devichand/MiniCPM_V_Noimg_ImgChat-7B

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

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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

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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.

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How to Use

Example usage with Hugging Face Transformers:

from transformers import AutoProcessor, AutoModelForVision2Seq

processor = AutoProcessor.from_pretrained("devichand/MiniCPM_V_Noimg_ImgChat-7B")
model = AutoModelForVision2Seq.from_pretrained("devichand/MiniCPM_V_Noimg_ImgChat-7B")

inputs = processor(images=your_image,
                   text="Describe the image.",
                   return_tensors="pt")

outputs = model.generate(**inputs)
print(processor.decode(outputs[0]))