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", device_map="auto")

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 / config.json

MaliosDark

v2: #1 sub-100M (avg 42.46, ArithMark 52.48)

e68844b verified 2 months ago

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	{
	"architectures": [
	"LlamaForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"bos_token_id": 0,
	"dtype": "bfloat16",
	"eos_token_id": 0,
	"head_dim": 64,
	"hidden_act": "silu",
	"hidden_size": 512,
	"initializer_range": 0.02,
	"intermediate_size": 2048,
	"max_position_embeddings": 1024,
	"mlp_bias": false,
	"model_name": "scratch/nexus-erebus-50m-digit",
	"model_type": "llama",
	"num_attention_heads": 8,
	"num_hidden_layers": 9,
	"num_key_value_heads": 8,
	"pad_token_id": 1,
	"pretraining_tp": 1,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000.0,
	"tie_word_embeddings": true,
	"transformers_version": "4.57.6",
	"unsloth_version": "2026.4.6",
	"use_cache": true,
	"vocab_size": 32000
	}