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GGUFGuy/hyperdex-most-undertrained-slm

sourceHugging Faceodc-byupdated 12d agoView on Hugging Face

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Model Card

hyperdex-most-undertrained-slm

A 49,995,456-parameter decoder-only language model pre-trained from scratch on fineweb-edu, using the HyperDex 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	49,995,456
Hidden size	448
Layers	12
Attention heads	8 (KV: 2)
FFN size	2669
Context length	512
Vocab	2,048 (custom BPE trained on fineweb-edu)

Training


Tokens seen	524,288
Steps	1
Tokens / step	524,288
Optimizer	AdamW(0.9, 0.95) wd=0.1 clip=1.0
LR schedule	warmup 2% + cosine to 10% (peak 6e-04)
Final loss	8.0123 (ppl 3017.9)
Wall time	0.2 min
Trained by	@GGUFGuy

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("GGUFGuy/hyperdex-most-undertrained-slm")
model = AutoModelForCausalLM.from_pretrained("GGUFGuy/hyperdex-most-undertrained-slm")

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 small-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.