Alternatives and similar repositories for merging-text-transformers

Users that are interested in merging-text-transformers are comparing it to the libraries listed below

• kamanphoebe / Look-into-MoEs
  [NAACL 2025] A Closer Look into Mixture-of-Experts in Large Language Models
  ☆55Updated 10 months ago
• BaohaoLiao / mefts
  [NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
  ☆33Updated 2 years ago
• yule-BUAA / MergeLLM
  Codes for Merging Large Language Models
  ☆34Updated last year
• hamishivi / automated-instruction-selection
  Exploration of automated dataset selection approaches at large scales.
  ☆50Updated 9 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…
  ☆56Updated 2 years ago
• r-three / smear
  ☆30Updated 2 years ago
• qiuzh20 / EMoE
  Official PyTorch Implementation of EMoE: Unlocking Emergent Modularity in Large Language Models [main conference @ NAACL2024]
  ☆37Updated last year
• prateeky2806 / ties-merging
  ☆200Updated last year
• bloomberg / dataless-model-merging
  Code release for Dataless Knowledge Fusion by Merging Weights of Language Models (https://openreview.net/forum?id=FCnohuR6AnM)
  ☆92Updated 2 years ago
• tianyi-lab / Mosaic-IT
  [ACL'25] Mosaic-IT: Cost-Free Compositional Data Synthesis for Instruction Tuning
  ☆20Updated 2 months ago
• princeton-nlp / Edge-Pruning
  [NeurIPS 2024 Spotlight] Code and data for the paper "Finding Transformer Circuits with Edge Pruning".
  ☆62Updated 3 months ago
• zzwjames / FailureLLMUnlearning
  An official implementation of "Catastrophic Failure of LLM Unlearning via Quantization" (ICLR 2025)
  ☆35Updated 9 months ago
• FeiyuZhang98 / IncreLoRA
  ☆34Updated 2 years ago
• arazd / ResidualPrompts
  Residual Prompt Tuning: a method for faster and better prompt tuning.
  ☆56Updated 2 years ago
• ShiZhengyan / DePT
  [ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"
  ☆99Updated last year
• Shwai-He / MEO
  The source code of "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts (EMNLP 2023)":
  ☆40Updated last year
• EnnengYang / RepresentationSurgery
  Representation Surgery for Multi-Task Model Merging. ICML, 2024.
  ☆47Updated last year
• socialfoundations / tttlm
  Test-time-training on nearest neighbors for large language models
  ☆48Updated last year
• ShiZhengyan / InstructionModelling
  [NeurIPS 2024 Main Track] Code for the paper titled "Instruction Tuning With Loss Over Instructions"
  ☆38Updated last year
• locuslab / massive-activations
  Code accompanying the paper "Massive Activations in Large Language Models"
  ☆187Updated last year
• adymaharana / d2pruning
  ☆43Updated 2 years ago
• cliang1453 / task-aware-distillation
  Less is More: Task-aware Layer-wise Distillation for Language Model Compression (ICML2023)
  ☆40Updated 2 years ago
• fangyuan-ksgk / CoT-Reasoning-without-Prompting
  Unofficial Implementation of Chain-of-Thought Reasoning Without Prompting
  ☆34Updated last year
• hkust-nlp / PreSelect
  [ICML 2025] Predictive Data Selection: The Data That Predicts Is the Data That Teaches
  ☆57Updated 9 months ago
• Leooyii / LCEG
  Long Context Extension and Generalization in LLMs
  ☆62Updated last year
• song-wx / SIFT
  [ICML2024 Spotlight] Fine-Tuning Pre-trained Large Language Models Sparsely
  ☆24Updated last year
• LAMDASZ-ML / Self-Backtracking
  ☆51Updated 10 months ago
• hbin0701 / Self-Explore
  [𝐄𝐌𝐍𝐋𝐏 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 𝟐𝟎𝟐𝟒 & 𝐀𝐂𝐋 𝟐𝟎𝟐𝟒 𝐍𝐋𝐑𝐒𝐄 𝐎𝐫𝐚𝐥] 𝘌𝘯𝘩𝘢𝘯𝘤𝘪𝘯𝘨 𝘔𝘢𝘵𝘩𝘦𝘮𝘢𝘵𝘪𝘤𝘢𝘭 𝘙𝘦𝘢𝘴𝘰𝘯𝘪𝘯…
  ☆51Updated last year
• Infini-AI-Lab / S2FT
  ☆19Updated 11 months ago
• activatedgeek / calibration-tuning
  ☆52Updated 8 months ago