ZZK (MARD1NO)

MARD1NO

Geek Repo

Company:SiliconFlow

Location:Neverland

Home Page:https://mard1no.github.io/

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ZZK's repositories

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gpt-fast

Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.

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cutlass_master

CUDA Templates for Linear Algebra Subroutines

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APPy

APPy (Annotated Parallelism for Python) enables users to annotate loops and tensor expressions in Python with compiler directives akin to OpenMP, and automatically compiles the annotated code to GPU kernels.

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attorch

A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.

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auto-round

SOTA Weight-only Quantization Algorithm for LLMs

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BitBLAS

BitBLAS is a library to support mixed-precision matrix multiplications, especially for quantized LLM deployment.

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cccl

CUDA C++ Core Libraries

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cudnn-frontend

cudnn_frontend provides a c++ wrapper for the cudnn backend API and samples on how to use it

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EETQ

Easy and Efficient Quantization for Transformers

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faster-nougat

Implementation of nougat that focuses on processing pdf locally.

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fp6_llm

An efficient GPU support for LLM inference with 6-bit quantization (FP6).

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GPUSorting

OneSweep, implemented in CUDA, D3D12, and Unity style compute shaders. Theoretically portable to all wave/warp/subgroup sizes.

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KIVI

KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

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KVQuant

KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

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lightning-thunder

Make PyTorch models up to 40% faster! Thunder is a source to source compiler for PyTorch. It enables using different hardware executors at once; across one or thousands of GPUs.

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LLMRoofline

Compare different hardware platforms via the Roofline Model for LLM inference tasks.

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open-gpu-kernel-modules

NVIDIA Linux open GPU with P2P support

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qllm-eval

Code Repository of Evaluating Quantized Large Language Models

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quanto

A pytorch Quantization Toolkit

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TensorRT-Model-Optimizer

TensorRT Model Optimizer is a unified library of state-of-the-art model optimization techniques such as quantization and sparsity. It compresses deep learning models for downstream deployment frameworks like TensorRT-LLM or TensorRT to optimize inference speed on NVIDIA GPUs.

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ThunderKittens

Tile primitives for speedy kernels

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tiny-gpu

A minimal GPU design in Verilog to learn how GPUs work from the ground up

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triton

Development repository for the Triton language and compiler

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Triton-Puzzles

Puzzles for learning Triton

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