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spacelabllm's Projects

agents icon agents

An Open-source Framework for Autonomous Language Agents

agrol icon agrol

Code release for "Avatars Grow Legs Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model", CVPR 2023

ane icon ane

Reverse engineered Linux driver for the Apple Neural Engine (ANE).

apex icon apex

A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch

autodiff icon autodiff

Symbolic differentiation engine for optimization-based machine learning models.

autolabel icon autolabel

Label, clean and enrich text datasets with LLMs. Discord: https://discord.gg/fweVnRx6CU

backprop icon backprop

Backpropagation in Python, C++, and Cuda

bundlesdf icon bundlesdf

[CVPR 2023] BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown Objects

calm icon calm

C(UDA) accelerated language model inference

cogvlm icon cogvlm

a state-of-the-art-level open visual language model | 多模态预训练模型

cuda-int8-gemm icon cuda-int8-gemm

CUDA 8-bit Tensor Core Matrix Multiplication based on m16n16k16 WMMA API

cuda_gemm icon cuda_gemm

A simple high performance CUDA GEMM implementation.

cudann icon cudann

Neural network framework in C++/Eigen and CUDA.

cudpp icon cudpp

CUDA Data Parallel Primitives Library

efficient-tuning-llms icon efficient-tuning-llms

大模型高效量化训练+部署 (Efficient Finetuning of QLoRA LLMs). QLoRA, LLama, bloom, baichuan-7B , baichuan-13B, GLM

eigenmha icon eigenmha

Forward and backward Attention DNN operators implementationed by LibTorch, cuDNN, and Eigen.

exllama icon exllama

A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.

fadbad icon fadbad

fadbad++定义了用于执行自动微分的c++模板函数

fastimu icon fastimu

the IMU library to rule them all (wip)

flash-ffn icon flash-ffn

fast fused feed forward network (still developping ...)

frp icon frp

A fast reverse proxy to help you expose a local server behind a NAT or firewall to the internet.

gemm_mma icon gemm_mma

Optimize GEMM with tensorcore step by step

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