---
license: odc-by
datasets:
  - HuggingFaceFW/fineweb-edu
language:
  - en
library_name: transformers
pipeline_tag: text-generation
tags:
  - nanodex
  - tiny-lm
  - pretrained-from-scratch
---

# hyperdex-test

A **1,000,224-parameter** decoder-only language model pre-trained
**from scratch** on [fineweb-edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu),
using the [NanoDex Trainer](https://huggingface.co/spaces/hugging-science/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 | 1,000,224 |
| Hidden size | 96 |
| Layers | 5 |
| Attention heads | 6 (KV: 2) |
| FFN size | 472 |
| Context length | 512 |
| Vocab | 2,048 (custom BPE trained on fineweb-edu) |

## Training

| | |
|---|---|
| Tokens seen | 299,892,736 |
| Steps | 2,288 |
| Tokens / step | 131,072 |
| Optimizer | AdamW(0.9, 0.95) wd=0.1 clip=1.0 |
| LR schedule | warmup 2% + cosine to 10% (peak 3e-03) |
| Final loss | 3.5412 (ppl 34.5) |
| Wall time | 14.7 min |
| Trained by | [@GGUFGuy](https://huggingface.co/GGUFGuy) |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("GGUFGuy/hyperdex-test")
model = AutoModelForCausalLM.from_pretrained("GGUFGuy/hyperdex-test")

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.