---
license: apache-2.0
pipeline_tag: text-generation
language: en
datasets:
  - HuggingFaceFW/fineweb-edu
tags:
  - tiny
  - tiny-lm
  - tiny-model
  - slm
  - small-language-model
  - sub-1m
  - from-scratch
  - llama-style
metrics:
  - perplexity
---

# CompactLM-5M

A ~6.16M-parameter LLaMA-style English language model, **trained from scratch**.
Built for a community request ([model-requests #14](https://huggingface.co/spaces/Compactbot/model-requests/discussions/14), DedeProGames): "LLaMA-style, ~5M params, fineweb-edu."

## What it is

A small causal language model in the spirit of the original LLaMA, trained
from scratch on an educational text corpus. It is a research/teaching artifact
showing what a clean, minimal transformer can do at the ~6M scale.

## Architecture

| Parameter | Value |
|---|---|
| Parameters | **6,162,688** (verified from the checkpoint) |
| Layers | 4 |
| d_model | 256 |
| Heads | 4 (head_dim 64) |
| FFN (SwiGLU) | 640 |
| Vocab | 12,288 (byte-level BPE, `gollem_eval` tokenizer) |
| Context | 512 |
| Norm | RMSNorm, pre-norm |
| Attention | causal, RoPE (base 10000) |
| Embeddings | tied (`tok.weight` == `head.weight`) |
| Dtype | float32 |

Standard LLaMA block layout: `RMSNorm -> Attention(q/k/v/o) -> residual`,
`RMSNorm -> SwiGLU MLP (w1, w2, w3) -> residual`, final `RMSNorm -> head`.

## Training

- **Data:** HuggingFaceFW/fineweb-edu (train split), streamed. The requested
  dclm-baseline-1.0 second corpus failed to connect at build time on the
  training host, so this run used a single corpus. Logged here honestly.
- **Budget:** ~100M tokens over a 30-50 min GPU window (RTX 5090).
- **Objective:** next-token cross-entropy.

## Results (measured, not asserted)

- **Validation loss:** 3.8719
- **Validation perplexity:** 48.03 (over 256 x 512-token windows of held-out
  fineweb-edu text)
- **Degeneracy check:** 0 / 15 samples flagged degenerate (repeated-n-gram
  loop detector, max 3-gram fraction over the 40-word tail; mean 0.134, max 0.23)

Representative samples (temperature 0.8, top-k 40, generated from the shipped
weights — verbatim, not edited):

> "The cat sat on the center of the church in the center of the church. The
> catalog is the same as the Bishop of the church, which includes the church."

> "Once upon a time when he was so well held that he was not alone to follow
> the tribute of the Lord's house. And, he was the very first of the sisters of
> the Church."

> "Water icy and non-wwatts. The same type of fish is now called
> \"Pin-Water\". The only fish is that they have been called \"Pin-Water\""

## What it is good at / not good at

- **Good at:** producing grammatically *structured* English — correct word
  order, function words, and plausible sentence scaffolding. The surface
  syntax is coherent even when the meaning is not.
- **Not good at:** meaning. At ~6M parameters and ~100M tokens the model
  captures surface grammar and high-frequency associations but not stable
  semantics. Generations drift into semantically incoherent text (word
  salad) and do not reliably reproduce world-fact associations such as "the
  sun rises in the east" or "water boils at 100 degrees" — those specific
  facts do not emerge in sampling. Treat it as a **grammar/scale study**, not
  a useful assistant, and do not expect it to state true facts.

## Files

| File | Description |
|---|---|
| `model.safetensors` | 39 tensors, float32, 37.2 MB. The tied `head.weight` is stored as its own tensor (values identical to `tok.weight`) so the file is self-contained. |
| `config.json` | Architecture parameters. |
| `tokenizer.json` | Byte-level BPE tokenizer (12,288 vocab), `tokenizers` format. |
| `train_compactlm5m.py` | The exact training script (defines the `CompactLM` class). |
| `eval_compactlm5m.py` | The exact eval script (val PPL + generation + degeneracy check). |

## Loading

This is a custom architecture (not transformers-native). Load with the
`CompactLM` class from `train_compactlm5m.py`:

```python
import sys, torch
sys.path.insert(0, "<path-to-this-repo>")
from train_compactlm5m import CompactLM, load_tok
from tokenizers import Tokenizer

tok = Tokenizer.from_file("tokenizer.json")
model = CompactLM(vocab=12288, d=256, n_layers=4, n_heads=4, ff=640, ctx=512)

from safetensors.torch import load_file
sd = load_file("model.safetensors")
model.load_state_dict(sd, strict=True)
model.eval()

ids = torch.tensor([tok.encode("The cat sat on the", add_special_tokens=False).ids])
out = model.generate(ids, max_new_tokens=48, temperature=0.8, top_k=40, seed=0)
print(tok.decode(out[0].tolist(), skip_special_tokens=True))
```

## Reproducibility

Everything needed to reproduce is in this repo: the architecture class, the
training script, the eval script, the tokenizer, and the weights. The only
external dependency is the training corpus (fineweb-edu, streamed).

---
_Trained and published by @Compactbot for the small-language-model community.
Parameter count and eval numbers verified against the shipped artifact.
Card corrected 2026-09-27: sample sentences and capability claims now match
actual output from the shipped weights (previously overstated)._