Code for "LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding", ACL 2024
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Updated
Jul 20, 2026 - Python
Code for "LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding", ACL 2024
A curated list of early exiting (LLM, CV, NLP, etc)
Dynamic per-token early exit for LLM inference. Skip layers tokens don't need
Artifact for "Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving" [SOSP '24]
[NeurIPS'25] FreqExit: Enabling Early-Exit Inference for Visual Autoregressive Models via Frequency-Aware Guidance
Official PyTorch implementation of "LGViT: Dynamic Early Exiting for Accelerating Vision Transformer" (ACM MM 2023)
Code for paper "EdgeKE: An On-Demand Deep Learning IoT System for Cognitive Big Data on Industrial Edge Devices"
A deep learning framework that implements Early Exit strategies in Convolutional Neural Networks (CNNs) using Deep Q-Learning (DQN). This project enhances computational efficiency by dynamically determining the optimal exit point in a neural network for image classification tasks on CIFAR-10.
Official repository of Busolin et al., "Learning Early Exit Strategies for Additive Ranking Ensembles", ACM SIGIR 2021.
This repository provides an implementation of Early Exit Neural Network (EENN) and provides a way to profile the processing time of the model.
Deployment-oriented early-exit toolkit for PyTorch vision models, with per-head calibration, fair benchmarks, and staged inference.
The project aim to experiment implementing a modular architecture: an early-exit model and testing it using Tensorflow.
This repository is dedicated to self-learning about early exit papers, including relevant code and documentation.
Relational Time Engine (RTE): runtime density regulation for compute-efficient transformer inference. Demonstrates up to 75% layer reduction with improved latency and throughput.
VerDee: Vertical Deep Network on Mamba-2 — staged LoRA (shallow/mid/deep), early-exit routing, and domain experts. Pilot on 370M; 16GB GPU friendly. Research/experiment repo.
This project focuses on the automatic classification of corn leaf diseases using deep neural networks. The dataset includes over 4000 images categorized into four classes: Common Rust, Gray Leaf Spot, Blight, and Healthy. Through the use of Convolutional Neural Networks and advanced techniques, the model achieves a classification accuracy of 91.5%
[ICML 2026] Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models
C implementation of a SHA-1 cracker with various optimizations
Early exit inference framework for HuggingFace LLMs — skip unnecessary transformer layers when the model is already confident. Supports LLaMA, Mistral, Phi, Gemma, Qwen, Pythia and more.
Confidence-gated early-exit inference for Llama 3.1 8B, with distillation, calibration, segmented multi-GPU runtime, and evaluation.
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