Instructions to use MagistrTheOne/murzik-15b-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagistrTheOne/murzik-15b-init with Transformers:

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

pipe = pipeline("text-generation", model="MagistrTheOne/murzik-15b-init", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MagistrTheOne/murzik-15b-init", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MagistrTheOne/murzik-15b-init with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MagistrTheOne/murzik-15b-init"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagistrTheOne/murzik-15b-init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MagistrTheOne/murzik-15b-init

SGLang

How to use MagistrTheOne/murzik-15b-init 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 "MagistrTheOne/murzik-15b-init" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagistrTheOne/murzik-15b-init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "MagistrTheOne/murzik-15b-init" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagistrTheOne/murzik-15b-init",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MagistrTheOne/murzik-15b-init with Docker Model Runner:
```
docker model run hf.co/MagistrTheOne/murzik-15b-init
```

MURZIK-15B-INIT

NULLXES MURZIK — custom causal language model (dense ~13B).
Canonical Hugging Face repo for the Murzik-15B dense line.


Organization	NULLXES
Contact	ceo@nullxes.com
Architecture	`MurzikForCausalLM` (custom, not a fork)
Total params	~13B
Precision	bf16
HF repo	`MagistrTheOne/murzik-15b-init` (this page)

Current checkpoint


Stage	Pre-training (first run)
Steps	1500
Data	Wikipedia (en/ru/de/es/fr/zh/uk) + Murzik identity corpus
Seq length	2048
Tokens seen	~~49M (~~0.37 epoch)
Chat / instructions	Not yet — SFT is the next stage

Weights in this repo are updated in place (random init → PT → later SFT).
The repo name stays murzik-15b-init; only the README and files change per stage.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MagistrTheOne/murzik-15b-init"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    torch_dtype="auto",
    device_map="auto",
)

Chat template (after SFT)

Template name in LlamaFactory: murzik

<|murzik|><|system|>
{system}<|end|>
<|user|>
{user}<|end|>
<|assistant|>
{assistant}<|end|>

Roadmap

Stage	Status
Random init	done
Pre-training	done (first run, 1500 steps)
SFT (identity + Aya)	next
MoE 32B	separate line

License

Proprietary — NULLXES. Weights are published for research and integration testing.
Commercial use requires written permission: ceo@nullxes.com.

Citation

@misc{murzik15b_init2026,
  title        = {NULLXES MURZIK-15B},
  author       = {NULLXES},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/MagistrTheOne/murzik-15b-init}},
  contact      = {ceo@nullxes.com}
}

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Safetensors

Model size

13B params

Tensor type

BF16