Instructions to use MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT with Transformers:

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

pipe = pipeline("text-generation", model="MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT"
# 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/NULLXES-SHINRA-4B-INSTRUCT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT

SGLang

How to use MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT 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/NULLXES-SHINRA-4B-INSTRUCT" \
    --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/NULLXES-SHINRA-4B-INSTRUCT",
		"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/NULLXES-SHINRA-4B-INSTRUCT" \
        --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/NULLXES-SHINRA-4B-INSTRUCT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT with Docker Model Runner:
```
docker model run hf.co/MagistrTheOne/NULLXES-SHINRA-4B-INSTRUCT
```

NULLXES SHINRA-4B-INSTRUCT

NULLXES SHINRA-4B-INSTRUCT is the Language Intelligence Layer of the NULLXES system.

| Layer | Role |

| SHINRA | Language Intelligence |

SHINRA is responsible for multilingual understanding, coding intelligence, instruction following, structured outputs, and agent preparation. This checkpoint is the instruction-tuned (and optionally DPO-aligned) 4B-class dense decoder.

Architecture

Proprietary ShinraForCausalLM (not a Llama / Mistral / Qwen / GPT-NeoX wrapper).


Type	Decoder-only Transformer
Parameters	3.93B (tied embeddings)
Hidden size	3072
Layers	32
Attention	GQA 24 query / 8 KV heads, head dim 128
MLP	SwiGLU, intermediate 9216
Norm	RMSNorm, pre-norm + QK-norm
Position	RoPE, θ = 1e6, YaRN-ready
Context	8192 train / 32768 native window
Vocab	131072 SentencePiece Unigram + byte fallback
Precision	BF16
Attention kernels	PyTorch SDPA Flash / FlashAttention-2

Block:

RMSNorm → GQA+RoPE → residual → RMSNorm → SwiGLU → residual then final RMSNorm and tied LM head.

Load:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "NULLXES/SHINRA-4B-INSTRUCT",
    torch_dtype="bfloat16",
    trust_remote_code=True,
    device_map="auto",
)
tok = AutoTokenizer.from_pretrained("NULLXES/SHINRA-4B-INSTRUCT", trust_remote_code=True)
messages = [
    {"role": "system", "content": "You are SHINRA, the NULLXES language intelligence layer."},
    {"role": "user", "content": "Explain grouped-query attention."},
]
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=256)

Special tokens: <|bos|> <|eot|> <|system|> <|user|> <|assistant|> <|reasoning|> <|code|> <|language|> <|tool_call|> <|tool_response|> <|document|> <|end_of_text|>. Generation stop is <|eot|>. Document stop is <|end_of_text|>.

Training data

Three stages.

Pretrain → NULLXES SHINRA-4B-BASE
SHINRA_PRETRAIN_V1: 40% FineWeb-Edu, 20% code (python-edu + licensed Stack), 15% math/science (OpenWebMath, ProofPile-2, peS2o, full arXiv bodies), 10% books (PG19 + Gutenberg; no TinyStories), 10% Wikipedia ru/de/fr, 5% NULLXES engineering (robotics/CUDA/docs slice). Language quotas en 75 / ru 15 / other 10. Documents pass ftfy, Gopher/FineWeb quality, script/language ID, toxicity heuristics, code AST/minified filters, and MinHash-LSH (Jaccard 0.80).

SFT → NULLXES SHINRA-4B-INSTRUCT
30% conversation (Tulu + SmolTalk), 25% code, 20% math, 15% tools/JSON (Hermes function-calling), 10% general (OpenHermes). Packed to 8192 with loss on assistant tokens only.

DPO
40% UltraFeedback, 30% Python code preference, 20% instruction/tool mix, 10% format (Orca), β = 0.10.

Exact mix weights live in configs/data_mix.yaml. Tokenizer trained on a ≥10B-character representative sample of the same mix.

Intended use

Research and internal NULLXES product integration (language layer behind RAIDEN / AION agents).
Instruction following, coding assistance, multilingual generation, structured JSON/tool drafts.
Further domain adaptation by NULLXES.

Out of scope without additional alignment and policy layers: autonomous high-stakes decisions, medical/legal advice, open internet agents with unconstrained tools.

Limitations

3.93B dense capacity: weaker than 70B-class models on multi-hop reasoning and rare languages.
Pretrain budget 200B tokens is overtrained vs Chinchilla-80B but far below frontier token counts.
Toxicity and safety filters are heuristic plus optional classifiers; residual harmful content is possible.
Long context above 8192 uses RoPE extrapolation (YaRN). Always re-run needle-in-haystack after extension.
Custom architecture requires trust_remote_code=True on Hugging Face loaders.

Evaluation

Run:

python -m evaluation.perplexity --model $CKPT --data-dir data/packed/pretrain
python -m evaluation.harness --model $CKPT
python -m evaluation.needle --model $CKPT

Suite: ARC-Challenge, HellaSwag, WinoGrande, TruthfulQA, MMLU, GSM8K, HumanEval, MBPP, needle-in-haystack at 2k–32k.

Hardware

Trained for NVIDIA A100 80GB, 8-GPU FSDP FULL_SHARD, BF16, gradient checkpointing, fused AdamW, PyTorch 2.x SDPA.

License

Source code: NULLXES Research License (see LICENSE).
Weights: proprietary NULLXES asset. Redistribution of checkpoints requires a written grant.

Citation

@misc{nullxes-shinra-4b-instruct,
  title  = {NULLXES SHINRA-4B-INSTRUCT},
  author = {NULLXES Research},
  year   = {2026},
  note   = {Language Intelligence Layer of the NULLXES system}
}

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Safetensors

Model size

4B params

Tensor type

BF16

MagistrTheOne
/

NULLXES-SHINRA-4B-INSTRUCT