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

Matching Guided Distillation (ECCV 2020)

Our open source implementation of MiniLMv2 (https://aclanthology.org/2021.findings-acl.188)

The Codebase for Causal Distillation for Language Models (NAACL '22)

A framework for knowledge distillation using TensorRT inference on teacher network

Repository for the publication "AutoGraph: Predicting Lane Graphs from Traffic"

The Codebase for Causal Distillation for Task-Specific Models

Awesome Deep Model Compression

Use AWS Rekognition to train custom models that you own.

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.

Model distillation of CNNs for classification of Seafood Images in PyTorch

[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.