pan-x-c / EE-LLM
EE-LLM is a framework for large-scale training and inference of early-exit (EE) large language models (LLMs).
☆57Updated 10 months ago
Alternatives and similar repositories for EE-LLM:
Users that are interested in EE-LLM are comparing it to the libraries listed below
- GEAR: An Efficient KV Cache Compression Recipefor Near-Lossless Generative Inference of LLM☆159Updated 9 months ago
- [ICLR2025] Breaking Throughput-Latency Trade-off for Long Sequences with Speculative Decoding☆113Updated 4 months ago
- ☆122Updated 2 months ago
- Unofficial implementations of block/layer-wise pruning methods for LLMs.☆68Updated 11 months ago
- [NeurIPS 2024] The official implementation of "Kangaroo: Lossless Self-Speculative Decoding for Accelerating LLMs via Double Early Exitin…☆51Updated 9 months ago
- Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks☆36Updated 2 months ago
- This repo contains the source code for: Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs☆36Updated 8 months ago
- ☆48Updated 4 months ago
- Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)☆57Updated 6 months ago
- Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"☆64Updated 9 months ago
- Activation-aware Singular Value Decomposition for Compressing Large Language Models☆62Updated 5 months ago
- Unofficial implementation for the paper "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆157Updated 9 months ago
- ☆36Updated 7 months ago
- [ICML 2024] KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache☆288Updated 3 months ago
- Explorations into some recent techniques surrounding speculative decoding☆254Updated 3 months ago
- The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".☆66Updated last month
- 16-fold memory access reduction with nearly no loss☆89Updated 3 weeks ago
- PyTorch implementation of paper "Response Length Perception and Sequence Scheduling: An LLM-Empowered LLM Inference Pipeline".☆85Updated last year
- Boosting 4-bit inference kernels with 2:4 Sparsity☆72Updated 7 months ago
- Ouroboros: Speculative Decoding with Large Model Enhanced Drafting (EMNLP 2024 main)☆101Updated 3 weeks ago
- PB-LLM: Partially Binarized Large Language Models☆151Updated last year
- Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs☆81Updated 4 months ago
- The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>☆123Updated 4 months ago
- ☆43Updated last year
- [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"☆79Updated 10 months ago
- ☆235Updated 11 months ago
- [ICML 2024] Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference☆269Updated 4 months ago
- An unofficial implementation of "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆35Updated 10 months ago
- Code associated with the paper **Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding**☆181Updated 2 months ago
- [ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.☆23Updated last year