Apple study exposes deep cracks in LLMs’ “reasoning” capabilities - Ars Technica
[https://arstechnica.com/ai/2024/10/llms-cant-perform-genuine-logical-reasoning-apple-researchers-suggest/] - - public:mzimmerm
Train and use my model
As method pto run ai using GPU in a vm on linux
Install this version. Find when in tumbleweed
Document about LM Studio
Optimum is an extension of Transformers that provides a set of performance optimization tools to train and run models on targeted hardware with maximum efficiency. It is also the repository of small, mini, tiny models.
Excellent document about BERT transformers / models and their parameters: - L=number of layers. - H=size of the hidden layer = number of vectors for each word in the sentence. - A = Number of self-attention heads - Total parameters.
Repository of all Bert models, including small. Start using this model for testing.
LM Studio can be installed on Linux with APU or GPU (looks like it needs the AI CPU though??) and run LLM. Install on Laptop and test if it works.
My account on SageMaker studio. The give out 4 hours of GPU a day!
8000G is the APU series for AI
Doing what a transformer is doing, by hand
Kaggle is like huggingface. They can run notebooks, and give GPU power to notebooks
Mini course of statistical foundations of ML
My account on Stability AI - it is just a link to huggingface
Comparison of efficiency of all LLM models on hugging face
Various methods to run LLM models locally hugging face is only one of them.
AMD seems to sell these accelerators, which are like video cards.
Train LLM on AMD APU. In this scenario, we’ll use an APU because most laptops with a Ryzen CPU include an iGPU; specifically, this post should work with iGPUs based on the “GCN 5.0” architecture, or “Vega” for friends. We’ll use an AMD Ryzen 2200G in this post, an entry-level processor equipped with 4C/4T and an integrated GPU.
GGML quantized models. They would let you leverage CPU and system RAM, instead of having to rely on a GPU’s. This could save you a fortune, especially if go for some used AMD Epyc platforms. This could be more viable for the larger models, especially the 30B/65B parameters models which would still press or exceed the VRAM on the P40.