DedeProGames / NanoDex-Test-500K-200M

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Model's Last Updated: September 12 2026
text-generation

Introduction of NanoDex-Test-500K-200M

Model Details of NanoDex-Test-500K-200M

NanoDex-Test-500K-200M

A 492,192-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 492,192
Hidden size 96
Layers 3
Attention heads 6 (KV: 2)
FFN size 256
Context length 512
Vocab 2,048 (custom BPE trained on fineweb-edu)
Training
Tokens seen 199,884,800
Steps 1,525
Tokens / step 131,072
Optimizer AdamW(0.9, 0.95) wd=0.1 clip=1.0
LR schedule warmup 2% + cosine to 10% (peak 4e-03)
Final loss 3.8176 (ppl 45.5)
Wall time 5.2 min
Trained by @DedeProGames
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("DedeProGames/NanoDex-Test-500K-200M")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/NanoDex-Test-500K-200M")

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.

Runs of DedeProGames NanoDex-Test-500K-200M on huggingface.co

680
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3-day runs
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