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Build, personalize and control your own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
The main features of stochasticai/xturing are: LLM Training and Optimization, Model Training, Model Training and Fine-tuning, Natural Language Processing.
Projects with overlapping indexed features include: microsoft/deepspeed — DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of… huggingface/transformers — Transformers is a comprehensive library for machine learning that provides a unified interface for training,… chenking2020/findthechatgpter — ChatGPT爆火，开启了通往AGI的关键一步，本项目旨在汇总那些ChatGPT的开源平替们，包括文本大模型、多模态大模型等，为大家提供一些便利. alexrozanski/llamachat — Chat with your favourite LLaMA models in a native macOS app. artidoro/qlora — This project is a quantized fine-tuning framework for large language models. It implements a low-rank adaptation… aethercortex/llama-x — Open Academic Research on Improving LLaMA to SOTA LLM.
DeepSpeed is a distributed deep learning optimization library and framework designed for the training and inference of massive AI models. It serves as a model parallelism orchestrator and a toolkit for scaling large language models across multiple GPUs and compute nodes. The project distinguishes itself through 3D parallelism orchestration, which combines data, pipeline, and tensor parallelism. It utilizes ZeRO-based memory partitioning to eliminate redundant storage and employs CPU-offload memory management to move weights and optimizer states to system RAM. Additionally, it provides special
Transformers is a comprehensive library for machine learning that provides a unified interface for training, fine-tuning, and deploying transformer-based models. It supports a wide range of tasks, including text classification, language modeling, question answering, and sequence-to-sequence translation, while offering specialized architectures for both text and vision processing. The framework includes tools for managing the entire model lifecycle, from data preprocessing and tokenization to distributed training and inference. The library features extensive support for model optimization and
A modular RL library to fine-tune language models to human preferences
Open Academic Research on Improving LLaMA to SOTA LLM