Stars
A scalable generative AI framework built for researchers and developers working on Large Language Models, Multimodal, and Speech AI (Automatic Speech Recognition and Text-to-Speech)
[ICLR 2025] SOTA discrete acoustic codec models with 40/75 tokens per second for audio language modeling
A Neural Audio Codec (NAC) for Universal Audio
This is the homepage of a new book entitled "Mathematical Foundations of Reinforcement Learning."
CPED: A Large-Scale Chinese Personalized and Emotional Dialogue Dataset for Conversational AI | 中文个性情感对话数据集
Collection of resources on the applications of Large Language Models (LLMs) in Audio AI.
The official repo of Pai-Megatron-Patch for LLM & VLM large scale training developed by Alibaba Cloud.
This repository follows papers and reports on discrete speech representation learning and speech tokenization methods for speech language modeling.
Vector (and Scalar) Quantization, in Pytorch
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models
Reading list of Instruction-tuning. A trend starts from Natrural-Instruction (ACL 2022), FLAN (ICLR 2022) and T0 (ICLR 2022).
This project support a WEB UI with Vicuna13B (using llama-cpp-python, chatbot-ui)
LLaMA Server combines the power of LLaMA C++ with the beauty of Chatbot UI.
A paper & resource list of large language models, including course, paper, demo, figures
Large-scale, Informative, and Diverse Multi-round Chat Data (and Models)
An implementation of 1D, 2D, and 3D positional encoding in Pytorch and TensorFlow
GTS Engine: A powerful NLU Training System。GTS引擎（GTS-Engine）是一款开箱即用且性能强大的自然语言理解引擎，聚焦于小样本任务，能够仅用小样本就 88B9 自动化生产NLP模型。
Official Code Repository for La-MAML: Look-Ahead Meta-Learning for Continual Learning"
Fork of the GEM project (https://github.com/facebookresearch/GradientEpisodicMemory) including Meta-Experience Replay (MER) methods from the ICLR 2019 paper (https://openreview.net/pdf?id=B1gTShAct7)
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
Reproduce Results for ICCV2019 "Symmetric Cross Entropy for Robust Learning with Noisy Labels" https://arxiv.org/abs/1908.06112
Codes for the paper: "Continual Learning for Text Classification with Information Disentanglement Based Regularization"
A curated list of resources for Learning with Noisy Labels
For the code release of our arXiv paper "Revisiting Few-sample BERT Fine-tuning" (https://arxiv.org/abs/2006.05987).
A novel method to tune language models. Codes and datasets for paper ``GPT understands, too''.
An Open Source Machine Learning Framework for Everyone