NanoDex-1M-300M

NanoDex-1M-300M

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.

Training

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("huggingworld/NanoDex-1M-300M")
model = AutoModelForCausalLM.from_pretrained("huggingworld/NanoDex-1M-300M")

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.