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Run an LLM Locally with LM Studio - KDnuggets
Document about LM Studio
Optimum
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 Transformers – How Do They Work? | Exxact Blog
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.
Generative pre-trained transformer - Wikipedia
(1) Most cost effective GPU for local LLMs? : LocalLLaMA
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 Mastering Large Language Models (LLMs) - KDnuggets
A Step-by-Step Guide to Training Your Own Large Language Models (LLMs). | by Sanjay Singh | GoPenAI
7 steps to master large language models (LLMs) | Data Science Dojo
Up to date List of LLM Models
Large Language Models for Domain-Specific Language Generation: How to Train Your Dragon | by Andreas Mülder | Medium
training a model like Llama with 2.7 billion parameters outperformed a larger model like Vicuna with 13 billion parameters. Especially when considering resource consumption, this might be a good alternative to using a 7B Foundation model instead of a full-blown ChatGPT. The best price-to-performance base model for our use case turned out to be Mistral 7b. The model is compact enough to fit into an affordable GPU with 24GB VRAM and outperforms the other models with 7B parameters.
Replit — How to train your own Large Language Models
Hi level only talk about training for a language
How to train a new language model from scratch using Transformers and Tokenizers
Describes how to train a new language (desperanto) model.