xuxingc's Stars
facebookresearch/moco
PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
clabrugere/wukong-recommendation
Implements the paper "Wukong: Towards a Scaling Law for Large-Scale Recommendation" from Meta.
Ascend/AscendSpeed
meta-llama/llama3
The official Meta Llama 3 GitHub site
yoheinakajima/babyagi
tengyuma/cs229m_notes
chengxiang/LinearTransformer
Pytorch code for experiments on Linear Transformers
xai-org/grok-1
Grok open release
Sohl-Dickstein/fractal
The boundary of neural network trainability is fractal
Ma-Lab-Berkeley/CRATE
Code for CRATE (Coding RAte reduction TransformEr).
facebookresearch/Pearl
A Production-ready Reinforcement Learning AI Agent Library brought by the Applied Reinforcement Learning team at Meta.
srush/do-we-need-attention
p-lambda/incontext-learning
Experiments and code to generate the GINC small-scale in-context learning dataset from "An Explanation for In-context Learning as Implicit Bayesian Inference"
facebookresearch/fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
dair-ai/Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
PKU-YuanGroup/Open-Sora-Plan
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
hoya012/awesome-anomaly-detection
A curated list of awesome anomaly detection resources
lucidrains/denoising-diffusion-pytorch
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
langchain-ai/langchain
🦜🔗 Build context-aware reasoning applications
FedML-AI/FedML
FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.
BaguaSys/bagua
Bagua Speeds up PyTorch
meta-llama/llama
Inference code for Llama models
microsoft/deep-language-networks
We view Large Language Models as stochastic language layers in a network, where the learnable parameters are the natural language prompts at each layer. We stack two such layers, feeding the output of one layer to the next. We call the stacked architecture a Deep Language Network - DLN
cassidylaidlaw/effective-horizon
Code and data for the paper "Bridging RL Theory and Practice with the Effective Horizon"
nlpxucan/WizardLM
LLMs build upon Evol Insturct: WizardLM, WizardCoder, WizardMath
BlinkDL/RWKV-LM
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
xuxingc/ProxDASA
Experiments for ProxDASA paper
gmancino/DEEPSTORM
Experiments for DEEPSTORM paper
ctlllll/LLM-ToolMaker
CompVis/latent-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models