---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- small-language-model
- slm
- from-scratch
- tiny
- nexus-erebus
- llama
- arithmetic
---

![Nexus-Erebus-50M](./nexus50m.png)

# Nexus-Erebus-50M

**Nexus-Erebus-50M** is a compact ~54M-parameter language model trained from scratch by **Ideoa Labs**,
combining strong commonsense reasoning with genuine integer arithmetic ability at tiny scale.

## Model details

| | |
|---|---|
| Parameters | ~54.1M |
| Architecture | Llama-style decoder |
| Hidden size | 512 |
| Layers | 9 |
| Attention heads | 8 |
| Vocab size | 32,000 (custom digit-aware tokenizer) |
| Context length | 1,024 |
| Precision | bfloat16 |

The tokenizer keeps digits atomic rather than merging them into BPE units, which preserves the
positional structure that integer arithmetic depends on.

## Benchmarks

Measured with `lm-eval-harness`, 0-shot, `acc_norm`, on the **full test sets** (no subsampling).
ArithMark-2 is scored on its full 2500 items with the public evaluation script.

| Task | Items | Nexus-Erebus-50M |
|---|---:|---:|
| ARC-easy | 2,376 | 44.40 |
| ARC-challenge | 1,172 | 22.70 |
| HellaSwag | 10,042 | 27.05 |
| PIQA | 1,838 | 58.27 |
| **ArithMark-2** | 2,500 | **52.48** |
| **Average** | | **40.98** |

### Against the sub-100M field

Published Open SLM Leaderboard values, same five tasks, same full-set protocol.

| Model | Params | Average |
|---|---:|---:|
| **Nexus-Erebus-50M** | **54M** | **40.98** |
| Atom 2.7M | 3M | 40.43 |
| Supra-1.5-50M-base-exp | 52M | 39.00 |
| Isabel-50M | 54M | 38.74 |
| Supra-50M-Base | 52M | 38.45 |
| Archaea-74M-V1.1 | 74M | 37.96 |

It leads the sub-100M class on ARC-easy and PIQA, and its ArithMark-2 score of 52.48 is the second
highest in that class.

## Training

Trained from scratch with a custom digit-aware 32k tokenizer, then refined on a curated mix of
educational, science, commonsense and reading-comprehension data, plus a large synthetic integer
arithmetic set covering addition, subtraction, multiplication, exact division, mixed multi-operator
expressions and parenthesised expressions. No benchmark test items were used at any stage.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("MaliosDark/Nexus-Erebus-50M")
model = AutoModelForCausalLM.from_pretrained("MaliosDark/Nexus-Erebus-50M")

prompt = "16 + 4 * 3 ="
print(tok.decode(model.generate(**tok(prompt, return_tensors="pt"), max_new_tokens=6)[0]))
```

## Example outputs

Real, unedited outputs from this checkpoint.

| Prompt | Output |
|---|---|
| `Question: What force pulls objects toward the Earth?` | gravity. |
| `Question: What gas do humans need to breathe to survive?` | oxygen. |
| `Question: What do we call the process by which plants make food?` | photosynthesis. |

## License

Apache-2.0.

Built by Ideoa Labs.