Lists (2)
Stars
A curated list of recent diffusion models for video generation, editing, restoration, understanding, etc.
✨✨Latest Papers and Benchmarks in Reasoning with Foundation Models
Unofficial PyTorch/🤗Transformers(Gemma/Llama3) implementation of Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
The Truth Is In There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Unified Efficient Fine-Tuning of 100+ LLMs (ACL 2024)
👨💻 An awesome and curated list of best code-LLM for research.
Benchmarking large language models' complex reasoning ability with chain-of-thought prompting
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
Awesome-LLM: a curated list of Large Language Model
General technology for enabling AI capabilities w/ LLMs and MLLMs
LLMs interview notes and answers:该仓库主要记录大模型（LLMs）算法工程师相关的面试题和参考答案
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。
本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）
The official GitHub page for the survey paper "A Survey of Large Language Models".
Python for《Deep Learning》，该书为《深度学习》(花书) 数学推导、原理剖析与源码级别代码实现
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
[ICLR 2022] Official implementation of cosformer-attention in cosFormer: Rethinking Softmax in Attention
Awesome LLM compression research papers and tools.
An annotated implementation of the Transformer paper.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
A PyTorch implementation of the Transformer model in "Attention is All You Need".
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
A plug-and-play library for parameter-efficient-tuning (Delta Tuning)
中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
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,…
《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。
We unified the interfaces of instruction-tuning data (e.g., CoT data), multiple LLMs and parameter-efficient methods (e.g., lora, p-tuning) together for easy use. We welcome open-source enthusiasts…