#multi-GPU

The mistral-finetune project provides an efficient platform for fine-tuning Mistral models by leveraging the LoRA training framework. This method focuses on memory conservation by locking most weights and adjusting only a small fraction. Tailored for multi-GPU environments, it also accommodates single GPU use for smaller models, like the 7B. Recently, it includes support for models such as Mistral Large v2 and Mistral Nemo, demanding more memory for larger tasks but enhancing finetuning capabilities. It serves as a straightforward entry point for finetuning Mistral models, emphasizing specific data formatting and installation instructions, essential for advanced training across various systems.

Explore a simplified and versatile codebase that enhances image training across diverse datasets. Features like Multi-GPU support, aspect bucketing, and optional EMA enable optimization of various image sizes and aspect ratios. Technologies such as Flux, PixArt Sigma, and Stable Diffusion ensure compatibility with modern GPUs, and DeepSpeed integration facilitates training on hardware with lower VRAM. Access streamlined installations, comprehensive tutorials, and community support for effective image processing.

Explore the RETURNN framework, an extensible Theano/TensorFlow-based platform for training recurrent neural networks efficiently across multiple GPUs. The framework prioritizes simplicity, flexibility, and efficiency, streamlining setup and troubleshooting. RETURNN accommodates a range of experiments and neural network models while offering high-speed training and decoding essential for research and industry. Core features include mini-batch training, sequence chunking for RNNs, custom CUDA kernels for LSTM, and multi-device load balancing. Illustrative real-world cases and benchmarks demonstrate RETURNN's superior capacity and performance, particularly in speech recognition and encoder-decoder modeling.

Discover a seamless PyTorch training experience with an innovative library that simplifies multi-device and distributed environments. Easily integrate minimal changes to enable smooth transitions between CPUs, GPUs, and TPUs with mixed precision. A user-friendly CLI aids in configuring and deploying scripts while maintaining control over training loops. Offering exceptional flexibility in scaling machine learning models, it supports frameworks like DeepSpeed and PyTorch FSDP, making it suitable for developers focusing on simplicity and adaptability.