Create README.md

README.md

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+# Model Card — Qwen2-VL-ImgChat-2B
+
+## Model Details
+- **Model Name:** Qwen2-VL-ImgChat-2B  
+- **Model Type:** Vision-Language Model fine-tuned for multimodal dialog auto-completion  
+- **Language(s):** English  
+- **Base Model:** Qwen2-VL-2B  
+- **Fine-tuning Dataset:** ImageChat  
+- **License:** Same as base model (Qwen2-VL license)  
+- **Repository:** https://github.com/devichand579/MAC
+
+---
+
+## Intended Use
+
+### Direct Use
+This model generates conversational responses conditioned on both textual and visual context. It is suitable for:
+- Multimodal dialog systems
+- Image-grounded conversational agents
+- Research on multimodal auto-completion
+
+### Out-of-Scope Use
+The model is not intended for:
+- Medical, legal, or financial advice
+- Safety-critical decision-making
+- Autonomous systems requiring guaranteed correctness
+
+---
+
+## Limitations and Risks
+- Model outputs may contain inaccuracies or biases inherited from training data.
+- Performance depends on image relevance and dialogue context quality.
+- The model is not explicitly safety-filtered.
+
+---
+
+## How to Use
+
+Example usage with Hugging Face Transformers:
+
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+processor = AutoProcessor.from_pretrained("devichand/MiniCPM_V_ImgChat-7B")
+model = AutoModelForVision2Seq.from_pretrained("devichand/MiniCPM_V_ImgChat-7B")
+
+inputs = processor(images=your_image,
+                   text="Describe the image.",
+                   return_tensors="pt")
+
+outputs = model.generate(**inputs)
+print(processor.decode(outputs[0]))