Ladder Side-Tuning:预训练模型的“过墙梯”kexue.fm/archives/9138 如果说大型的预训练模型是自然语言处理的“张良计”,那么对应的“过墙梯”是什么呢?笔者认为是高效地微调这些大模型到特定任务上的各种技巧。除了直接微调全部参数外,还有像Adapter、P-Tuning等很多参数高效的微调技巧,它们能够通过只微调很少的参...
Github:https://github.com/bojone/LST-CLUE 注意,原论文的“梯子”是用跟 Adapter 中的 MLP 层来搭建的,而笔者上述实现直接用了 Transformer 一样的“Attention + FFN”组合,可训练的参数量控制在 100 万左右,约为 base 版的 1.2%,或者 large 版的 0.4%,梯子的初始化直接用随机初始化,最终在验证集的效果...
When i run: bash scripts/baseline.sh "1" $"cola" It reports: FileNotFoundError: Couldn't find remote file with version master at https://raw.githubusercontent.com/huggingface/datasets/master/datasets/glue/glue.py. Please provide a valid ...
Github:https://github.com/bojone/LST-CLUE 注意,原论文的“梯子”是用跟 Adapter 中的 MLP 层来搭建的,而笔者上述实现直接用了 Transformer 一样的“Attention + FFN”组合,可训练的参数量控制在 100 万左右,约为 base 版的 1.2%,或者 large 版的 0.4%,梯子的初始化直接用随机初始化,最终在验证集的效果...
RUN git clone https://github.com/LeiWang1999/Ladder --recursive -b develop Ladder\ && cd Ladder && maint/scripts/installation.shENV PYTHONPATH /root/tvm/python:$PYTHONPATH ENV PYTHONPATH /root/Ladder/3rdparty/tvm/python:$PYTHONPATHRUN
BananaBrain 3305 - - 50% 1774 1808 Come to the dark side; we have candy! BASIL:PUBLISH-READ Mixed Enabled 2024-10-16 17:25:46 Stardust 3243 - - 52% 1804 1526 https://github.com/bmnielsen/Stardust Mixed Enabled 2023-09-28 20:54:14 Hao Pan 3233 - - 54% 1319 1193 Halo by Hao...
git clone --recursive https://github.com/microsoft/BitBLAS --branch osdi24_ladder_artifact Ladder cd Ladder/docker # build the image, this may take a while (around 30+ minutes on our test machine) as we install all benchmark frameworks docker build -t ladder_cuda -f Dockerfile.cu120 ....
RUN git clone https://github.com/LeiWang1999/Ladder --recursive -b develop Ladder\ RUN git clone https://github.com/microsoft/BitBLAS --recursive -b osdi24_ladder_artifact Ladder \ && cd Ladder && maint/scripts/installation.sh ENV PYTHONPATH /root/Ladder/3rdparty/tvm/python:$PYTHONPATH2...
(This may take days to finish the tuning process.) Moreover, even Ladder can have a giant reduction in tuning time, it still takes a long time to tune the all settings (around 40x models need to be tuned to reproduce all the paper data, This may takes around 10 hours to finish all...
Some computation can be moved to the host side if applicable. Grouped Syr2k kernels are added, too. Optimizations for GEMM+Softmax. All the reduction computation is fused into the previous GEMM. More template arguments are provided to fine tune the performance. Grouped GEMM for Multihead ...