Alternatives and similar repositories for MaskLLM:

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

• astramind-ai / Mixture-of-depths
  Unofficial implementation for the paper "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"
  ☆157Updated 9 months ago
• kyegomez / Mixture-of-Depths
  Implementation of the paper: "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"
  ☆89Updated this week
• Lucky-Lance / Expert_Sparsity
  [ACL 2024] Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models
  ☆86Updated 10 months ago
• liyunqianggyn / Awesome-LLMs-Pruning
  Awesome LLM pruning papers all-in-one repository with integrating all useful resources and insights.
  ☆83Updated 4 months ago
• UNITES-Lab / MC-SMoE
  [ICLR 2024 Spotlight] Code for the paper "Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy"
  ☆79Updated 10 months ago
• hahnyuan / ASVD4LLM
  Activation-aware Singular Value Decomposition for Compressing Large Language Models
  ☆62Updated 5 months ago
• mit-han-lab / duo-attention
  [ICLR 2025] DuoAttention: Efficient Long-Context LLM Inference with Retrieval and Streaming Heads
  ☆451Updated 2 months ago
• thu-nics / MoA
  The official implementation of the paper <MoA: Mixture of Sparse Attention for Automatic Large Language Model Compression>
  ☆123Updated 4 months ago
• CASE-Lab-UMD / Unified-MoE-Compression
  The official implementation of the paper "Towards Efficient Mixture of Experts: A Holistic Study of Compression Techniques (TMLR)".
  ☆66Updated last month
• ChenMnZ / PrefixQuant
  An algorithm for weight-activation quantization (W4A4, W4A8) of LLMs, supporting both static and dynamic quantization
  ☆127Updated 2 months ago
• locuslab / massive-activations
  Code accompanying the paper "Massive Activations in Large Language Models"
  ☆154Updated last year
• FasterDecoding / TEAL
  ☆122Updated 2 months ago
• jy-yuan / KIVI
  [ICML 2024] KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache
  ☆288Updated 3 months ago
• yxli2123 / LoftQ
  ☆219Updated 10 months ago
• TianjinYellow / EdgeDeviceLLMCompetition-Starting-Kit
  ☆43Updated 5 months ago
• jxiw / MambaInLlama
  [NeurIPS 2024] Official Repository of The Mamba in the Llama: Distilling and Accelerating Hybrid Models
  ☆212Updated 2 weeks ago
• pprp / Pruner-Zero
  Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for LLMs
  ☆81Updated 4 months ago
• CASE-Lab-UMD / LLM-Drop
  The official implementation of the paper "What Matters in Transformers? Not All Attention is Needed".
  ☆166Updated 3 weeks ago
• sramshetty / ShortGPT
  Unofficial implementations of block/layer-wise pruning methods for LLMs.
  ☆68Updated 11 months ago
• FasterDecoding / SnapKV
  ☆235Updated 11 months ago
• hahnyuan / PB-LLM
  PB-LLM: Partially Binarized Large Language Models
  ☆151Updated last year
• NVlabs / COAT
  [ICLR 2025] COAT: Compressing Optimizer States and Activation for Memory-Efficient FP8 Training
  ☆182Updated last week
• luuyin / OWL
  Official Pytorch Implementation of "Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High Sparsity"
  ☆64Updated 9 months ago
• jiwonsong-dev / SLEB
  Official Implementation of SLEB: Streamlining LLMs through Redundancy Verification and Elimination of Transformer Blocks
  ☆36Updated 2 months ago
• pprp / Awesome-LLM-Prune
  Awesome list for LLM pruning.
  ☆222Updated 4 months ago
• nbasyl / DoRA
  Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"
  ☆123Updated 11 months ago
• wuhy68 / Parameter-Efficient-MoE
  Parameter-Efficient Sparsity Crafting From Dense to Mixture-of-Experts for Instruction Tuning on General Tasks
  ☆142Updated 6 months ago
• OpenGVLab / EfficientQAT
  EfficientQAT: Efficient Quantization-Aware Training for Large Language Models
  ☆263Updated 6 months ago
• FasterDecoding / BitDelta
  ☆197Updated 4 months ago
• hao-ai-lab / Dynasor
  Simple extension on vLLM to help you speed up reasoning model without training.
  ☆146Updated last month