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phi-3.5-mini,-vision
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README.md

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@@ -4,8 +4,8 @@ Phi-3-MLX is a versatile AI framework that leverages both the Phi-3-Vision multi
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## Features
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- Integration with Phi-3-Vision (multimodal) model
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- Support for the Phi-3-Mini-128K (language-only) model
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- Integration with [Phi-3.5-vision](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) model
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- Support for the [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) model
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- Optimized performance on Apple Silicon using MLX
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- Batched generation for processing multiple prompts
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- Flexible agent system for various AI tasks

phi_3_vision_mlx.py

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@@ -246,8 +246,8 @@ def to_lora(layer):
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def _setup():
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paths = [
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("microsoft/Phi-3-mini-128k-instruct", PATH_ORIGINAL_PHI3_BLIND, PATH_QUANTIZED_PHI3_BLIND),
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("microsoft/Phi-3-vision-128k-instruct", PATH_ORIGINAL_PHI3_VISION, PATH_QUANTIZED_PHI3_VISION)
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("microsoft/Phi-3.5-mini-instruct", PATH_ORIGINAL_PHI3_BLIND, PATH_QUANTIZED_PHI3_BLIND),
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("microsoft/Phi-3.5-vision-instruct", PATH_ORIGINAL_PHI3_VISION, PATH_QUANTIZED_PHI3_VISION)
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]
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for hub, local, quant in paths:
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raw = snapshot_download(repo_id=hub, allow_patterns=["*.safetensors", "*.json"])
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"""
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prompts = [
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('Write a mystery horror.', ),
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('What is shown in this image?', 'https://assets-c4akfrf5b4d3f4b7.z01.azurefd.net/assets/2024/04/BMDataViz_661fb89f3845e.png'),
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('What is shown in this image?', 'https://collectionapi.metmuseum.org/api/collection/v1/iiif/344291/725918/main-image'),
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([
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"Write an executive summary for a communications business plan",
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"Explain quantum computing.",

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