
Gemma2 vs Qwen2 vs Mistral Nemo vs...
Testing logical fallacy detection
Recently we have seen several new LLMs were released. Exciting times. Let’s test and see how they perform when detecting logical fallacies.
Testing logical fallacy detection
Recently we have seen several new LLMs were released. Exciting times. Let’s test and see how they perform when detecting logical fallacies.
Not so many to choose from but still....
When I started experimenting with LLMs the UIs for them were in active development and now some of them are really good.
Requires some experimenting but
Still there are some common approaches how to write good prompts so LLM would not get confused trying to understand what you wand from it.
Frequenly needed bits of python code
Sometimes need this but can’t find rightaway. So keeping them all here.
Labelling and training needs some glueing
When I trained object detector AI some time ago - LabelImg was a very helpful tool, but the export from Label Studio to COCO format wasn’t accepted by MMDetection framework..
8 llama3 (Meta+) and 5 phi3 (Microsoft) LLM versions
Testing how models with different number of parameters and quantization are behaving.
Ollama LLM model files take a lot of space
After installing ollama better to reconfigure ollama to store them in new place right away. So after we pull a new model, it doesn’t get downloaded to the old location.
Let's test the LLMs' speed on GPU vs CPU
Comparing prediction speed of several versions of LLMs: llama3 (Meta/Facebook), phi3 (Microsoft), gemma (Google), mistral(open source) on CPU and GPU.
Let's test logical fallacy detection quality of different LLMs
Here I am comparing several LLM versions: Llama3 (Meta), Phi3 (Microsoft), Gemma (Google), Mistral Nemo(Mistral AI) and Qwen(Alibaba).
Quite some time ago I trained object detector AI
On one cold winter day in July … that is in Australia … I felt urgent need to train AI model for detecting uncapped concrete reinforcement bars…