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# Phi-3-Vision VLM Model for Apple MLX: An All-in-One Port
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This project brings the powerful phi-3-vision VLM to Apple's MLX framework, offering a comprehensive solution for various text and image processing tasks. With a focus on simplicity and efficiency, this implementation offers a straightforward and minimalistic integration of the VLM model. It seamlessly incorporates essential functionalities such as generating quantized model weights, optimizing KV cache quantization during inference, facilitating LoRA/QLoRA training, and conducting model benchmarking, all encapsulated within a single file for convenient access and usage.
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### **
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```python
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# from phi_3_vision_mlx import chat
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---
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license: mit
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language:
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- en
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library_name: mlx
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tags:
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- vqa
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- vlm
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- llm
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- phi
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---
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# Phi-3-Vision VLM Model for Apple MLX: An All-in-One Port
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This project brings the powerful phi-3-vision VLM to Apple's MLX framework, offering a comprehensive solution for various text and image processing tasks. With a focus on simplicity and efficiency, this implementation offers a straightforward and minimalistic integration of the VLM model. It seamlessly incorporates essential functionalities such as generating quantized model weights, optimizing KV cache quantization during inference, facilitating LoRA/QLoRA training, and conducting model benchmarking, all encapsulated within a single file for convenient access and usage.
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### **Visual Question Answering (VQA)**
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Simply drag and drop screenshot images from clipboard into the chatui textbox or upload images files for VQA.
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Or,
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```python
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# from phi_3_vision_mlx import chat
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