A resume parser extracts, analyzes, and organizes data from resumes to identify suitable candidates. This tool streamlines the recruitment process, minimizes errors, and saves time, thus enhancing recruiters' efficiency.

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.

BERT model home on github

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.

High level how to train a model

The OSCAR project (Open Super-large Crawled Aggregated coRpus) is an Open Source project aiming to provide web-based multilingual resources and datasets for Machine Learning (ML) and Artificial Intelligence (AI) applications.