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[https://www.rchilli.com/blog/resume-parsing-101/] - - public:Annie
AI, CV parsing, HR, recruitment, resume parser, Technology - 6 | id:1490828 -

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.

[https://huggingface.co/docs/optimum/index] - - public:mzimmerm
ai, doc, huggingface, llm, model, optimum, repo, small, transformer - 9 | id:1489894 -

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.

[https://medium.com/@rafaelmanzanom/ditching-cuda-for-amd-rocm-for-more-accessible-llm-inference-ryzen-apus-edition-92c3649f8f7d] - - public:mzimmerm
ai, amd, apu, compile, gfx902, install, pytorch, rocm - 8 | id:1489810 -

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.

[https://www.reddit.com/r/LocalLLaMA/comments/12vxxze/most_cost_effective_gpu_for_local_llms/] - - public:mzimmerm
ai, doc, llm, model, optimize, perform - 6 | id:1489804 -

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.

[https://oscar-project.org/] - - public:mzimmerm
ai, dataset, opensource - 3 | id:1489792 -

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.

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