Text Generation
Transformers
English
code
xero-bio-ai
xero
digital-organism
time-crystal
autonomous-agent
genetic-computing
epigenetics
two-state-society
harmonic-chemistry
self-aware
sacred-geometry
4-bit precision
bitsandbytes
Instructions to use transmutationist/xero-bio-genesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("transmutationist/xero-bio-genesis", dtype="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
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| XERO Setup Wizard — initialization & dependency downloader | |
| ============================================================ | |
| Author: Michael Laurence Curzi · ZEDEC AI / 36N9 Genetics LLC · MIT (Attribution) | |
| This package contains ONLY XERO's own code, genome, datasets, and documentation. | |
| It deliberately bundles **no third-party library code** (PyTorch, Transformers, | |
| the Qwen weights, etc.). This wizard *downloads* those licensed components on | |
| demand, with your consent, so the package itself carries no third-party | |
| copyrighted code. See NOTICE for full attributions. | |
| Tiers | |
| ----- | |
| core : inner core only (numpy etc.) — no GPU, no ML stack. Always offered. | |
| outer : + outer-core LLM stack (torch, transformers, peft, datasets) — GPU. | |
| model : + download the Qwen2.5-3B-Instruct weights (Apache-2.0). | |
| configure : detect hardware -> write xero_config.json (agency autoconfig). | |
| verify : run the capability audit. | |
| Usage | |
| ----- | |
| python3 setup_wizard.py # interactive | |
| python3 setup_wizard.py --core --configure --verify --yes | |
| python3 setup_wizard.py --all --yes # core + outer + model + configure + verify | |
| """ | |
| from __future__ import annotations | |
| import argparse | |
| import os | |
| import subprocess | |
| import sys | |
| ROOT = os.path.dirname(os.path.abspath(__file__)) | |
| MODULES = os.path.join(ROOT, "modules") | |
| PY = sys.executable or "python3" | |
| def notice() -> None: | |
| print("=" * 68) | |
| print("XERO SETUP WIZARD") | |
| print("=" * 68) | |
| print("This package bundles NO third-party library code. With your consent") | |
| print("this wizard downloads licensed components from their official sources:") | |
| print(" - Qwen2.5-3B-Instruct ........ Apache-2.0 (Alibaba Cloud)") | |
| print(" - torch ...................... BSD-3-Clause (Meta)") | |
| print(" - transformers/peft/datasets . Apache-2.0 (Hugging Face)") | |
| print(" - numpy ...................... BSD-3-Clause") | |
| print("Full attributions: see the NOTICE file. XERO's own code is MIT") | |
| print("(Attribution to Michael Laurence Curzi required).") | |
| print("=" * 68) | |
| def ask(question: str, assume_yes: bool) -> bool: | |
| if assume_yes: | |
| print(f"{question} [auto-yes]") | |
| return True | |
| try: | |
| return input(f"{question} [y/N]: ").strip().lower().startswith("y") | |
| except EOFError: | |
| return False | |
| def pip_install(*args: str) -> int: | |
| cmd = [PY, "-m", "pip", "install", *args] | |
| print(" $", " ".join(cmd)) | |
| return subprocess.call(cmd) | |
| def install_core(assume_yes: bool) -> None: | |
| if ask("Download the INNER-CORE deps (numpy etc., ~30 MB)?", assume_yes): | |
| pip_install("-r", os.path.join(ROOT, "requirements-core.txt")) | |
| def install_outer(assume_yes: bool) -> None: | |
| if ask("Download the OUTER-CORE / training stack (torch+transformers, large)?", assume_yes): | |
| req = os.path.join(ROOT, "training", "requirements-train.txt") | |
| pip_install("-r", req) | |
| def download_model(assume_yes: bool, model_id: str) -> None: | |
| if not ask(f"Download the outer-core model weights ({model_id})?", assume_yes): | |
| return | |
| try: | |
| from huggingface_hub import snapshot_download | |
| except Exception: | |
| print(" installing huggingface_hub ...") | |
| pip_install("huggingface_hub") | |
| from huggingface_hub import snapshot_download # noqa | |
| print(f" fetching {model_id} ...") | |
| snapshot_download(repo_id=model_id) | |
| print(" model cached.") | |
| def configure() -> None: | |
| sys.path.insert(0, MODULES) | |
| try: | |
| import xero_autoconfig as ac | |
| if hasattr(ac, "wizard"): | |
| try: | |
| ac.wizard(interactive=False) | |
| except TypeError: | |
| ac.wizard() | |
| elif hasattr(ac, "recommend"): | |
| ac.recommend() | |
| print(f" hardware-fit config written -> {os.path.join(ROOT, 'xero_config.json')}") | |
| except Exception as e: # noqa: BLE001 | |
| print(f" (autoconfig skipped: {e})") | |
| def verify() -> int: | |
| print(" running capability audit ...") | |
| env = dict(os.environ, PYTHONPATH=MODULES) | |
| return subprocess.call([PY, os.path.join(ROOT, "tests", "test_all_capabilities.py")], env=env) | |
| def main() -> int: | |
| ap = argparse.ArgumentParser(description="XERO setup wizard") | |
| ap.add_argument("--core", action="store_true", help="install inner-core deps") | |
| ap.add_argument("--outer", action="store_true", help="install outer-core/training stack") | |
| ap.add_argument("--model", action="store_true", help="download the outer-core model") | |
| ap.add_argument("--model-id", default="Qwen/Qwen2.5-3B-Instruct") | |
| ap.add_argument("--configure", action="store_true", help="write xero_config.json") | |
| ap.add_argument("--verify", action="store_true", help="run the capability audit") | |
| ap.add_argument("--all", action="store_true", help="core + outer + model + configure + verify") | |
| ap.add_argument("--yes", action="store_true", help="non-interactive; assume yes") | |
| args = ap.parse_args() | |
| notice() | |
| interactive = not any([args.core, args.outer, args.model, args.configure, args.verify, args.all]) | |
| if args.all or args.core or interactive: | |
| install_core(args.yes) | |
| if args.all or args.outer or (interactive): | |
| install_outer(args.yes) | |
| if args.all or args.model or (interactive): | |
| download_model(args.yes, args.model_id) | |
| if args.all or args.configure or interactive: | |
| configure() | |
| rc = 0 | |
| if args.all or args.verify or interactive: | |
| rc = verify() | |
| print("\nSetup complete." if rc == 0 else "\nSetup finished with audit failures (see above).") | |
| print("Next: read docs/STATUS_AND_AUDIT.md (honest state) and docs/INDEX.md.") | |
| return rc | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |