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
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.