18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
-
Updated
Jun 28, 2024 - Jupyter Notebook
18 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
Unify Efficient Fine-Tuning of 100+ LLMs
👑 Easy-to-use and powerful NLP and LLM library with 🤗 Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including 🗂Text Classification, 🔍 Neural Search, ❓ Question Answering, ℹ️ Information Extraction, 📄 Document Intelligence, 💌 Sentiment Analysis etc.
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
CVPR 2024 论文和开源项目合集
Ongoing research training transformer models at scale
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
🔍 LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
GPT-powered chat for documentation, chat with your documents
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
This repository contains demos I made with the Transformers library by HuggingFace.
A PyTorch-based Speech Toolkit
Accelerate local LLM inference and finetuning (LLaMA, Mistral, ChatGLM, Qwen, Baichuan, Mixtral, Gemma, Phi, etc.) on Intel CPU and GPU (e.g., local PC with iGPU, discrete GPU such as Arc, Flex and Max); seamlessly integrate with llama.cpp, Ollama, HuggingFace, LangChain, LlamaIndex, DeepSpeed, vLLM, FastChat, Axolotl, etc.
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
An implementation of model parallel autoregressive transformers on GPUs, based on the Megatron and DeepSpeed libraries
An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
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, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
Add a description, image, and links to the transformers topic page so that developers can more easily learn about it.
To associate your repository with the transformers topic, visit your repo's landing page and select "manage topics."