princeton-nlp / CoFiPruningLinks
[ACL 2022] Structured Pruning Learns Compact and Accurate Models https://arxiv.org/abs/2204.00408
☆196Updated 2 years ago
Alternatives and similar repositories for CoFiPruning
Users that are interested in CoFiPruning are comparing it to the libraries listed below
Sorting:
- This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).☆109Updated 3 years ago
- Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models☆142Updated 2 years ago
- [KDD'22] Learned Token Pruning for Transformers☆98Updated 2 years ago
- Must-read papers on improving efficiency for pre-trained language models.☆104Updated 2 years ago
- ICML'2022: Black-Box Tuning for Language-Model-as-a-Service & EMNLP'2022: BBTv2: Towards a Gradient-Free Future with Large Language Model…☆270Updated 2 years ago
- Code for ACL 2023 paper titled "Lifting the Curse of Capacity Gap in Distilling Language Models"☆28Updated 2 years ago
- ☆32Updated 3 years ago
- ☆139Updated last year
- Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃☆114Updated 2 years ago
- This package implements THOR: Transformer with Stochastic Experts.☆65Updated 3 years ago
- The code of paper "Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation" published at NeurIPS 202…☆46Updated 2 years ago
- Code for the ACL-2022 paper "StableMoE: Stable Routing Strategy for Mixture of Experts"☆48Updated 3 years ago
- DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference☆157Updated 3 years ago
- Block Sparse movement pruning☆81Updated 4 years ago
- A curated list of Early Exiting papers, benchmarks, and misc.☆117Updated last year
- Source code for our AAAI'22 paper 《From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression》☆25Updated 3 years ago
- Method to improve inference time for BERT. This is an implementation of the paper titled "PoWER-BERT: Accelerating BERT Inference via Pro…☆61Updated 2 months ago
- Code for the paper "Are Sixteen Heads Really Better than One?"☆172Updated 5 years ago
- ☆104Updated 2 years ago
- This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).☆46Updated 2 years ago
- Train llm (bloom, llama, baichuan2-7b, chatglm3-6b) with deepspeed pipeline mode. Faster than zero/zero++/fsdp.☆97Updated last year
- Code associated with the paper **SkipBERT: Efficient Inference with Shallow Layer Skipping**, at ACL 2022☆16Updated 3 years ago
- ☆22Updated 4 years ago
- [NeurIPS 2022] A Fast Post-Training Pruning Framework for Transformers☆191Updated 2 years ago
- ☆99Updated 3 years ago
- This is the implementation of the paper AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning (https://arxiv.org/abs/2205.1…☆132Updated last year
- Code for ACL 2022 paper "BERT Learns to Teach: Knowledge Distillation with Meta Learning".☆86Updated 3 years ago
- An original implementation of "MetaICL Learning to Learn In Context" by Sewon Min, Mike Lewis, Luke Zettlemoyer and Hannaneh Hajishirzi☆270Updated 2 years ago
- Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》☆61Updated 3 years ago
- Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method ; GKD: A General Knowledge Distillation…☆32Updated 2 years ago