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With this article I would like to help you to run locally under Ubuntu 22.04 Llama 2 in the 70B version on a NVIDIA A6000. Of course this is not easy because the classic Llama 2 70B model needs a GPU memory of about 280GB in the Float32 bit version. But if you quantize the… Read more: Chatbot Llama 2 70B – run locally in a jupyter notebook

A lot of data and matching labels are very important for training neural networks. Therefore, label tools are an essential part in the preparation of data. If the labeling works easily and quickly, the more time is saved when preparing the data and later when training the neural networks. Since the beginning of 2023 there… Read more: AnyLabeling – installation guide and experience report

I always wanted to try a Llama model of META and with the release of Llama 2 it was then so far that I went on the search how I can set up this on my computer locally. There are several guides but the best framework I could find was oobabooga which is also opensource.… Read more: oobabooga – text-generation-webui mit Llama 2 – local Installation

After training the first 31 models for anti-personnel mine detection, my data set has grown significantly. The goal was to reduce the false positives. This was because there were always leaves detected that were similar to a PFM-1 anti-personnel mine and were falsely detected as such. In order for the YOLOv5 network to learn that… Read more: YOLOv5 – Optimization of training data for PFM-1 antipersonnel mine detection

This paper is about training a neural network based on YOLOv5. This YOLO network should be able to recognize PFM-1 anti-personnel mines in order to support the automated detection of these mines. Since this is my first time working with YOLOv5 and synthetic data, I am curious about the results that a validation dataset will… Read more: YOLOv5 – Training a neural network for PFM-1 antipersonnel mine detection