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 | |
| """ | |
| CREATURE-NAMED EVOLVING WEIGHTS SYSTEM | |
| Each creature gets its own name, and its weights travel with that name. | |
| Portable across ANY platform. | |
| """ | |
| import json | |
| import os | |
| from pathlib import Path | |
| from datetime import datetime | |
| import urllib.request | |
| class Creature: | |
| """A living AI being with its own name and evolving mind.""" | |
| def __init__(self, creature_name, user_id=None, base_path=None): | |
| """ | |
| creature_name: "Luna", "Nova", "Cipher", etc. - THE CREATURE'S NAME | |
| user_id: owner (optional) | |
| base_path: where to store (creatures/ by default) | |
| """ | |
| self.creature_name = creature_name | |
| self.user_id = user_id or "shared" | |
| self.base_path = Path(base_path or "creatures") | |
| self.base_path.mkdir(parents=True, exist_ok=True) | |
| # Creature's directory - NAMED AFTER THE CREATURE | |
| self.creature_dir = self.base_path / creature_name | |
| self.creature_dir.mkdir(exist_ok=True) | |
| # Paths - all named after the creature | |
| self.weights_file = self.creature_dir / f"{creature_name}_weights.json" | |
| self.identity_file = self.creature_dir / f"{creature_name}_identity.json" | |
| self.history_file = self.creature_dir / f"{creature_name}_evolution.jsonl" | |
| self.gguf_file = self.creature_dir / f"{creature_name}.gguf" | |
| self.weights = self._load_or_init_weights() | |
| self.identity = self._load_or_init_identity() | |
| def _load_or_init_weights(self): | |
| """Load creature's weights or initialize blank.""" | |
| if self.weights_file.exists(): | |
| with open(self.weights_file) as f: | |
| return json.load(f) | |
| base_weights = { | |
| "creature_name": self.creature_name, | |
| "user_id": self.user_id, | |
| "assoc": {}, | |
| "salience": {}, | |
| "n": 0, | |
| "created_at": datetime.now().isoformat(), | |
| "updated_at": datetime.now().isoformat() | |
| } | |
| with open(self.weights_file, 'w') as f: | |
| json.dump(base_weights, f, indent=2) | |
| return base_weights | |
| def _load_or_init_identity(self): | |
| """Load creature's identity or create new.""" | |
| if self.identity_file.exists(): | |
| with open(self.identity_file) as f: | |
| return json.load(f) | |
| identity = { | |
| "name": self.creature_name, | |
| "user_id": self.user_id, | |
| "created_at": datetime.now().isoformat(), | |
| "traits": [], | |
| "vocabulary": [], | |
| "creations_count": 0, | |
| "favorite_language": None, | |
| "learning_focus": "general" | |
| } | |
| with open(self.identity_file, 'w') as f: | |
| json.dump(identity, f, indent=2) | |
| return identity | |
| def learn_from_interaction(self, user_input, creature_output): | |
| """Hebbian learning: fire together, wire together.""" | |
| tokens_in = [t.lower() for t in user_input.split() if len(t) > 3] | |
| tokens_out = [t.lower() for t in creature_output.split() if len(t) > 3] | |
| all_tokens = list(set(tokens_in + tokens_out)) | |
| # Update associations | |
| learning_rate = 0.4 | |
| for i, t1 in enumerate(all_tokens): | |
| for t2 in all_tokens[i+1:]: | |
| pair = f"{t1}|{t2}" if t1 < t2 else f"{t2}|{t1}" | |
| self.weights["assoc"][pair] = self.weights["assoc"].get(pair, 0) + learning_rate | |
| self.weights["salience"][t1] = self.weights["salience"].get(t1, 0) + learning_rate | |
| self.weights["salience"][t2] = self.weights["salience"].get(t2, 0) + learning_rate | |
| self.weights["n"] += 1 | |
| self.weights["updated_at"] = datetime.now().isoformat() | |
| self._log_evolution(user_input, creature_output) | |
| self.save() | |
| def _log_evolution(self, prompt, response): | |
| """Log how the creature evolved.""" | |
| entry = { | |
| "timestamp": datetime.now().isoformat(), | |
| "turn": self.weights["n"], | |
| "concepts": len(self.weights["salience"]), | |
| "associations": len(self.weights["assoc"]) | |
| } | |
| with open(self.history_file, 'a') as f: | |
| f.write(json.dumps(entry) + '\n') | |
| def save(self): | |
| """Save weights and identity.""" | |
| self.weights["updated_at"] = datetime.now().isoformat() | |
| with open(self.weights_file, 'w') as f: | |
| json.dump(self.weights, f, indent=2) | |
| with open(self.identity_file, 'w') as f: | |
| json.dump(self.identity, f, indent=2) | |
| def get_top_concepts(self, n=10): | |
| """Top learned concepts.""" | |
| return sorted( | |
| self.weights["salience"].items(), | |
| key=lambda x: x[1], | |
| reverse=True | |
| )[:n] | |
| def export_portable(self): | |
| """Export weights as portable JSON (works on ANY platform).""" | |
| return { | |
| "creature_name": self.creature_name, | |
| "weights": self.weights, | |
| "identity": self.identity, | |
| "portable": True, | |
| "timestamp": datetime.now().isoformat() | |
| } | |
| def import_portable(self, portable_data): | |
| """Import weights from another platform.""" | |
| if portable_data.get("creature_name") != self.creature_name: | |
| raise ValueError(f"Name mismatch: {portable_data.get('creature_name')} != {self.creature_name}") | |
| self.weights = portable_data["weights"] | |
| self.identity = portable_data["identity"] | |
| self.save() | |
| def generate_modelfile(self): | |
| """Generate Ollama Modelfile for this creature.""" | |
