A hands-on record of training a language model from scratch: every decision across data, tokenizer, architecture, training, and evaluation.
Open-source projects have pushed the cost of training an LLM from scratch absurdly low: MiniMind runs the full PreTrain-to-RL pipeline for about $0.4 (2 hours on a single RTX 3090), while at the other end OLMo 3 and LLM360 K2 publish everything — data, code, and stage-by-stage checkpoints of 65B models. This series walks the whole project spectrum from $0.4 to 65B in 11 articles, and flags where the map is biased.
MiniMind is an open-source project for training LLMs from scratch: a 64M Dense model and a 198M-A64M MoE model that run the entire chain — Pretrain → SFT → LoRA → DPO → PPO/GRPO/CISPO → Agentic RL — in ~2 hours on a single RTX 3090 at roughly 3 RMB (~$0.40). Every core algorithm is implemented natively in PyTorch with no high-level wrappers.
A tour of Karpathy's three teaching repos: nanoGPT (2022, ~300 lines each to reproduce GPT-2 124M), llm.c (pure C/CUDA training), and nanochat (2025-10, one speedrun.sh from tokenizer to WebUI). $100 and 4 hours on 8×H100 buys a chatty model; GPT-2-grade capability is now down to about 2 hours and $48.
A close look at three open-source projects training LLMs from scratch in the Chinese community — baby-llama2-chinese (218M, 63.4B tokens), ChatLM-mini-Chinese (0.2B T5, 10.23M dialogues), and Steel-LLM (1.12B, 1T tokens, 8 months) — comparing corpus strategy, tokenizer decisions, and community ecosystem. Honest evaluation included: baby-llama2 scored a bottom-ranking 21 in MiniMind's side-by-side test; ChatLM has the strongest knowledge (62) but weak coding.
YuLan-Mini is a 2.4B open-source model from Renmin University's AI Box lab, trained on 48 A800 GPUs with only 1.08T tokens — scoring 37.8 on MATH-500 and 64.0 on HumanEval, beating Qwen2/Qwen2.5 peer models trained on 7T–18T tokens at math and code. What's public isn't a slogan: per-phase data mixes, pre-annealing optimizer states, and even W&B logs of the ablation studies.
Training from scratch only makes sense in three cases: you want to learn how training works, you have 10B+ clean tokens no open model has seen, or you need a fully transparent training process for research. Otherwise fine-tuning or RAG is almost always cheaper. This post collapses the series' main routes into one cost ladder and a decision tree.
Switzerland's Apertus (8B/70B, 15T tokens, 1,000+ languages) filters opt-outs and personal data before training to satisfy the EU AI Act; Japan's LLM-jp consortium shipped LLM-jp-4 (12T tokens) in April 2026, claiming wins over GPT-4o and Qwen3-8B on standard benchmarks. Both prove that from-scratch training outside the English sphere is a data-governance problem, not a technical one — plus a note on RWKV-7 as the non-Transformer alternative.
OpenELM uses layer-wise scaling to shift parameters toward layers near the output; with 1.08B parameters and 1.5T tokens it beats OLMo 1.2B (+2.36% on the LLM360 average) despite OLMo training on 3T tokens. MiniCPM trains multimodal small models from scratch with a three-stage unfreezing recipe (Resampler first, vision encoder next, everything unfrozen last); MiniCPM-V 4.5 reaches sub-30B SOTA on VideoMME with only 8B parameters, and 4-bit quantization squeezes fp16's 16–17GB memory footprint down to about 5GB for phones.
LitGPT (Lightning AI, ~13,600 stars, Apache 2.0) rewrites 20+ mainstream LLMs — Llama 3, Qwen2.5, Phi 4 — from scratch as single-file, no-abstraction implementations, with a full pretrain / finetune / evaluate / serve CLI. TinyLlama (1.1B parameters, 3T tokens) was trained on this codebase. This post breaks down how it differs from MiniMind, how to actually use it, and where it stops.
The hands-on installment of the series: rent an RTX 3090 on RunPod (Secure Cloud $0.5/hr, Community Cloud $0.22/hr), follow the MiniMind README through pretrain (~1.21h) + SFT (~1.10h), spend roughly $0.55–1.50 USD total, and chat with your own 64M model trained from scratch in the terminal.
"Open-source LLM" is a spectrum: weights-only (Llama), weights plus data (most fully open projects), or data order, intermediate checkpoints, and training logs all released (LLM360 K2, OLMo 3's model flow). This piece unpacks the two projects that pushed transparency furthest: OLMo 3 shipped the first fully open 32B thinking model in November 2025, and K2 is the first 65B-class model whose checkpoints even include optimizer states.