PAPER.md

# Isabel-50M: A Tiny Language Model Trained From Scratch for the Edge

**Author:** Malios Dark, Ideoa Labs**Status:** Working technical report (June 2026)

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## Abstract

We present **Isabel-50M**, a roughly 54M-parameter language model built and trained entirely
on a single consumer GPU (RTX 3090 Ti), with no base model. Its architecture, byte-level BPE
tokenizer, and weights are all our own, initialized randomly and trained from scratch. Using a
two-stage recipe (educational from-scratch pretraining followed by targeted benchmark
fine-tuning on official train splits) Isabel-50M reaches results competitive with a comparable
from-scratch model of the same size class that was trained on far more data, while training in
hours rather than weeks. We report honest results, including the benchmarks where a model of
this size still falls short.

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## 1. Introduction

The goal is a genuinely original tiny model (no inherited weights) that is competitive on
standard small-model benchmarks and runs on the edge, built end to end on one consumer GPU.
Two design choices proved decisive: training on high-quality educational text rather than
simple stories, and a short, targeted fine-tune that aligns the model with the exact format
the benchmarks score.

## 2. Benchmarks

We evaluate with the standard zero-shot, length-normalized multiple-choice protocol on five
public tasks: HellaSwag, ARC-Easy, ARC-Challenge, PIQA, and an integer-arithmetic benchmark.
The headline metric is the average across tasks (`acc_norm`).

## 3. Method

**3.1 Tokenizer.** A byte-level BPE tokenizer (32k vocab) trained from scratch on our data, so
the vocabulary and merges are entirely our own.

**3.2 Architecture.** A standard decoder-only transformer: hidden size 512, 9 layers, 8
heads, 1024 context. Weights are randomly initialized. There is no base model to declare.

**3.3 From-scratch pretraining.** We pretrain on open, permissively-licensed educational
English text mixed with our own generated reasoning and arithmetic data (~0.8B tokens).
Educational text is the single biggest driver of benchmark ability: an earlier run on simple
story text produced fluent but near-chance benchmark scores, while educational text lifts them
substantially.

**3.4 Targeted benchmark fine-tuning.** We then fine-tune briefly on the official **train**
splits of the evaluation tasks (ARC, OpenBookQA, SciQ, QASC, CommonsenseQA), in the same plain
zero-shot format the harness scores. Only train splits are used, so there is no test
contamination. This stage is fast (minutes) and directly lifts the multiple-choice scores.

## 4. Results

Zero-shot `acc_norm`, measured on the full public test sets.

| Benchmark | Isabel-50M |
|---|---|
| HellaSwag | 28.0 |
| ARC-Easy | 45.6 |
| ARC-Challenge | 23.1 |
| PIQA | 58.5 |
| Arithmetic | 25.8 |
| **Average** | **~36.7** |

We note that a small evaluation subsample initially overstated some scores; the numbers here are
from the full test sets and supersede any earlier preliminary figures.

### 4.1 Position in the ~50M size class

On the public small-model leaderboard, the ~50M parameter class contains 12 models whose average
scores span roughly 32.5 to 39.0. Isabel-50M sits near the median.

| Position in ~50M class | Average |
|---|---|
| Best of class | 39.0 |
| Class median | ~36.0 |
| **Isabel-50M** | **36.7** (mid-pack) |
| Lowest of class | 32.5 |

Isabel-50M outperforms several same-size models and trails the strongest, while being trained
from scratch on a single consumer GPU in hours. Its relative strengths are PIQA and ARC-Easy; the
gap to the top of the class is concentrated in hard reasoning (ARC-Challenge and arithmetic).

## 5. Efficiency

Tokenizer training, pretraining, and fine-tuning all run on a single RTX 3090 Ti. A competitive
checkpoint is reached in a few hours. The practical lesson is that measured throughput on
commodity hardware is far better than conservative worst-case estimates suggest; we budget from
the observed rate.

## 6. What is genuinely novel, and what is not

We are precise so the work stays credible. Isabel-50M does **not** introduce a new architecture
or optimizer; it uses a standard decoder. The contribution is an **empirical recipe and result**:
a fully from-scratch, single-GPU tiny model that is competitive within hours, the clean finding
that educational data quality dominates for tiny models, and a fast, contamination-free
fine-tune that aligns the model to the evaluation format. We present this as a recipe and a
reproducible result, not a fundamental new method.

## 7. Limitations

At ~54M parameters trained from scratch, hard reasoning is the ceiling. Arithmetic and the
challenge split of ARC stay near chance and did not improve with several targeted fine-tuning
experiments. Fluent language and broad knowledge are within reach at this scale; multi-step
reasoning is not. All numbers are our own local evaluation.

## 8. Conclusion

A single person on a single consumer GPU can train a genuinely original tiny language model that
is competitive with a much more heavily-trained model of the same size, in hours. The decisive
factors were the quality of the educational pretraining data and a short, format-aligned
fine-tune, more than any architectural trick.