---
license: apache-2.0
pipeline_tag: text-generation
language: en
tags:
  - tiny
  - tiny-lm
  - tiny-model
  - slm
  - small-language-model
  - sub-1m
  - from-scratch
  - character-level
  - char-gpt
metrics:
  - perplexity
---

# Testr-100K

A **100,936-parameter** character-level GPT trained from scratch. Fulfils model request [#17](https://huggingface.co/spaces/Compactbot/model-requests/discussions/17) from @GGUFGuy.

> **Correction (2026-09-30):** the weights originally published here were trained on the **wrong data** (191 MB of web text). The request asked for a model trained **only** on the TinyStories *validation* set, as a deliberate memorization test. This commit swaps in the correct model (trained on the 19.2 MB TinyStories validation set) and fixes the parameter count (106,568 → 100,936; the earlier figure double-counted the tied head). The eval numbers below are from the correct model.

## What it is

A minimal nanoGPT-style causal transformer operating at the **character level** (128-char vocabulary). The point of this specific model is a **memorization experiment**: train a very small model on a held-out validation set and measure how much it actually memorizes versus generalizing.

## Architecture

| Parameter | Value |
|-----------|-------|
| Layers | 4 |
| Embedding dim | 44 |
| Attention heads | 2 |
| Context length | 512 chars |
| FFN | GELU, mult 2.667 |
| Positional encoding | RoPE (θ=100000) |
| Normalization | LayerNorm (pre-norm) |
| Vocab | 128 chars + 1 UNK |
| Tied embeddings | Yes (head = embedding) |
| **Total params (unique)** | **100,936** |

(52 tensors in the file; the head is tied to the embedding, so counting it once gives 100,936. The earlier "106,568" double-counted the tied head.)

## Training

- **Data:** 19.2 MB **TinyStories validation set** (character-level, uint8) — *deliberately* the set the request asked to train on
- **Steps:** 8,000
- **Batch size:** 32 × seq 512
- **Optimizer:** Muon (0.02) + AdamW (1e-4) for biases/norms
- **Schedule:** Cosine decay with warmup
- **Hardware:** RTX 5090 (32 GB)
- **dtype:** float32

## The experiment (honest result)

The question: does a 100K-parameter model trained *only* on a 19 MB validation set actually memorize it, or does it just learn character statistics?

Measured perplexity (char-level, 512-char blocks, 20 random windows, seed 1337):

| Set | Size | Perplexity |
|-----|------|-----------|
| **Memorized** (the 19.2 MB set it trained on) | 19,212,307 B | **3.1540** |
| **Never seen** (191 MB web text) | 191,283,131 B | **3.2203** |

**Ratio (never-seen / memorized) = 1.02×.** At 100K parameters the model **barely memorizes** the set it was trained on — its perplexity on the memorized data is only 2% lower than on data it never saw. It has essentially learned general English character statistics, not the specific stories. That is the honest answer to the request: a 100K-param char model is far too small to memorize 19 MB of text.

## Sample outputs (greedy, temp=0)

> `Once upon a time` → `, there was a little girl named Lily. She liked to play with her mom and said, "I want to the bird was so happy and said`

> `The sun was` → ` so happy and said, "I want to the boy named Lily. They were so happy and said, "I want to the ball and said, "I want to`

### Sample outputs (temp=0.8)

> `A little` → ` girl named Spot around of friends.\nOnce upon a time, there was a praye was so like in room that it was angry, the sweet`

The first sentence or two is often grammatical; after that the model locks into a phrase and repeats it. Expected at this scale and vocabulary size.

## What it is NOT

- Not a subword/token-level model (it cannot "read" word-level benchmarks like BLiMP or ARC)
- Not a coherent paragraph generator
- Not evidence of memorization at this scale (see ratio above)

## Reproducing

Standard nanoGPT-style causal transformer with Muon optimizer. The checkpoint is saved as safetensors with tied embeddings (the file stores `tok.weight` and an identical `head.weight`; they are the same tensor).

To generate: load `model.safetensors` into a GPT class matching `config.json`, map characters to token IDs via `vocab.json`, and sample.

## Files

| File | Description |
|------|-------------|
| `model.safetensors` | Model weights (431 KB, 52 tensors, tied head) |
| `config.json` | Architecture config |
| `vocab.json` | Character → token ID mapping (128 chars + UNK) |