---
license: apache-2.0
pipeline_tag: text-generation
language: en
tags:
  - tiny
  - tiny-lm
  - tiny-model
  - slm
  - small-language-model
  - from-scratch
  - tinystories
  - bpe
  - gpt
datasets:
  - roneneldan/TinyStories
metrics:
  - perplexity
  - accuracy
---

# TinyStories-24m

A **24.59M-parameter** BPE language model trained **from scratch** on
[roneneldan/TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories),
producing coherent short stories with proper dialogue, names, punctuation and
narrative flow.

## What it is

- **Architecture:** decoder-only GPT, weight-tied embeddings, RMSNorm, fused
  qkv multi-head causal attention (SDPA), GELU FFN.
- **Shape:** D=384, L=12 layers, H=8 heads, FFN=1536, SEQ=512, vocab=8192 (BPE).
- **Params:** 24,585,600 (verified against the safetensors header).
- **Data:** roneneldan/TinyStories — 447.8M train tokens, 2M held-out val.
- **Training:** 1 epoch ≈ 13,600 steps, AdamW, cosine LR 6e-4 + 500 warmup,
  bf16 autocast, on a single RTX 5090.

## Quality

- **Val perplexity:** 8.76 (2.1618 nats/token, training-time fixed-window score).
  Full-split repro (100 random 512-token windows over the 4.5M-token val split):
  **12.39** (2.5172 nats/token). The gap is methodology (fixed window vs random
  windows), not a card error.
- **Generation:** coherent. Sampled 9/9 seeded generations (3 seeds × 3 prompts)
  produce proper dialogue, character names (Ben, Lily, Mom, Tom, Sarah, Max),
  punctuation and narrative flow. This model is a story generator for its
  training domain — it is **not** a general-purpose assistant and will not
  answer questions it was not trained on.

### Benchmark results (zero-shot loglikelihood, 400 examples per task)

| Task | Accuracy | Chance | Notes |
|------|----------|--------|-------|
| ARC-Easy | 13.3% | 25% | below chance |
| ARC-Challenge | 12.5% | 25% | below chance |
| HellaSwag | 25.0% | 25% | at chance |
| SciQ | 25.0% | 25% | at chance |
| PIQA | 50.0% | 50% | at chance |

All results are at or below chance — expected for a 24M model trained exclusively
on simple children's stories. The model has learned the distribution of story
text but has no general reasoning, commonsense, or science knowledge.

## Honest caveats

- **Divergence:** the full 13,600-step run diverged to NaN at step 9,350 (LR 6e-4
  is too hot for a 24M model). The **best** checkpoint (step 6,000, val 2.1618)
  is what is published here — it is clean and coherent. The divergence is late,
  so a clean early checkpoint is the right artifact; always sample the best
  checkpoint, not the final one.
- **Domain-bound:** trained only on TinyStories. Out-of-domain text (code,
  questions, general conversation) is out of scope.

## Usage

Not a `transformers` model — load with the bundled `modeling.py`:

```python
import sys, torch
sys.path.insert(0, "path/to/this/repo")
from modeling import TinyStoriesGPT
from tokenizers import Tokenizer

m = TinyStoriesGPT.from_pretrained("path/to/this/repo", device="cpu")
tok = Tokenizer.from_file("path/to/this/repo/tokenizer.json")

ids = tok.encode("Ben was playing in the park.", add_special_tokens=False).ids
x = torch.tensor([ids], dtype=torch.long)
with torch.no_grad():
    for _ in range(80):
        logits = m(x[:, -512:])[:, -1]
        nxt = torch.multinomial(torch.softmax(logits / 0.8, -1), 1).item()
        ids.append(nxt)
        x = torch.tensor([ids[-512:]], dtype=torch.long)
print(tok.decode(ids, skip_special_tokens=True))
```

## Files

| file | bytes | what |
|------|-------|------|
| `model.safetensors` | 98,349,056 | 75 tensors, float32 |
| `config.json` | — | architecture + training metadata |
| `modeling.py` | — | the `TinyStoriesGPT` class (load with `from_pretrained`) |
| `tokenizer.json` | 560,804 | BPE-8k tokenizer (HF `tokenizers` format) |