Instructions to use MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0 with Transformers:

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

pipe = pipeline("text-generation", model="MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0 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-100M-Stage0a-v0"
# 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-100M-Stage0a-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0

SGLang

How to use MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0 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-100M-Stage0a-v0" \
    --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-100M-Stage0a-v0",
		"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-100M-Stage0a-v0" \
        --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-100M-Stage0a-v0",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0 with Docker Model Runner:
```
docker model run hf.co/MagistrTheOne/NULLXES-L-TEX-100M-Stage0a-v0
```

NULLXES-L-TEX-100M-Stage0a-v0 / dataset_manifest.json

MagistrTheOne

NULLXES-LÆTEX-100M-Stage0a-v0 identity bootstrap

786df5b verified about 2 months ago

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	{
	"name": "pretrain_stage0",
	"version": "0.2-identity",
	"mix": {
	"multilingual": 0.4,
	"code": 0.25,
	"enterprise": 0.2,
	"scientific": 0.1,
	"synthetic_structure": 0.05
	},
	"shards": {
	"multilingual": {
	"files": [
	"datasets/seed/multilingual/seed_0001.jsonl",
	"datasets/raw/shards/identity/nullxes_identity.jsonl"
	],
	"docs": 36
	},
	"code": {
	"files": [
	"datasets/seed/code/seed_0001.jsonl",
	"datasets/raw/shards/code/nullxes_repo_clean.jsonl",
	"datasets/raw/shards/code/frontend.jsonl",
	"datasets/raw/shards/code/backend.jsonl",
	"datasets/raw/shards/code/infrastructure.jsonl"
	],
	"docs": 168
	},
	"enterprise": {
	"files": [
	"datasets/seed/enterprise/seed_0001.jsonl",
	"datasets/raw/shards/docs/digital_employee.jsonl",
	"datasets/raw/shards/docs/enterprise_ai.jsonl"
	],
	"docs": 31
	},
	"scientific": {
	"files": [
	"datasets/seed/scientific/seed_0001.jsonl",
	"datasets/raw/shards/identity/latex_architecture.jsonl",
	"datasets/raw/shards/reasoning/technical_reasoning.jsonl"
	],
	"docs": 31
	},
	"synthetic_structure": {
	"files": [
	"datasets/seed/synthetic_structure/seed_0001.jsonl",
	"datasets/sft/identity_v0.1.jsonl"
	],
	"docs": 25
	}
	},
	"totals": {
	"multilingual": 36,
	"code": 168,
	"enterprise": 31,
	"scientific": 31,
	"synthetic_structure": 25
	},
	"notes": "Identity + repo code + seed. Goal: LÆTEX knows its name; Stage0a pretrain next — not 7B random SFT.",
	"status": "READY_FOR_STAGE0A"
	}