Text Generation
PyTorch
GGUF
English
quantum
quantum-entropy
from-scratch
char-level
cosmic-synapse-theory
custom-architecture
llama-cpp
continual-learning
reproducible-seed
open-science
null-results
Instructions to use phera-ra/QC67_cosmo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use phera-ra/QC67_cosmo with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: llama cli -hf phera-ra/QC67_cosmo
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./llama-cli -hf phera-ra/QC67_cosmo
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf phera-ra/QC67_cosmo # Run inference directly in the terminal: ./build/bin/llama-cli -hf phera-ra/QC67_cosmo
Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- LM Studio
- Jan
- vLLM
How to use phera-ra/QC67_cosmo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "phera-ra/QC67_cosmo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "phera-ra/QC67_cosmo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/phera-ra/QC67_cosmo
- Ollama
How to use phera-ra/QC67_cosmo with Ollama:
ollama run hf.co/phera-ra/QC67_cosmo
- Unsloth Studio
How to use phera-ra/QC67_cosmo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for phera-ra/QC67_cosmo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for phera-ra/QC67_cosmo to start chatting
- Docker Model Runner
How to use phera-ra/QC67_cosmo with Docker Model Runner:
docker model run hf.co/phera-ra/QC67_cosmo
- Lemonade
How to use phera-ra/QC67_cosmo with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull phera-ra/QC67_cosmo
Run and chat with the model
lemonade run user.QC67_cosmo-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 6,228 Bytes
b8fadbf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 | #!/usr/bin/env python3
"""Offline integrity and privacy-boundary verifier for the QC67 Cosmos kit."""
from __future__ import annotations
import argparse
import hashlib
import json
import sys
from collections import Counter
from pathlib import Path
ROOT = Path(__file__).resolve().parent
MANIFEST = ROOT / "RELEASE_MANIFEST.json"
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as handle:
for chunk in iter(lambda: handle.read(1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def check_manifest(strict: bool) -> list[str]:
errors: list[str] = []
release = json.loads(MANIFEST.read_text(encoding="utf-8"))
expected = set()
for entry in release.get("files", []):
rel = entry["path"]
expected.add(rel)
path = ROOT / rel
if not path.is_file():
errors.append(f"missing: {rel}")
continue
size = path.stat().st_size
if size != int(entry["bytes"]):
errors.append(f"size mismatch: {rel} ({size} != {entry['bytes']})")
continue
actual = sha256(path)
if actual != entry["sha256"]:
errors.append(f"hash mismatch: {rel}")
if strict:
ignored = {"RELEASE_MANIFEST.json"}
actual = {
path.relative_to(ROOT).as_posix()
for path in ROOT.rglob("*")
if path.is_file()
and path.relative_to(ROOT).as_posix() not in ignored
and not path.relative_to(ROOT).as_posix().startswith("downloads/")
}
for rel in sorted(actual - expected):
errors.append(f"unmanifested file: {rel}")
return errors
def check_blank_credentials() -> list[str]:
errors: list[str] = []
config = json.loads(
(ROOT / "genesis_engine" / "config.json").read_text(encoding="utf-8")
)
for key in ("ibm_token", "azure_connection_string"):
if str(config.get(key) or "").strip():
errors.append(f"credential field is not blank: genesis_engine/config.json:{key}")
forbidden_names = ("oauth2_tokens.json", ".env", "credentials.json")
for path in ROOT.rglob("*"):
if path.is_file() and path.name.casefold() in forbidden_names:
errors.append(f"forbidden credential file present: {path.relative_to(ROOT)}")
return errors
def check_public_archive() -> tuple[list[str], dict]:
errors: list[str] = []
archive = ROOT / "data" / "quantum_measurements_public.jsonl"
data_manifest = json.loads(
(ROOT / "data" / "quantum_measurements_manifest.json").read_text(
encoding="utf-8"
)
)
records = Counter()
samples = Counter()
total = 0
for line_number, line in enumerate(
archive.open(encoding="utf-8", errors="strict"), 1
):
try:
row = json.loads(line)
except Exception as exc:
errors.append(f"archive line {line_number}: invalid JSON ({exc})")
continue
counts = row.get("counts")
if not isinstance(counts, dict) or not counts:
errors.append(f"archive line {line_number}: missing counts")
continue
observed = sum(int(value) for value in counts.values())
declared = int(row.get("total_shots", -1))
if observed != declared:
errors.append(
f"archive line {line_number}: shot mismatch {observed} != {declared}"
)
category = str(row.get("provider_class") or "missing")
records[category] += 1
samples[category] += observed
total += observed
expected = data_manifest["summary"]
if total != int(expected["total_samples"]):
errors.append(
f"archive total mismatch: {total} != {expected['total_samples']}"
)
for category, expected_count in expected["records_by_provider_class"].items():
if records[category] != int(expected_count):
errors.append(
f"archive record count mismatch for {category}: "
f"{records[category]} != {expected_count}"
)
return errors, {
"records": sum(records.values()),
"samples": total,
"records_by_class": dict(records),
"samples_by_class": dict(samples),
}
def check_model_metadata() -> list[str]:
errors: list[str] = []
metadata = json.loads(
(ROOT / "weights" / "cosmos_born.meta.json").read_text(encoding="utf-8")
)
if int(metadata.get("params", 0)) != 1_842_432:
errors.append("unexpected cosmos_born parameter count")
if str(metadata.get("base_model") or "").upper().split()[0] != "NONE":
errors.append("cosmos_born metadata no longer reports a from-scratch base")
return errors
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument(
"--no-strict",
action="store_true",
help="allow extra files not listed in the release manifest",
)
args = parser.parse_args()
if not MANIFEST.is_file():
print("[FAIL] RELEASE_MANIFEST.json is missing")
return 1
errors = []
errors.extend(check_manifest(strict=not args.no_strict))
errors.extend(check_blank_credentials())
archive_errors, archive_stats = check_public_archive()
errors.extend(archive_errors)
errors.extend(check_model_metadata())
if errors:
print(f"[FAIL] {len(errors)} release check(s) failed")
for error in errors:
print(" -", error)
return 1
print("[OK] release manifest hashes verified")
print("[OK] shipped cloud credential fields are blank")
print("[OK] cosmos_born metadata is internally consistent")
print(
"[OK] public archive:",
f"{archive_stats['records']:,} records,",
f"{archive_stats['samples']:,} samples",
)
for category in sorted(archive_stats["records_by_class"]):
print(
" ",
category,
f"{archive_stats['records_by_class'][category]:,} records /",
f"{archive_stats['samples_by_class'][category]:,} samples",
)
return 0
if __name__ == "__main__":
raise SystemExit(main())
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