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SLM-Archive/Overaddicted-500K

sourceHugging Faceodc-byupdated 6d agoView on Hugging Face

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<div align="center"> <h3 align="center">Model archived by:</h3>

<table align="center"> <tr> <td align="center" valign="middle" width="140"> <a href="https://huggingface.co/DedeProGames" target="_blank"> <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/685ea8ff7b4139b6845ce395/Im--QSnbrnAhHPPhpX8L0.png" width="80" height="80" alt="DedeProGames" style="display:block; margin:0 auto; border-radius:50%; object-fit:cover;" /> <br /> <b>DedeProGames</b> </a> </td> </tr> </table> </div>

Overaddicted-500K

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	1,499,987,968
Steps	11,444
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	3.5919 (ppl 36.3)
Wall time	33.9 min
Trained by	@DedeProGames

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("DedeProGames/Overaddicted-500K")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/Overaddicted-500K")

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.