#GPU memory

The tool estimates required GPU memory and token throughput for large language models (LLMs) on multiple GPUs and CPUs. It provides a detailed memory usage analysis for both training and inference, supporting quantization tools such as GGML, bitsandbytes, and frameworks like vLLM, llama.cpp, and HF. Key functionalities include vRAM requirements, token rate calculation, and finetuning duration approximation. The tool assists in assessing quantization suitability, maximum context capacity, and batch-size capability for GPUs, offering valuable insights into GPU memory optimization.

Explore a novel method for scaling large language models with BitNet's 1-bit transformers, built using the Llama(2) architecture in PyTorch. The Huggingface Transformers implementation helps to minimize GPU memory consumption while ensuring optimal performance. Learn about the environment setup, Wikitext-103 model training, and GPU memory usage across varying precision levels, offering a resourceful guide for developers looking for memory-efficient model training.

This Pytorch-based baseline for object re-identification achieves notable performance with a Rank@1 of 88.24% and mAP of 70.68% using softmax loss. It requires minimal GPU memory, operating efficiently with Nvidia's fp16 technology on just 2GB. The project offers a range of customizable options and techniques including part-based convolutional methods and various loss functions. An 8-minute introductory tutorial assists new users, making this solution adaptable and accessible for various levels of expertise.