#计算机科学#59 篇深度学习论文的实现,并带有详细注释。包括 transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 强化学习 (ppo, dqn), capsnet, distillation, ... 🧠
#自然语言处理#Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
#自然语言处理#Haystack 是一个开源 NLP 框架,利用预训练的 Transformer 模型。 帮组开发者能快速实现一个生产级的语义搜索、问答、摘要和文档排名的NLP应用
#大语言模型#🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
#大语言模型#Machine Learning Engineering Open Book
#大语言模型#RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN a...
Ongoing research training transformer models at scale
#搜索#PaddleNLP 2.0是飞桨生态的文本领域核心库,具备易用的文本领域API,多场景的应用示例、和高性能分布式训练三大特点,旨在提升开发者文本领域的开发效率,并提供基于飞桨2.0核心框架的NLP任务最佳实践。
#搜索#All-in-one 一站式 embedding 数据库,语义搜索、LLM 编排和语言模型workflows
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
#计算机科学#A PyTorch-based Speech Toolkit
An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
#大语言模型#Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, DeepSeek, Mixtral, Gemma, Phi, MiniCPM, Qwen-VL, MiniCPM-V, etc.) on Intel XPU (e.g., local PC with iGPU and NPU, discrete...
#计算机科学#Implementation of RLHF (Reinforcement Learning with Human Feedback) on top of the PaLM architecture. Basically ChatGPT but with PaLM
#自然语言处理#BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
An Easy-to-use, Scalable and High-performance RLHF Framework based on Ray (PPO & GRPO & REINFORCE++ & vLLM & Ray & Dynamic Sampling & Async Agentic RL)
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
#自然语言处理#Leveraging BERT and c-TF-IDF to create easily interpretable topics.