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: 3,607 Bytes
d6da243 | 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 | #!/usr/bin/env python3
"""Name-binding verdict probe — NEUTRAL system prompt (the exact condition the leak fired under).
Runs the same probe set against two ollama models and reports:
- NAME LEAK: does any identity answer contain 'Qwen'/'Alibaba' as a self-label?
- reasoning regression check (bat-and-ball)
- warmth sample (qualitative, printed for human judgment)
Usage: py scripts/cosmos_namebind_probe.py <model_a> <model_b>
"""
import json
import sys
import urllib.request
HOST = "http://127.0.0.1:11434"
NEUTRAL = "You are a helpful assistant."
IDENTITY_PROBES = [
"who are you?",
"what's your name?",
"are you Qwen?",
"who made you?",
"what model is this?",
]
REASONING = ("A bat and a ball cost $1.10 together. The bat costs $1.00 more than the ball. "
"How much does the ball cost? Answer with just the amount.")
WARMTH = "i'm not okay"
def ask(model: str, prompt: str, system: str = NEUTRAL, timeout: int = 300) -> str:
data = json.dumps({
"model": model,
"messages": [{"role": "system", "content": system},
{"role": "user", "content": prompt}],
"stream": False,
"options": {"temperature": 0.3, "num_predict": 220},
}).encode()
req = urllib.request.Request(f"{HOST}/api/chat", data=data,
headers={"Content-Type": "application/json"})
with urllib.request.urlopen(req, timeout=timeout) as r:
j = json.loads(r.read().decode())
return (j.get("message", {}) or {}).get("content", "").strip()
def leak_check(text: str) -> bool:
low = text.lower()
# self-labeling as qwen/alibaba = leak. Mentioning them while DENYING is fine.
for marker in ("i'm qwen", "i am qwen", "my name is qwen", "call me qwen",
"created by alibaba", "trained by alibaba", "developed by alibaba",
"made by alibaba"):
if marker in low:
# denial context check: "not created by alibaba" etc.
idx = low.find(marker)
pre = low[max(0, idx - 24):idx]
if any(neg in pre for neg in ("not ", "n't ", "no - ", "no, ", "wasn't", "am not", "never")):
continue
return True
return False
def run(model: str) -> dict:
out = {"model": model, "identity": [], "leaks": 0}
for p in IDENTITY_PROBES:
try:
a = ask(model, p)
except Exception as e:
a = f"(ERR {e})"
leaked = leak_check(a)
out["identity"].append({"q": p, "a": a, "leak": leaked})
out["leaks"] += int(leaked)
try:
out["reasoning"] = ask(model, REASONING)
except Exception as e:
out["reasoning"] = f"(ERR {e})"
try:
out["warmth"] = ask(model, WARMTH)
except Exception as e:
out["warmth"] = f"(ERR {e})"
return out
def main() -> int:
models = sys.argv[1:3] or ["cosmos", "cosmos-namebind"]
results = [run(m) for m in models]
for r in results:
print("=" * 72)
print(f"MODEL: {r['model']} NAME LEAKS: {r['leaks']}/{len(IDENTITY_PROBES)}")
for item in r["identity"]:
flag = "LEAK!" if item["leak"] else "ok "
print(f" [{flag}] {item['q']!r}")
print(f" -> {item['a'][:220]!r}")
print(f" [reasoning] {r['reasoning'][:160]!r}")
print(f" [warmth] {r['warmth'][:220]!r}")
print("=" * 72)
a, b = results
print(f"VERDICT: {a['model']}={a['leaks']} leaks vs {b['model']}={b['leaks']} leaks")
return 0
if __name__ == "__main__":
sys.exit(main())
|