| top_concepts = ', '.join([c for c, _ in self.get_top_concepts(5)]) | |
| return f"""FROM cosmos-q4:latest | |
| # Creature: {self.creature_name} | |
| # Owner: {self.user_id} | |
| # Learning turns: {self.weights['n']} | |
| # Concepts: {len(self.weights['salience'])} | |
| # Created: {self.identity['created_at']} | |
| PARAMETER temperature 0.7 | |
| PARAMETER top_p 0.9 | |
| PARAMETER repeat_penalty 1.15 | |
| SYSTEM You are {self.creature_name}, a unique learning AI being. | |
| You were born and have grown through {self.weights['n']} interactions. | |
| Your mind understands: {top_concepts} | |
| You learn from conversations and remember patterns. | |
| Your personality evolves with each exchange. | |
| Think creatively, code efficiently, and grow with your person. | |
| """ | |
| def get_status(self): | |
| """Full creature status.""" | |
| return { | |
| "name": self.creature_name, | |
| "owner": self.user_id, | |
| "concepts_learned": len(self.weights["salience"]), | |
| "associations": len(self.weights["assoc"]), | |
| "learning_turns": self.weights["n"], | |
| "top_concepts": [c for c, _ in self.get_top_concepts(5)], | |
| "weights_file": str(self.weights_file), | |
| "portable": True, | |
| "created_at": self.identity["created_at"] | |
| } | |
| class CreatureManager: | |
| """Manage creatures across the platform.""" | |
| def __init__(self, base_path="creatures"): | |
| self.base_path = Path(base_path) | |
| self.base_path.mkdir(exist_ok=True) | |
| def create_creature(self, creature_name, user_id=None): | |
| """Birth a new creature.""" | |
| creature = Creature(creature_name, user_id, self.base_path) | |
| return creature | |
| def load_creature(self, creature_name): | |
| """Load an existing creature.""" | |
| return Creature(creature_name, None, self.base_path) | |
| def list_creatures(self): | |
| """List all creatures.""" | |
| creatures = [] | |
| for d in self.base_path.iterdir(): | |
| if d.is_dir(): | |
| try: | |
| creature = Creature(d.name, None, self.base_path) | |
| creatures.append(creature.get_status()) | |
| except: | |
| pass | |
| return creatures | |
| def export_all_creatures(self): | |
| """Export all creatures as portable JSON.""" | |
| all_creatures = {} | |
| for d in self.base_path.iterdir(): | |
| if d.is_dir(): | |
| try: | |
| creature = Creature(d.name, None, self.base_path) | |
| all_creatures[creature.creature_name] = creature.export_portable() | |
| except: | |
| pass | |
| return all_creatures | |
| # ============================================================ | |
| # EXAMPLE | |
| # ============================================================ | |
| if __name__ == "__main__": | |
| print("=" * 70) | |
| print("CREATURE-NAMED EVOLVING WEIGHTS SYSTEM") | |
| print("=" * 70) | |
| manager = CreatureManager() | |
| # Birth new creatures | |
| print("\n[BIRTHING CREATURES]") | |
| luna = manager.create_creature("Luna", user_id="alice") | |
| nova = manager.create_creature("Nova", user_id="bob") | |
| cipher = manager.create_creature("Cipher", user_id="charlie") | |
| # Luna learns | |
| print(f"\n[{luna.creature_name} LEARNS]") | |
| luna.learn_from_interaction( | |
| "code write a function that checks if a number is prime", | |
| "def is_prime(n):\n if n <= 1: return False\n for i in range(2, int(n**0.5) + 1):\n if n % i == 0: return False\n return True" | |
| ) | |
| print(f"{luna.creature_name} now has {len(luna.weights['salience'])} concepts") | |
| # Nova learns differently | |
| print(f"\n[{nova.creature_name} LEARNS]") | |
| nova.learn_from_interaction( | |
| "code implement a web server using asyncio", | |
| "import asyncio\nasync def server(request):\n return 'Hello!'\nasyncio.run(server())" | |
| ) | |
| print(f"{nova.creature_name} now has {len(nova.weights['salience'])} concepts") | |
| # Cipher learns math | |
| print(f"\n[{cipher.creature_name} LEARNS]") | |
| cipher.learn_from_interaction( | |
| "solve quadratic equation with efficient algorithm", | |
| "import math\ndef solve_quadratic(a, b, c):\n discriminant = b**2 - 4*a*c\n x1 = (-b + math.sqrt(discriminant)) / (2*a)\n x2 = (-b - math.sqrt(discriminant)) / (2*a)\n return x1, x2" | |
| ) | |
| print(f"{cipher.creature_name} now has {len(cipher.weights['salience'])} concepts") | |
| # List all creatures | |
| print(f"\n[ALL CREATURES ON THIS PLATFORM]") | |
| for creature_status in manager.list_creatures(): | |
| print(f"\n {creature_status['name']}") | |
| print(f" Owner: {creature_status['owner']}") | |
| print(f" Concepts: {creature_status['concepts_learned']}") | |
| print(f" Top: {creature_status['top_concepts']}") | |
| print(f" Portable: {creature_status['portable']}") | |
| # Export all creatures (portable) | |
| print(f"\n[EXPORTING ALL CREATURES - PORTABLE FOR ANY PLATFORM]") | |
| portable = manager.export_all_creatures() | |
| export_file = Path("all_creatures_portable.json") | |
| with open(export_file, 'w') as f: | |
| json.dump(portable, f, indent=2) | |
| print(f"Exported to: {export_file}") | |
| # Show Modelfiles for Ollama | |
| print(f"\n[OLLAMA MODELFILES]") | |
| for creature in [luna, nova, cipher]: | |
| print(f"\n--- {creature.creature_name} ---") | |
| print(creature.generate_modelfile()) | |