **Qwen3-VL-4B-Thinking** optimized for **Qualcomm NPUs** with [nexaSDK](https://sdk.nexa.ai).
+# Qwen3-VL-4B-Instruct
+Run **Qwen3-VL-4B-Instruct** optimized for **Qualcomm NPUs** with [nexaSDK](https://sdk.nexa.ai).
 
 ## Quickstart
 
@@ -21,32 +21,32 @@ Run **Qwen3-VL-4B-Thinking** optimized for **Qualcomm NPUs** with [nexaSDK](http
    ```
 
 ## Model Description
-**Qwen3-VL-4B-Thinking** is a 4-billion-parameter multimodal large language model from the Qwen team at Alibaba Cloud.  
-Part of the **Qwen3-VL** (Vision-Language) family, it is designed for advanced visual reasoning and chain-of-thought generation across image, text, and video inputs.
+**Qwen3-VL-4B-Instruct** is a 4-billion-parameter instruction-tuned multimodal large language model from Alibaba Cloud’s Qwen team.  
+As part of the **Qwen3-VL** series, it fuses powerful vision-language understanding with conversational fine-tuning, optimized for real-world applications such as chat-based reasoning, document analysis, and visual dialogue.
 
-Compared to the *Instruct* variant, the **Thinking** model emphasizes deeper multi-step reasoning, analysis, and planning. It produces detailed, structured outputs that reflect intermediate reasoning steps, making it well-suited for research, multimodal understanding, and agentic workflows.
+The *Instruct* variant is tuned for following user prompts naturally and safely — producing concise, relevant, and user-aligned responses across text, image, and video contexts.
 
 ## Features
-- **Vision-Language Understanding**: Processes images, text, and videos for joint reasoning tasks.
-- **Structured Thinking Mode**: Generates intermediate reasoning traces for better transparency and interpretability.
-- **High Accuracy on Visual QA**: Performs strongly on visual question answering, chart reasoning, and document analysis benchmarks.
-- **Multilingual Support**: Understands and responds in multiple languages.
-- **Optimized for Efficiency**: Delivers strong performance at 4B scale for on-device or edge deployment.
+- **Instruction-Following**: Optimized for dialogue, explanation, and user-friendly task completion.  
+- **Vision-Language Fusion**: Understands and reasons across text, images, and video frames.  
+- **Multilingual Capability**: Handles multiple languages for diverse global use cases.  
+- **Contextual Coherence**: Balances reasoning ability with natural, grounded conversational tone.  
+- **Lightweight & Deployable**: 4B parameters make it efficient for edge and device-level inference.
 
 ## Use Cases
-- Multimodal reasoning and visual question answering  
-- Scientific and analytical reasoning tasks involving charts, tables, and documents  
-- Step-by-step visual explanation or tutoring  
-- Research on interpretability and chain-of-thought modeling  
-- Integration into agent systems that require structured reasoning  
+- Visual chatbots and assistants  
+- Image captioning and scene understanding  
+- Chart, document, or screenshot analysis  
+- Educational or tutoring systems with visual inputs  
+- Multilingual, multimodal question answering  
 
 ## Inputs and Outputs
 **Input:**
-- Text, images, or combined multimodal prompts (e.g., image + question)
+- Text prompts, image(s), or mixed multimodal instructions.  
 
 **Output:**
-- Generated text, reasoning traces, or structured responses  
-- May include explicit thought steps or structured JSON reasoning sequences  
+- Natural-language responses or visual reasoning explanations.  
+- Can return structured text (summaries, captions, answers, etc.) depending on the prompt.
 
 ## License
-Check the [official Qwen license](https://huggingface.co/Qwen) for terms of use and redistribution.
+Refer to the [official Qwen license](https://huggingface.co/Qwen) for terms of use and redistribution.