Awesome papers on Language-Model-as-a-Service (LMaaS)
txsun1997/lmaas-papers 的主要功能包括：Pre-trained Language Models。
txsun1997/lmaas-papers 的开源替代品包括： codertimo/bert-pytorch. huggingface/course — This project is an educational course and learning curriculum for implementing and fine-tuning transformer models… openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next… morvanzhou/tutorials — This repository is a comprehensive collection of instructional guides and practical examples for Python development,… eleutherai/gpt-neox — gpt-neox is a distributed training system and framework for building large-scale autoregressive language models. It… cluebenchmark/electra — 中文 预训练 ELECTRA 模型: 基于对抗学习 pretrain Chinese Model.
This project is an educational course and learning curriculum for implementing and fine-tuning transformer models using the Hugging Face ecosystem. It serves as a structured guide and technical walkthrough for processing multimodal data, adapting pre-trained neural networks, and deploying models. The material includes a guide for managing, versioning, and distributing model weights and datasets through a centralized asset hub. It also provides a practical tutorial on adapting models to specific datasets using parameter-efficient methods and an implementation guide for solving natural language
This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad
This project is a transformer-based language model and autoregressive text generator designed to predict the next token in a sequence to produce human-like prose and synthetic text. It functions as a large language model that utilizes a transformer architecture to learn linguistic patterns from large datasets for unsupervised multitask learning. The repository provides a distribution of pre-trained weights, enabling natural language processing tasks without requiring additional training. This allows the model to perform zero-shot task generalization by applying learned patterns to new tasks.
