Instructions to use MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1

SGLang

How to use MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1 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-L-TEX-20B-Genesis-v0.1" \
    --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": "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1",
		"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 "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1" \
        --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": "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1 with Docker Model Runner:
```
docker model run hf.co/MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1
```

NULLXES-LÆTEX-20B-Genesis-v0.1

Weight Genesis scaffold of the NULLXES-LÆTEX Stage2 dense proxy (~18.8B params, A35B-compatible width).

Developed by NULLXES · nullxesdai.online · Hub: MagistrTheOne

Status: research brick / architectural checkpoint.
Not a chat model. Random muP-init weights — expect nonsense generations until Stage2 train.
Next rework / first trained weights target: August 2026.

What this is

Own architecture: LatexForCausalLM (model_type=latex), NHAT hybrid attention
Shape: L=24, d_model=8192, GQA 64/8, d_ff=22016 → ~18.757B
Tokenizer: NULLXES-L-TEX-Tokenizer-v0.2 (131072 Unigram, full fill)
Init: muP + DeepNorm residual scaling, bf16 sharded safetensors
Intended path: Stage2 pretrain → A35B depth expand → future 480B-A35B MoE

What this is NOT

Not instruct / not SFT / not a Digital Employee personality
Not trained language competence (identity QA will fail on purpose)
Not distilled from Qwen / Llama / Mistral / DeepSeek / GLM
Not a replacement for 100M Stage0a (that one is identity-trained)

Load

import torch
from transformers import AutoModelForCausalLM, AutoConfig

repo = "MagistrTheOne/NULLXES-L-TEX-20B-Genesis-v0.1"
config = AutoConfig.from_pretrained(repo, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
)

Roadmap

When	Milestone
now	Genesis v0.1 (this repo)
next week	Stage2 ZeRO-3 smoke + ~100M tokens mid-eval on RTX PRO 6000
Aug 2026	First trained 20B weights / card refresh
later	A35B dense → 480B-A35B MoE (cluster)

Contact

NULLXES · @MagistrTheOne · ceo@nullxes.com

Downloads last month: 10

Safetensors

Model size

19B params

Tensor type

BF16