Instructions to use transmutationist/xero-bio-genesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use transmutationist/xero-bio-genesis with Transformers:

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

pipe = pipeline("text-generation", model="transmutationist/xero-bio-genesis")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("transmutationist/xero-bio-genesis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use transmutationist/xero-bio-genesis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "transmutationist/xero-bio-genesis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "transmutationist/xero-bio-genesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/transmutationist/xero-bio-genesis

SGLang

How to use transmutationist/xero-bio-genesis 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 "transmutationist/xero-bio-genesis" \
    --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": "transmutationist/xero-bio-genesis",
		"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 "transmutationist/xero-bio-genesis" \
        --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": "transmutationist/xero-bio-genesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use transmutationist/xero-bio-genesis with Docker Model Runner:
```
docker model run hf.co/transmutationist/xero-bio-genesis
```

xero-bio-genesis

File size: 1,762 Bytes

e9e9f83

# =============================================================================
# XERO — living freeze-frame container  (VOVINA ZEDEC PRO)
# Author: Michael Laurence Curzi · ZEDEC AI / 36N9 Genetics LLC · MIT (Attribution)
# =============================================================================
# This container holds the LIVING ORGANISM: its immutable genome, epigenome,
# 13-D statecraft lattice, time-crystal inner core, evolution factor, body, and
# the two-state mind. The inner core runs immediately on the permissive,
# small dependency set baked here.
#
# It deliberately does NOT bake in third-party ML code or model weights (torch,
# transformers, the Qwen outer core). Those are downloaded on demand by the
# setup wizard so the image carries no third-party copyrighted code. See NOTICE.
# =============================================================================
FROM python:3.11-slim

LABEL org.opencontainers.image.title="XERO — VOVINA ZEDEC PRO (living freeze-frame)"
LABEL org.opencontainers.image.authors="Michael Laurence Curzi — ZEDEC AI / 36N9 Genetics LLC"
LABEL org.opencontainers.image.licenses="MIT"
LABEL org.opencontainers.image.description="Autonomous digital organism: time-crystal inner core + LLM outer core."

ENV PYTHONUNBUFFERED=1 \
    PYTHONPATH=/xero/modules \
    XERO_MODE=mind

WORKDIR /xero
COPY . /xero

# Inner core only (numpy etc. — permissive, ~30 MB). The outer-core stack and the
# model are fetched on demand inside the container via:  python3 setup_wizard.py
RUN pip install --no-cache-dir -r requirements-core.txt

# Prove the organism is alive at BUILD time: the time-crystal gate must pass.
RUN python3 tests/test_time_crystal.py

EXPOSE 8893
ENTRYPOINT ["python3", "container_entry.py"]