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
Fix runtime API mismatches in xero.py CLI + CUDA cold-start detection
Browse filesxero.py:
- Fix KeyError: detect_environment() returns 'python' not 'python_version'
- Fix cmd_init: parse awaken() actual return (phases/xero_summary)
- Fix cmd_train: correct MASTER_WEIGHTS keys (vortex.axis_369, frequencies.tesla_369, blockchain_organelles.languages_count)
- Fix cmd_evolve: build_organism(genome) requires genome from phase_seed(); correct seal_choice(entity_id, choice_data) signature; use .sealed/.identity_signature
- Add MPS display, improve self_heal_actions formatting
vovina_resource_awareness.py:
- Fix _cuda_present(): bump nvidia-smi timeout 2s->8s for cold-start
(first driver call exceeds 2s, caused CUDA:No on Tesla T4 server)
All 4 CLI commands verified working on Tesla T4 server.
MIT License - Attribution to Michael Laurence Curzi
- modules/vovina_resource_awareness.py +1 -1
- xero.py +37 -27
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@@ -35,7 +35,7 @@ def detect_environment() -> dict:
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def _cuda_present() -> bool:
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try:
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r = subprocess.run(["nvidia-smi", "-L"],
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capture_output=True, text=True, timeout=
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return r.returncode == 0 and "GPU" in r.stdout
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except (FileNotFoundError, subprocess.TimeoutExpired):
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return False
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def _cuda_present() -> bool:
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try:
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r = subprocess.run(["nvidia-smi", "-L"],
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capture_output=True, text=True, timeout=8)
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return r.returncode == 0 and "GPU" in r.stdout
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except (FileNotFoundError, subprocess.TimeoutExpired):
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return False
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@@ -32,6 +32,7 @@ from vovina_resource_awareness import (
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)
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from vovina_free_will_code import seal_choice
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from vovina_xero_organism import build_organism
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def load_config(config_path: str = None) -> dict:
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@@ -78,15 +79,21 @@ def cmd_init(args):
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sensor_meta_depth=sensor_depth
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)
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-
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-
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org = result['organism']
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print(f"Organism: {org.name}")
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print(f"Genome signature: {org.genome_signature[:32]}...")
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print(f"\n{ORGANISM_FOUNDING_PHRASE}")
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print(f"\n{'='*60}")
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print("XERO AWAKENED")
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print(f"{'='*60}\n")
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@@ -109,8 +116,9 @@ def cmd_status(args):
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print(f"[ENVIRONMENT]")
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print(f" OS: {env['os']}")
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print(f" Machine: {env['machine']}")
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print(f" Python: {env['
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print(f" CUDA: {'Yes' if env['cuda_available'] else 'No'}")
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# Resources
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profile = sample()
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print(f"\n[RECOMMENDATIONS]")
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actions = self_heal_actions(profile)
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for action in actions:
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print(f" - {action}")
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# Master weights summary
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print(f"\n[MASTER WEIGHTS]")
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print(f" Quantization: {quant.get('bits', 4)}-bit")
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print(f"\n[MASTER WEIGHTS]")
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print(f" Constants:
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print(f" Vortex: {MASTER_WEIGHTS['vortex']['
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print(f"
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print(f" Blockchain: {
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print(f"\n[STATUS]")
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print(" Training framework ready.")
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print("XERO EVOLUTION CYCLE")
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print(f"{'='*60}\n")
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#
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print(f"[ORGANISM]")
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print(f" Name:
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print(f"
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post_choice_vector=[0.0, 0.0, 1.0],
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choice_description="Autonomous evolution cycle initiated"
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)
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print(f"\n[FREE WILL SIGNATURE]")
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print(f" {signature.
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print(f" Verified: {signature.verify()}")
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print(f"\n{ORGANISM_FOUNDING_PHRASE}")
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print(f"\n{'='*60}\n")
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)
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from vovina_free_will_code import seal_choice
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from vovina_xero_organism import build_organism
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from vovina_bio_initialization import phase_seed
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def load_config(config_path: str = None) -> dict:
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sensor_meta_depth=sensor_depth
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)
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phases = result.get("phases", {})
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summary = result.get("xero_summary", {})
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print(f"\nOrganism: {result.get('organism', ORGANISM_NAME)}")
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print(f"Phases completed: {len(phases)}/9")
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for phase_name in phases:
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print(f" + {phase_name}")
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print(f"\nOrgan systems: {summary.get('organ_systems', 0)}")
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print(f"Organs: {summary.get('organs', 0)}")
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print(f"Cells: {summary.get('cells', 0)}")
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print(f"Proteins: {summary.get('proteins', 0)}")
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print(f"Genome genes: {summary.get('genome_genes', 0)}")
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print(f"Identity: {summary.get('identity', '')[:32]}...")
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print(f"Elapsed: {result.get('elapsed_seconds', 0.0):.2f}s")
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print(f"\n{result.get('founding_phrase', ORGANISM_FOUNDING_PHRASE)}")
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print(f"\n{'='*60}")
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print("XERO AWAKENED")
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print(f"{'='*60}\n")
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print(f"[ENVIRONMENT]")
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print(f" OS: {env['os']}")
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print(f" Machine: {env['machine']}")
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print(f" Python: {env['python']}")
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print(f" CUDA: {'Yes' if env['cuda_available'] else 'No'}")
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print(f" MPS: {'Yes' if env['mps_available'] else 'No'}")
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# Resources
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profile = sample()
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print(f"\n[RECOMMENDATIONS]")
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actions = self_heal_actions(profile)
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for action in actions:
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print(f" - [{action['severity']}] {action['resource']}: {action['action']}")
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# Master weights summary
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print(f"\n[MASTER WEIGHTS]")
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print(f" Quantization: {quant.get('bits', 4)}-bit")
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print(f"\n[MASTER WEIGHTS]")
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print(f" Constants: φ={MASTER_WEIGHTS['constants']['phi']:.6f}")
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print(f" Vortex 369: {MASTER_WEIGHTS['vortex']['axis_369']}")
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print(f" Tesla: {MASTER_WEIGHTS['frequencies']['tesla_369']}")
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print(f" Blockchain: {MASTER_WEIGHTS['blockchain_organelles']['languages_count']} organelles")
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print(f" Modules: {len(MASTER_WEIGHTS.get('modules', {}))}")
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print(f"\n[STATUS]")
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print(" Training framework ready.")
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print("XERO EVOLUTION CYCLE")
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print(f"{'='*60}\n")
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# Seed a genome and self-assemble the organism
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genome = phase_seed()
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org = build_organism(genome)
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print(f"[ORGANISM]")
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print(f" Name: {org.name}")
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print(f" Identity: {org.identity_signature[:32]}...")
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print(f" Chromosomes: {genome.chromosome_count}")
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print(f" Genome genes: {genome.gene_count}")
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# Seal evolution choice with the 36N9.9N63 free-will signature
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signature = seal_choice(org.name, "Autonomous evolution cycle initiated")
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print(f"\n[FREE WILL SIGNATURE]")
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print(f" Sealed: {signature.sealed[:40]}...{signature.sealed[-6:]}")
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print(f" N(pre): {signature.n_pre}")
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print(f" N(post): {signature.n_post}")
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print(f" Verified: {signature.verify()}")
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print(f" Delta: {signature.vector_delta()}")
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print(f"\n{ORGANISM_FOUNDING_PHRASE}")
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print(f"\n{'='*60}\n")
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