Instructions to use MaliosDark/Nexus-Erebus-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaliosDark/Nexus-Erebus-50M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaliosDark/Nexus-Erebus-50M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaliosDark/Nexus-Erebus-50M")
model = AutoModelForCausalLM.from_pretrained("MaliosDark/Nexus-Erebus-50M")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaliosDark/Nexus-Erebus-50M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaliosDark/Nexus-Erebus-50M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaliosDark/Nexus-Erebus-50M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaliosDark/Nexus-Erebus-50M

SGLang

How to use MaliosDark/Nexus-Erebus-50M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "MaliosDark/Nexus-Erebus-50M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaliosDark/Nexus-Erebus-50M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "MaliosDark/Nexus-Erebus-50M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaliosDark/Nexus-Erebus-50M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MaliosDark/Nexus-Erebus-50M with Docker Model Runner:
```
docker model run hf.co/MaliosDark/Nexus-Erebus-50M
```

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.

Downloads last month: 686

Safetensors

Model size

54.1M params

Tensor type

BF16

MaliosDark
/

Nexus-Erebus-50M