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.
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
Total runs
0
24-hour runs
9
3-day runs
680
7-day runs
680
30-day runs
More Information About NanoDex-Test-500K-200M huggingface.co Model
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