Alternatives and similar repositories for llm-surgeon

Users that are interested in llm-surgeon are comparing it to the libraries listed below

• 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
• mengxiayu / LLMSuperWeight
  Code for studying the super weight in LLM
  ☆121Updated last year
• locuslab / massive-activations
  Code accompanying the paper "Massive Activations in Large Language Models"
  ☆187Updated last year
• JeanKaddour / NoTrainNoGain
  Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)
  ☆81Updated 2 years ago
• ml-jku / EVA
  One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation
  ☆45Updated last month
• epfml / schedules-and-scaling
  Code for NeurIPS 2024 Spotlight: "Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations"
  ☆86Updated last year
• CASE-Lab-UMD / Unified-MoE-Compression
  The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".
  ☆87Updated 8 months ago
• RobertCsordas / moeut
  ☆89Updated last year
• kamanphoebe / Look-into-MoEs
  [NAACL 2025] A Closer Look into Mixture-of-Experts in Large Language Models
  ☆55Updated 10 months ago
• jiwonsong-dev / SLEB
  [ICML 2024] Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
  ☆37Updated 10 months ago
• yxli2123 / LoSparse
  ☆62Updated 2 years ago
• hahnyuan / ASVD4LLM
  Activation-aware Singular Value Decomposition for Compressing Large Language Models
  ☆82Updated last year
• CASE-Lab-UMD / LLM-Drop
  The official implementation of the paper "What Matters in Transformers? Not All Attention is Needed".
  ☆185Updated 3 weeks ago
• andyjm3 / SLTrain
  SLTrain: a sparse plus low-rank approach for parameter and memory efficient pretraining (NeurIPS 2024)
  ☆38Updated last year
• hdong920 / GRIFFIN
  ☆39Updated last year
• song-wx / SIFT
  [ICML2024 Spotlight] Fine-Tuning Pre-trained Large Language Models Sparsely
  ☆24Updated last year
• Lucky-Lance / Expert_Sparsity
  [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
  ☆111Updated last year
• htqin / IR-QLoRA
  [ICML 2024 Oral] This project is the official implementation of our Accurate LoRA-Finetuning Quantization of LLMs via Information Retenti…
  ☆67Updated last year
• mlfoundations / scaling
  Language models scale reliably with over-training and on downstream tasks
  ☆100Updated last year
• minyoungg / LTE
  ☆70Updated last year
• Infini-AI-Lab / Kinetics
  Kinetics: Rethinking Test-Time Scaling Laws
  ☆84Updated 5 months ago
• wang-kee / LiNeS
  Official repository of "LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging"
  ☆31Updated last year
• VijayLingam95 / SVFT
  ☆34Updated 10 months ago
• MadryLab / DsDm
  ☆51Updated last year
• PiotrNawrot / sparse-frontier
  The evaluation framework for training-free sparse attention in LLMs
  ☆106Updated 2 months ago
• VITA-Group / Junk_DNA_Hypothesis
  [ICML 2024] Junk DNA Hypothesis: A Task-Centric Angle of LLM Pre-trained Weights through Sparsity; Lu Yin*, Ajay Jaiswal*, Shiwei Liu, So…
  ☆16Updated 7 months ago
• UNITES-Lab / MC-SMoE
  [ICLR‘24 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"
  ☆100Updated 5 months ago
• gregorbachmann / Next-Token-Failures
  ☆106Updated last year
• epfml / llm-baselines
  nanoGPT-like codebase for LLM training
  ☆113Updated last month
• shreyansh26 / Attention-Mask-Patterns
  Using FlexAttention to compute attention with different masking patterns
  ☆47Updated last year