---
license: mit
language: en
tags:
  - gpt
  - from-scratch
  - tinystories
  - small-language-model
  - pytorch
datasets:
  - roneneldan/TinyStories
pipeline_tag: text-generation
---

# TinyStories SLM

A ~12.4M-parameter decoder-only transformer, **written from scratch** — no
`transformers`, no `tokenizers`, no `lightning`. Tokenizer, model, training
loop, and sampler are all hand-written PyTorch. Full source, training logs,
and design writeup: **[github.com/sur950/tinystories-slm](https://github.com/sur950/tinystories-slm)**.

It trains on [TinyStories](https://arxiv.org/abs/2305.07759) — Ronen Eldan
and Yuanzhi Li's dataset of GPT-4-generated children's stories — and asks how
small a language model can be and still write coherent English.

## Not a `transformers` model

This architecture (RMSNorm, tied embeddings, hand-rolled KV cache) isn't a
`transformers`-compatible class, so `AutoModel.from_pretrained(...)` won't
work here. To actually run it:

```bash
git clone https://github.com/sur950/tinystories-slm.git
cd tinystories-slm
./setup.sh
```

Download `stage2.pt`/`stage3.pt`/`tokenizer.json` from this repo into
`./checkpoints` and `./data` respectively, then:

```bash
python chat.py --stage 2   # base model: plain text continuation, no chat format
python chat.py --stage 3   # instruction-tuned: greetings, "tell me a story about X",
                            # and a redirect for anything outside TinyStories' domain
```

## Two checkpoints

| file | what it is |
|---|---|
| `stage2.pt` | Base language model. Trained in two passes over the ~560M-token corpus (Chinchilla-sized for this model). No chat format — continues whatever text precedes it, same as the original TinyStories models. |
| `stage3.pt` | `stage2.pt` fine-tuned on a small (~4k example) instruction set — see `instruct_raw.jsonl` in this repo for the exact data. Responds to greetings, takes "tell me a story about X" requests, and gives a fixed redirect for anything outside the TinyStories domain instead of hallucinating an answer. |

`stage3.pt` is the one to use for anything chat-like — **not** the
lower-val-loss checkpoint from the same run (not included here). Val loss on
this fine-tune is a single number blended across three very unevenly sized
categories, so "lowest aggregate val" mostly tracks the largest category
(story requests) and says little about whether the smaller, demo-critical
categories (greetings, redirects) are reliable. Tested directly: the final
checkpoint answered "Hi" correctly on-template ~4/5 times; the lowest-val
checkpoint from partway through the same run only ~2/5. Full writeup in the
GitHub repo's `DEVELOPMENT.md`.

## Architecture

```
vocab 4096   d_model 384   layers 6   heads 6   context 512   tied embeddings
12,391,296 parameters total
```

Byte-level BPE tokenizer, vocab size 4096, fit on this same corpus — not the
standard GPT-2 vocabulary, so token IDs from other tokenizers won't map
correctly onto this model.

## Limitations

- English-only, children's-story register — no world knowledge, no code, no
  reasoning beyond what a simple short story requires.
- `stage3.pt`'s instruction-following is narrow by design: greetings, story
  requests within a small set of trained keywords, and off-domain redirects.
  It is not a general chatbot.
- 512-token context window.

## Citation

```bibtex
@misc{eldan2023tinystories,
  title         = {TinyStories: How Small Can Language Models Be and Still
                   Speak Coherent English?},
  author        = {Ronen Eldan and Yuanzhi Li},
  year          = {2023},
  eprint        = {2305.07759},
  archivePrefix = {arXiv},
}
```

Built by [Suresh](https://github.com/sur950). [MIT licensed](https://github.com/sur950/tinystories-slm/blob/main/LICENSE).