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Optimum
[https://huggingface.co/docs/optimum/index] - - public:mzimmerm
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.
A Step-by-Step Guide to Model Evaluation in Python | by Shreya Singh | Medium
[https://medium.com/@jscvcds/a-step-by-step-guide-to-model-evaluation-in-python-3a72dee92560] - - public:mzimmerm
(1) Most cost effective GPU for local LLMs? : LocalLLaMA
[https://www.reddit.com/r/LocalLLaMA/comments/12vxxze/most_cost_effective_gpu_for_local_llms/] - - public:mzimmerm
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.
Optimizing LLMs for Speed and Memory
7 steps to master large language models (LLMs) | Data Science Dojo
Up to date List of LLM Models
[https://docs.google.com/spreadsheets/d/1kT4or6b0Fedd-W_jMwYpb63e1ZR3aePczz3zlbJW-Y4/edit#gid=741531996] - - public:mzimmerm
Replit — How to train your own Large Language Models
[https://blog.replit.com/llm-training] - - public:mzimmerm
Hi level only talk about training for a language
How to train a new language model from scratch using Transformers and Tokenizers
[https://huggingface.co/blog/how-to-train] - - public:mzimmerm
Describes how to train a new language (desperanto) model.