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
RAIDEN	Reasoning Intelligence
CERBER	Vision Intelligence
SHINRA	Language Intelligence
AION	Embodied 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