https://github.com/LUMII-Syslab/RSE
공식 코드Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
Papers with Code 아카이브
LAMBADA
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/LUMII-Syslab/shuffle-exchange
공식 코드Neural Shuffle-Exchange Networks -- Sequence Processing in O(n log n) Time
https://github.com/NVIDIA/Megatron-LM
공식 코드Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/amazon-science/llm-asymptotic-decoding
공식 코드https://github.com/harvardnlp/readcomp
공식 코드https://github.com/jdeschena/sdtt
공식 코드Beyond Autoregression: Fast LLMs via Self-Distillation Through Time
https://github.com/microsoft/DeepSpeed
공식 코드The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models
https://github.com/nolanoorg/spectrasuite
공식 코드Spectra: Surprising Effectiveness of Pretraining Ternary Language Models at Scale
https://github.com/tensorflow/tensor2tensor
공식 코드https://github.com/tomyoung903/mlm_inconsistencies
공식 코드https://github.com/Aroksak/RSE
Residual Shuffle-Exchange Networks for Fast Processing of Long Sequences
https://github.com/Beacontownfc/MegatronBert_paddle
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/MostafaDehghani/bAbI-T2T
https://github.com/PaddlePaddle/FleetX/tree/develop/examples/hybrid_parallelism
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/megatronbert
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/THUDM/ProteinLM
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/allenai/dolma
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/andreamad8/Universal-Transformer-Pytorch
https://github.com/bramiozo/PubScience
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/cfiken/universal_transformer
https://github.com/ezelikman/STaR
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/facebookresearch/fairscale
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/karpathy/llama2.c
https://github.com/keyonvafa/sequential-rationales
The LAMBADA dataset: Word prediction requiring a broad discourse context
https://github.com/kingoflolz/mesh-transformer-jax
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/kpot/keras-transformer
https://github.com/makcedward/nlpaug
https://github.com/moon23k/Transformer_Archs
https://github.com/nkluge-correa/teenytinyllama
https://github.com/nvidia/transformerengine
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/qhduan/CPM-LM-TF2
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
https://github.com/rvlopes/gloria
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
https://github.com/sidney1505/arc_maml_transformer
https://github.com/ypeleg/MinimalIsAllYouNeed
https://github.com/zhenwang9102/coherence-boosting
The LAMBADA dataset: Word prediction requiring a broad discourse context
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.