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The Evolution of Large Language Models: From Transformers to DeepSeek-R1

The field of artificial intelligence has seen remarkable progress in recent years, particularly in the domain of large language models (LLMs). This article explores the journey from the foundational Transformer architecture to the cutting-edge DeepSeek-R1 model, highlighting key developments and breakthroughs along the way.

Transformer Architecture: The Foundation of Modern LLMs

The Transformer architecture, introduced in 2017, revolutionized natural language processing. Its attention mechanism allowed for more efficient processing of sequential data, paving the way for larger and more capable language models1.

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Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

	#!/bin/sh

	set -eux -o pipefail

	export GIT_USER=hughdbrown
	export GIT_REPO_NAME_USER=AIE5 # What the user is going to call the repo
	export GIT_REPO_NAME_AI_MAKER=AIE5 # What the repo is already called by AI_MAKER

	mkdir -p ~/workspace/${GIT_USER}/maven.com/${GIT_REPO_NAME_USER}
	cd ~/workspace/${GIT_USER}/maven.com/${GIT_REPO_NAME_USER}

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer