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 / README.md

MagistrTheOne

Murzik-15B: PT checkpoint (1500 steps, wiki+identity) → murzik-15b-init

83d9edb verified 1 day ago

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	---
	language:
	- en
	- ru
	- de
	- es
	- fr
	- zh
	- uk
	license: other
	tags:
	- murzik
	- nullxes
	- causal-lm
	- custom_code
	- multilingual
	library_name: transformers
	pipeline_tag: text-generation
	---

	# MURZIK-15B-INIT

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

	\| \| \|
	\|---\|---\|
	\| Organization \| [NULLXES](https://nullxes.com) \|
	\| Contact \| [ceo@nullxes.com](mailto: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

	```python
	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

	```bibtex
	@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}
	}
	```