Alternatives and similar repositories for PEAK

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

• twinkle0331 / LGTM
  [ACL 2023] Code for paper “Tailoring Instructions to Student’s Learning Levels Boosts Knowledge Distillation”(https://arxiv.org/abs/2305.…
  ☆38Updated 2 years ago
• tianyi-lab / Mosaic-IT
  [ACL'25] Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
  ☆19Updated last month
• TencentARC / pi-Tuning
  Official code for "pi-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task Interpolation", ICML 2023.
  ☆33Updated 2 years ago
• BaohaoLiao / mefts
  [NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
  ☆31Updated 2 years ago
• gzcch / Bingo
  ☆55Updated last year
• maszhongming / ParaKnowTransfer
  Code for "Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric Perspective"
  ☆32Updated last year
• JasonForJoy / Model-Editing-Hurt
  EMNLP 2024: Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue
  ☆35Updated 2 months ago
• LooperXX / ManagerTower
  Code for ACL 2023 Oral Paper: ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning
  ☆11Updated 7 months ago
• EnnengYang / RepresentationSurgery
  Representation Surgery for Multi-Task Model Merging. ICML, 2024.
  ☆46Updated 10 months ago
• ImKeTT / ZeroGen
  [NLPCC'23] ZeroGen: Zero-shot Multimodal Controllable Text Generation with Multiple Oracles PyTorch Implementation
  ☆13Updated last year
• yikangshen / MoA
  Mixture of Attention Heads
  ☆48Updated 2 years ago
• xiangyu-mm / EasyGen
  The official code for paper "EasyGen: Easing Multimodal Generation with a Bidirectional Conditional Diffusion Model and LLMs"
  ☆74Updated 8 months ago
• lapisrocks / DiscreteAdversarialDistillation
  [NeurIPS 2023] Official repository for "Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models"
  ☆12Updated last year
• wangxu0820 / NegativePrompt
  The official GitHub page for paper "NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional St…
  ☆23Updated last year
• arazd / ResidualPrompts
  Residual Prompt Tuning: a method for faster and better prompt tuning.
  ☆55Updated 2 years ago
• locuslab / T-MARS
  Code for T-MARS data filtering
  ☆35Updated last year
• Pbihao / SLM
  ☆28Updated last year
• ShiZhengyan / DePT
  [ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"
  ☆95Updated last year
• HKUNLP / efficient-attention
  [EVA ICLR'23; LARA ICML'22] Efficient attention mechanisms via control variates, random features, and importance sampling
  ☆86Updated 2 years ago
• song-wx / SIFT
  [ICML2024 Spotlight] Fine-Tuning Pre-trained Large Language Models Sparsely
  ☆23Updated last year
• ZHZisZZ / weak-to-strong-search
  [NeurIPS'24] Weak-to-Strong Search: Align Large Language Models via Searching over Small Language Models
  ☆62Updated 8 months ago
• yiren-jian / BLIText
  [NeurIPS 2023] Bootstrapping Vision-Language Learning with Decoupled Language Pre-training
  ☆25Updated last year
• ExplainableML / fomo_in_flux
  Code and benchmark for the paper: "A Practitioner's Guide to Continual Multimodal Pretraining" [NeurIPS'24]
  ☆57Updated 8 months 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…
  ☆53Updated 2 years ago
• yule-BUAA / MergeLLM
  Codes for Merging Large Language Models
  ☆33Updated last year
• Pranjal2041 / AdaptiveConsistency
  Let's Sample Step by Step: Adaptive-Consistency for Efficient Reasoning with LLMs
  ☆38Updated last year
• adymaharana / d2pruning
  ☆35Updated last year
• mlfoundations / clip_quality_not_quantity
  ☆29Updated 2 years ago
• ggjy / vision_weak_to_strong
  ☆38Updated last year
• keven980716 / weak-to-strong-deception
  [ICLR 2025] Code&Data for the paper "Super(ficial)-alignment: Strong Models May Deceive Weak Models in Weak-to-Strong Generalization"
  ☆13Updated last year