Alternatives and similar repositories for LTP

Users that are interested in LTP are comparing it to the libraries listed below

• princeton-nlp / CoFiPruning
  [ACL 2022] Structured Pruning Learns Compact and Accurate Models https://arxiv.org/abs/2204.00408
  ☆197Updated 2 years ago
• huggingface / block_movement_pruning
  Block Sparse movement pruning
  ☆81Updated 4 years ago
• WoosukKwon / retraining-free-pruning
  [NeurIPS 2022] A Fast Post-Training Pruning Framework for Transformers
  ☆192Updated 2 years ago
• microsoft / Stochastic-Mixture-of-Experts
  This package implements THOR: Transformer with Stochastic Experts.
  ☆65Updated 3 years ago
• HKUNLP / efficient-attention
  [EVA ICLR'23; LARA ICML'22] Efficient attention mechanisms via control variates, random features, and importance sampling
  ☆86Updated 2 years ago
• yxli2123 / LoSparse
  ☆60Updated last year
• QingruZhang / PLATON
  This pytorch package implements PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance (ICML 2022).
  ☆46Updated 2 years ago
• SimiaoZuo / MoEBERT
  This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).
  ☆112Updated 3 years ago
• kssteven418 / BigLittleDecoder
  [NeurIPS'23] Speculative Decoding with Big Little Decoder
  ☆94Updated last year
• thunlp / MoEfication
  ☆139Updated last year
• txsun1997 / awesome-early-exiting
  A curated list of Early Exiting papers, benchmarks, and misc.
  ☆117Updated last year
• CASIA-IVA-Lab / FLAP
  [AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models
  ☆61Updated last year
• teelinsan / parallel-decoding
  Repository of the paper "Accelerating Transformer Inference for Translation via Parallel Decoding"
  ☆120Updated last year
• Hunter-DDM / stablemoe
  Code for the ACL-2022 paper "StableMoE: Stable Routing Strategy for Mixture of Experts"
  ☆50Updated 3 years ago
• dguo98 / DiffPruning
  Parameter Efficient Transfer Learning with Diff Pruning
  ☆74Updated 4 years ago
• ruikangliu / IntactKV
  [ACL 2024] Official PyTorch implementation of "IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact"
  ☆48Updated last year
• berlino / gated_linear_attention
  ☆105Updated last year
• rycolab / differentiable-subset-pruning
  ☆15Updated 4 years ago
• hemingkx / SpecDec
  Codes for our paper "Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation" (EMNLP 2023 Findings)
  ☆44Updated last year
• raymin0223 / fast_robust_early_exit
  Fast and Robust Early-Exiting Framework for Autoregressive Language Models with Synchronized Parallel Decoding (EMNLP 2023 Long)
  ☆63Updated last year
• hdong920 / LESS
  ☆52Updated last year
• VITA-Group / Random-MoE-as-Dropout
  [ICLR 2023] "Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers" by Tianlong Chen*, Zhenyu Zhang*, Ajay Jaiswal…
  ☆55Updated 2 years ago
• htqin / BiBERT
  This project is the official implementation of our accepted ICLR 2022 paper BiBERT: Accurate Fully Binarized BERT.
  ☆88Updated 2 years ago
• ziplab / QLLM
  [ICLR 2024] This is the official PyTorch implementation of "QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Mod…
  ☆30Updated last year
• sanagno / adaptively_sparse_attention
  ☆21Updated 2 years ago
• twinkle0331 / Xcompression
  [ICLR 2022] Code for paper "Exploring Extreme Parameter Compression for Pre-trained Language Models"(https://arxiv.org/abs/2205.10036)
  ☆22Updated 2 years ago
• HazyResearch / fly
  ☆218Updated 2 years ago
• ag1988 / top_k_attention
  The accompanying code for "Memory-efficient Transformers via Top-k Attention" (Ankit Gupta, Guy Dar, Shaya Goodman, David Ciprut, Jonatha…
  ☆70Updated 4 years ago
• Shwai-He / MEO
  The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":
  ☆38Updated last year
• haochengxi / Train_Transformers_with_INT4
  ☆156Updated 2 years ago