HUSTHY
Follow
Stars
A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations
An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & vLLM & RFT & Dynamic Sampling & Async Agent RL)
Example models using DeepSpeed
LMDeploy is a toolkit for compressing, deploying, and serving LLMs.
Code and models for ICML 2024 paper, NExT-GPT: Any-to-Any Multimodal Large Language Model
很多镜像都在国外。比如 gcr 。国内下载很慢，需要加速。致力于提供连接全世界的稳定可靠安全的容器镜像服务。
Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
📚LeetCUDA: Modern CUDA Learn Notes with PyTorch for Beginners🐑, 200+ CUDA/Tensor Cores Kernels, HGEMM, FA-2 MMA etc.🔥
本项目旨在收集开源的表格智能任务数据集（比如表格问答、表格-文本生成等），将原始数据整理为指令微调格式的数据并微调LLM，进而增强LLM对于表格数据的理解，最终构建出专门面向表格智能任务的大型语言模型。
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Summarize existing representative LLMs text datasets.
The most comprehensive database of Chinese poetry 🧶最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。
This repository contains datasets and baselines for benchmarking Chinese text recognition.
Generate text images for training deep learning ocr model
The official repo of Qwen-VL (通义千问-VL) chat & pretrained large vision language model proposed by Alibaba Cloud.
Personal Project: MPP-Qwen14B & MPP-Qwen-Next(Multimodal Pipeline Parallel based on Qwen-LM). Support [video/image/multi-image] {sft/conversations}. Don't let the poverty limit your imagination! Tr…
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. Documentation:
TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and support state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorR…
A high-throughput and memory-efficient inference and serving engine for LLMs
Official Implementation of EAGLE-1 (ICML'24), EAGLE-2 (EMNLP'24), and EAGLE-3.
[ICML 2024] Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
Fast and low-memory attention layer written in CUDA