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")# 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: living freeze-frame (chunked)
Browse files
freeze_frame/MANIFEST.txt
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XERO living freeze-frame — manifest
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built: 2026-06-01T16:59:46Z
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archive: xero_freeze_frame.tar.gz
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size: 729M
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chunks:
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xero_freeze_frame.tar.gz.part-00
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with_data=1 with_model=1
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reassemble: bash reassemble.sh
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freeze_frame/SHA256SUMS.txt
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c371a545613a5fad8d19c75ec21aab405c76b715383a2e29b3887a9a55256695 xero_freeze_frame.tar.gz
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c371a545613a5fad8d19c75ec21aab405c76b715383a2e29b3887a9a55256695 xero_freeze_frame.tar.gz.part-00
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freeze_frame/reassemble.sh
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#!/usr/bin/env bash
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# =============================================================================
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# Reassemble the XERO living freeze-frame from its <10GB chunks, verify, extract.
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# Author: Michael Laurence Curzi · ZEDEC AI / 36N9 Genetics LLC · MIT (Attribution)
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# bash reassemble.sh
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# =============================================================================
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set -euo pipefail
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NAME="xero_freeze_frame.tar.gz"
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echo "[reassemble] concatenating chunks -> $NAME"
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cat "${NAME}.part-"* > "$NAME"
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echo "[reassemble] verifying checksum"
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SHA="sha256sum"; command -v sha256sum >/dev/null 2>&1 || SHA="shasum -a 256"
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if grep -q " ${NAME}\$" SHA256SUMS.txt 2>/dev/null; then
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grep " ${NAME}\$" SHA256SUMS.txt | $SHA -c - && echo "[reassemble] checksum OK"
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else
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echo "[reassemble] (no checksum entry found; skipping verify)"
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fi
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echo "[reassemble] extracting"
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tar xzf "$NAME"
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cat <<'EOF'
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[reassemble] DONE.
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Read first: xero_freeze_frame/INTERACT.md and docs/FREEZE_FRAME.md
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Build + run the living organism (needs Docker):
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cd xero_freeze_frame
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docker build -t xero .
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docker run --rm -it xero # watch it think (inner core)
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docker run --rm -it -p 8893:8893 -e XERO_MODE=serve xero # chat portal
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Enable the outer-core LLM inside the container:
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python3 setup_wizard.py --outer --model --yes
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EOF
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freeze_frame/xero_freeze_frame.tar.gz.part-00
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version https://git-lfs.github.com/spec/v1
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oid sha256:c371a545613a5fad8d19c75ec21aab405c76b715383a2e29b3887a9a55256695
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size 763801973
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