README.md

metadata

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, 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:

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)