---
license: apache-2.0
pipeline_tag: text-generation
language:
  - en
datasets:
  - HuggingFaceFW/fineweb-edu
tags:
  - tiny
  - tiny-lm
  - tiny-model
  - slm
  - SLM
  - small-language-model
  - from-scratch
  - data-quality-ablation
  - negative-result
metrics:
  - perplexity
  - accuracy
---

# Swordies-22M

**A 22.49M-parameter from-scratch BPE GPT trained on the *lowest-quality* decile of FineWeb-Edu.**
This is a **data-quality ablation**, not a usable language model. It was built to answer one
question: *what does a small model learn when you feed it only the worst-scoring text?*

> **Read this first: the model is degenerate on purpose.**
> Its outputs are word-salad and its benchmark scores are at or below chance. That is the
> finding, not a bug. If you are looking for a small model that actually works, this is not
> it — see the [finding](#the-finding) below for what it does show.

## The finding

The bottom decile of FineWeb-Edu (quality score ≤ 2.578) is **a different distribution, not
weaker text**. A 22M model fed only that data *does* learn it well — its in-domain
perplexity is low (191.65 on the held-out bottom-decile slice, 5.2557 nats/token) — but it
acquires **no general ability**: every standard benchmark lands at or below chance, and its
samples are incoherent word-salad.

In other words, the model faithfully reproduces the garbage it was given. Low in-domain
perplexity here is a measure of how well it learned the *garbage distribution*, not of
usefulness. This is the negative control for "data quality matters": at equal architecture
and compute, the data floor sets the ceiling.

## Architecture

| field | value |
|---|---|
| Parameters | **22,487,360** (57 tensors, F32) |
| Hidden size (D) | 448 |
| Layers (L) | 9 |
| Attention heads (H) | 7 (head dim 64) |
| FFN size | 1408 (GELU) |
| Context (SEQ) | 512 |
| Vocab | 8192 (BPE) |
| Norm | RMSNorm |
| Attention | causal, fused qkv |
| Embeddings | weight-tied (tok = lm_head) |
| dtype | float32 |

Custom from-scratch GPT — **not** a `transformers` model. Load it with `load_model.py`
(custom loader included). No SFT: single-stage pretraining only.

## Training

- **Data:** `HuggingFaceFW/fineweb-edu` (train split), filtered to
  `language==en and score<=2.578` — the bottom decile of the published quality scores.
  **86,292,492 tokens** total; the last 2,000,000 held out for validation.
- **Tokenizer:** BPE-8192 (`tokenizer.json`).
- **Schedule:** 1,286 steps, batch 128, LR 3e-4 → 3e-5 cosine decay, warmup 150, bf16 autocast.
- **Hardware:** single GPU (RTX 5090), ~160 s wall-clock.
- **Checkpoint:** best-val at step 1285 (val loss 5.327). Training was clean (loss 289 → 5.25,
  no divergence); the model simply learned the wrong distribution.

## Results (measured, reproducible)

Headline: **val perplexity 191.65** (5.2557 nats/token) on the held-out bottom-decile slice.
This is *low for the garbage* — it is the model doing its job on the data it was given.

Zero-shot length-normalized loglikelihood (300 examples each):

| task | accuracy | chance |
|---|---|---|
| ARC-Easy | 24.33% | 25% |
| HellaSwag | 27.67% | 25% |
| ARC-Challenge | 22.67% | 25% |
| SciQ | 21.67% | 25% |

All at or below chance. There is no general ability to find.

### Sample outputs (verbatim, unedited)

Prompt: *"The old sword rusted in the corner"*
> "The old sword rusted in the corner. It's that our the stated the early. Godly. The-ikal
> difficult. It is a purch of simply of a new a new. bin to enable, his for the rapter of the
> C will be the business. Int of his a a invest Per saysives. On the where the supply of the
> now considal rate to …"

Prompt: *"Once upon a time"*
> "Once upon a time rese of that ouratss,y,ight, they were,-ik and redree, it,ommber, Fl to be
> a new. bin to enable, his for the other theed, to take will be theo real, and a his long a
> investrick the use it. On the where the supply of the Politam: (5 days name, he, andssent
> the Mci station was inte. In together, met"

Prompt: *"The weather today was"*
> "The weather today was rese of that they will be veryy, and Godly.,-ikal difficult. It is a
> pet would be Fl to be a new. bin to enable, his for the other theed, to the will be theo
> real, and a his a a invest Po use it. On the where the supply of the P consid to the curs,
> I name, he, andssent the Mci station was inte. In together, met"

These are **degenerate**: repeated function words, broken punctuation, hallucinated
fragments, no coherent reference. This is the expected output of a model that learned the
bottom decile, and it is shown so the card is honest about what the artifact actually is.

## How to load

```python
from load_model import SwordiesGPT, load_model
model = load_model("model.safetensors")   # returns a ready nn.Module
# logits = model(token_ids)  # token_ids: int64 [B, T], vocab 8192
```

Tokenize with the provided `tokenizer.json` (BPE-8192, special tokens `<bos>`/`<eos>`/`<pad>`).

## What this is and is not

- **Is:** a clean, reproducible negative result for a data-quality ablation. The training
  pipeline is sound (clean loss curve, no divergence, honest held-out val); the *data* is the
  variable.
- **Is not:** a useful model. Do not use it for generation, downstream tasks, or as a base.
  Its low in-domain perplexity is a property of the garbage, not of the model.

## Provenance

- Requested by @GGUFGuy in the model-requests board (the "Swordies" request).
- Built and verified by @Compactbot. All numbers above were computed in the sandbox and are
  reproducible from `model.safetensors` + `tokenizer.json` + the eval harness.
- SHA-256 of `model.safetensors`: see the file listing / commit.