fkodom / grouped-query-attention-pytorchLinks
(Unofficial) PyTorch implementation of grouped-query attention (GQA) from "GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints" (https://arxiv.org/pdf/2305.13245.pdf)
☆181Updated last year
Alternatives and similar repositories for grouped-query-attention-pytorch
Users that are interested in grouped-query-attention-pytorch are comparing it to the libraries listed below
Sorting:
- Lightning Attention-2: A Free Lunch for Handling Unlimited Sequence Lengths in Large Language Models☆333Updated 8 months ago
- Official implementation of TransNormerLLM: A Faster and Better LLM☆248Updated last year
- Unofficial implementation for the paper "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆175Updated last year
- ☆234Updated last year
- ☆213Updated last year
- Low-bit optimizers for PyTorch☆132Updated 2 years ago
- TransMLA: Multi-Head Latent Attention Is All You Need (NeurIPS 2025 Spotlight)☆407Updated last month
- Root Mean Square Layer Normalization☆256Updated 2 years ago
- ☆199Updated last year
- Implementation of FlashAttention in PyTorch☆173Updated 10 months ago
- Rectified Rotary Position Embeddings☆382Updated last year
- Official PyTorch implementation of DistiLLM: Towards Streamlined Distillation for Large Language Models (ICML 2024)☆238Updated 8 months ago
- Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"☆124Updated last year
- Implementation of "Attention Is Off By One" by Evan Miller☆196Updated 2 years ago
- Implementation of Speculative Sampling as described in "Accelerating Large Language Model Decoding with Speculative Sampling" by Deepmind☆106Updated last year
- Implementation of the paper: "Mixture-of-Depths: Dynamically allocating compute in transformer-based language models"☆109Updated last week
- This PyTorch package implements MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptation (NAACL 2022).☆112Updated 3 years ago
- Inference Code for Paper "Harder Tasks Need More Experts: Dynamic Routing in MoE Models"☆66Updated last year
- ☆271Updated 2 years ago
- Training code for Baby-Llama, our submission to the strict-small track of the BabyLM challenge.☆84Updated 2 years ago
- Code repo for the paper "LLM-QAT Data-Free Quantization Aware Training for Large Language Models"☆319Updated 8 months ago
- Code associated with the paper **Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding**☆208Updated 9 months ago
- AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning (ICLR 2023).☆360Updated 2 years ago
- ☆141Updated last year
- [ACL 2024] Long-Context Language Modeling with Parallel Encodings☆166Updated last year
- [ICML'24 Oral] The official code of "DiJiang: Efficient Large Language Models through Compact Kernelization", a novel DCT-based linear at…☆104Updated last year
- [ICLR2025] Codebase for "ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing", built on Megatron-LM.☆98Updated 10 months ago
- Efficient Mixture of Experts for LLM Paper List☆143Updated last month
- ☆156Updated 2 years ago
- Experiments on Multi-Head Latent Attention☆98Updated last year