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
| #!/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()) | |