Stars
Tmux plugin for copying to system clipboard. Works on OSX, Linux and Cygwin.
A Community-led Hyper-Hackable Text Editor
An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & TIS & vLLM & Ray & Dynamic Sampling & Async Agentic RL)
Community maintained hardware plugin for vLLM on Ascend
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
TensorFlow wheels (whl) for aarch64 / ARMv8 / ARM64
Simple, minimal implementation of the Mamba SSM in one file of PyTorch.
LLM Finetuning with peft
The new Windows Terminal and the original Windows console host, all in the same place!
Official implementation of Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion Transformer
This is an inference framework for the RWKV large language model implemented purely in native PyTorch. The official native implementation is overly complex and lacks extensibility. Let's join the f…
🍅🍅🍅YOLOv5-Lite: Evolved from yolov5 and the size of model is only 900+kb (int8) and 1.7M (fp16). Reach 15 FPS on the Raspberry Pi 4B~
Open-Sora: Democratizing Efficient Video Production for All
This project aim to reproduce Sora (Open AI T2V model), we wish the open source community contribute to this project.
[ICLR 2024] Controlling Vision-Language Models for Universal Image Restoration. 5th place in the NTIRE 2024 Restore Any Image Model in the Wild Challenge.
整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。
Fish-like autosuggestions for zsh
The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.
Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
Accessible large language models via k-bit quantization for PyTorch.
关于Transformer模型的最简洁pytorch实现，包含详细注释
Pythonic AI generation of images and videos
Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.
This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.