Alternatives and similar repositories for papa

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

• pietrolesci / memorisation-profiles
  This is the official implementation for our ACL 2024 paper: "Causal Estimation of Memorisation Profiles".
  ☆23Updated 9 months ago
• pkuzengqi / Skyformer
  Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method (NeurIPS 2021)
  ☆63Updated 3 years ago
• princeton-nlp / LM-Kernel-FT
  A Kernel-Based View of Language Model Fine-Tuning https://arxiv.org/abs/2210.05643
  ☆78Updated 2 years ago
• 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…
  ☆56Updated 2 years ago
• ThomasScialom / T0_continual_learning
  Adding new tasks to T0 without catastrophic forgetting
  ☆33Updated 3 years ago
• xiamengzhou / training_trajectory_analysis
  [ACL 2023]: Training Trajectories of Language Models Across Scales https://arxiv.org/pdf/2212.09803.pdf
  ☆25Updated 2 years ago
• dguo98 / DiffPruning
  Parameter Efficient Transfer Learning with Diff Pruning
  ☆74Updated 4 years ago
• xtinkt / editable
  A supplementary code for Editable Neural Networks, an ICLR 2020 submission.
  ☆46Updated 5 years ago
• VITA-Group / EarlyBERT
  [ACL-IJCNLP 2021] "EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets" by Xiaohan Chen, Yu Cheng, Shuohang Wang, Zhe Gan, …
  ☆18Updated 3 years ago
• lsj2408 / URPE
  [NeurIPS 2022] Your Transformer May Not be as Powerful as You Expect (official implementation)
  ☆34Updated 2 years ago
• frankxu2004 / knnlm-why
  Repo for ICML23 "Why do Nearest Neighbor Language Models Work?"
  ☆59Updated 2 years ago
• microsoft / Stochastic-Mixture-of-Experts
  This package implements THOR: Transformer with Stochastic Experts.
  ☆65Updated 4 years ago
• bigscience-workshop / architecture-objective
  ☆98Updated 2 years ago
• ekinakyurek / influence
  Code for "Tracing Knowledge in Language Models Back to the Training Data"
  ☆39Updated 2 years ago
• allenai / staged-training
  Staged Training for Transformer Language Models
  ☆33Updated 3 years ago
• JeanKaddour / NoTrainNoGain
  Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)
  ☆81Updated 2 years ago
• archiki / GrIPS
  Code for our paper: "GrIPS: Gradient-free, Edit-based Instruction Search for Prompting Large Language Models"
  ☆57Updated 2 years ago
• lucidrains / coco-lm-pytorch
  Implementation of COCO-LM, Correcting and Contrasting Text Sequences for Language Model Pretraining, in Pytorch
  ☆46Updated 4 years ago
• JetRunner / PABEE
  Code for the paper "BERT Loses Patience: Fast and Robust Inference with Early Exit".
  ☆66Updated 4 years ago
• alon-albalak / FLAD
  Few-shot Learning with Auxiliary Data
  ☆31Updated 2 years ago
• peterbhase / SLAG-Belief-Updating
  Code for paper "Do Language Models Have Beliefs? Methods for Detecting, Updating, and Visualizing Model Beliefs"
  ☆28Updated 3 years ago
• abhishekpanigrahi1996 / transformer_in_transformer
  ☆45Updated 2 years ago
• renll / SparseLT
  [EMNLP 2022] Language Model Pre-Training with Sparse Latent Typing
  ☆14Updated 2 years ago
• guy-dar / embedding-space
  ☆56Updated 2 years ago
• mlpc-ucsd / BERT_Convolutions
  (ACL-IJCNLP 2021) Convolutions and Self-Attention: Re-interpreting Relative Positions in Pre-trained Language Models.
  ☆21Updated 3 years ago
• frankaging / Causal-Distill
  The Codebase for Causal Distillation for Language Models (NAACL '22)
  ☆25Updated 3 years ago
• CyndxAI / QKNorm
  Code for the paper "Query-Key Normalization for Transformers"
  ☆50Updated 4 years ago
• da03 / Residual-EBM
  Code for Residual Energy-Based Models for Text Generation in PyTorch.
  ☆25Updated 4 years ago
• carolinlawrence / gradient-rollback
  Code for gradient rollback, which explains predictions of neural matrix factorization models, as for example used for knowledge base comp…
  ☆21Updated 4 years ago
• cambridgeltl / composable-sft
  A library for parameter-efficient and composable transfer learning for NLP with sparse fine-tunings.
  ☆75Updated last year