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license: mit
language:
- en
library_name: transformers
tags:
- text-generation
- tiny-lm
- tinystories
- educational
- built-with-llama
- small-model
pipeline_tag: text-generation
datasets:
- roneneldan/TinyStories
---
# TinyBuddy-500K
> ⚠️ **Educational / experimental model.** TinyBuddy-500K is a from-scratch tiny Llama-style language model (~547K parameters) trained on a synthetic slice of TinyStories-style text.
> It is **not** a useful assistant — it is a working demonstration of training extremely small models from scratch. See the [Limitations](#limitations) section.
## Model description
TinyBuddy-500K is a very small decoder-only Transformer language model trained on synthetic children's stories in the style of [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories). The architecture follows the LLaMA design (RMSNorm, Grouped Query Attention, SiLU MLP, tied embeddings).
| Hyperparameter | Value |
|-------------------------|--------------------------------|
| Parameters | **547,296** (~547K) |
| Layers | 2 |
| Attention heads | 4 |
| Key-Value heads (GQA) | 2 |
| Hidden size | 96 |
| MLP intermediate size | 384 |
| Context length | 512 |
| Vocab size | 2,048 (BPE trained from scratch) |
| Norm | RMSNorm |
| Activation | SiLU |
| Position embeddings | Learned absolute |
| Weight tying | Yes (tied embeddings) |
| Precision | float32 |
## Training details
- **Data**: Synthetic TinyStories-style corpus (~128K tokens)
- **Tokenizer**: Custom byte-level BPE with 2048 vocabulary
- **Optimizer**: AdamW
- **Steps**: ~300 steps on CPU
- **Hardware**: Single CPU core
- **Final loss**: ~0.17
## Usage
This model uses **custom modeling code**, so you must pass `trust_remote_code=True`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "Eeppa/TinyBuddy-500K"
tokenizer = AutoTokenizer.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True)
model.eval()
prompt = "Once upon a time, there was a little girl named Lily."
input_ids = tokenizer.encode(prompt, return_tensors="pt")
out = model.generate(input_ids, max_new_tokens=60, temperature=0.8, top_k=50)
print(tokenizer.decode(out[0], skip_special_tokens=True))
```
## Limitations
This model is extremely small and was trained for a very short time on limited data.
**What works**:
- Basic English patterns and short sentence structure
- Simple story-like generation
**What's broken**:
- Very limited coherence (usually breaks after 1–2 sentences)
- High repetition
- Poor long-range consistency
- No real reasoning or factual knowledge
This model exists purely for educational purposes to explore the lower limits of language model size.
## License
MIT
## Citation
```bibtex
@misc{tinybuddy500k,
title = {TinyBuddy-500K: An educational ~500K parameter Llama-style model trained on TinyStories},
year = {2026},
note = {Educational demonstration of extremely small language models.}
}
```
**Built with Llama.** |