Awesome Knowledge Distillation
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Updated
Jun 10, 2025
Awesome Knowledge Distillation
Images to inference with no labeling (use foundation models to train supervised models).
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
[ECCV'20] PyTorch Implementation of Matching Guided Distillation
Our open source implementation of MiniLMv2 (https://aclanthology.org/2021.findings-acl.188)
The Codebase for Causal Distillation for Language Models (NAACL '22)
AI Community Tutorial, including: LoRA/Qlora LLM fine-tuning, Training GPT-2 from scratch, Generative Model Architecture, Content safety and control implementation, Model distillation techniques, Dreambooth techniques, Transfer learning, etc for practice with real project!
A framework for knowledge distillation using TensorRT inference on teacher network
Repository for the publication "AutoGraph: Predicting Lane Graphs from Traffic"
A Segmentation-guided Box Teacher-student Approach For Weakly Supervised Road Segmentation
The Codebase for Causal Distillation for Task-Specific Models
Awesome Deep Model Compression
Use LLaMA to label data for use in training a fine-tuned LLM.
Autodistill Google Cloud Vision module for use in training a custom, fine-tuned model.
Use AWS Rekognition to train custom models that you own.
[Master Thesis] Research project at the Data Analytics Lab in collaboration with Daedalean AI. The thesis was submitted to both ETH Zürich and Imperial College London.
Model distillation of CNNs for classification of Seafood Images in PyTorch
Model distiller automator
Zero-data blackbox machine translation model distillation / stealing
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