NanoDex-1M

A 1,062,272-parameter decoder-only language model pre-trained from scratch on fineweb-edu, using the NanoDex Trainer Space.

Architecture

A standard LlamaForCausalLM decoder-only transformer — SiLU MLP, RMSNorm, rotary position embeddings, grouped-query attention, tied embeddings, no biases — scaled down in width and depth to fit the parameter budget.

Parameters	1,062,272
Hidden size	128
Layers	5
Attention heads	8 (KV: 4)
FFN size	288
Context length	512
Vocab	2,048 (custom BPE trained on fineweb-edu)

Training

Tokens seen	999,817,216
Steps	3,814
Tokens / step	262,144
Optimizer	AdamW(0.9, 0.95) wd=0.1 clip=1.0
LR schedule	warmup 2% + cosine to 10% (peak 3e-03)
Final loss	3.3092 (ppl 27.4)
Wall time	43.8 min
Trained by	@DedeProGames

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("DedeProGames/NanoDex-1M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/NanoDex-1M")

ids = tok("The mitochondria is", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=60, do_sample=True,
                                temperature=0.8, top_k=50)[0]))

Caveats

This is a nano-scale research artifact. At this parameter count and token budget the model learns word shapes, common collocations and a little syntax — it is not a useful assistant and its output is not factual. It exists to make "pre-train a transformer from scratch" something you can actually watch happen.