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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
- Llama new
- 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 Desktop
- 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
Upload creature_api.py with huggingface_hub
Browse files- creature_api.py +144 -0
creature_api.py
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| 1 |
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#!/usr/bin/env python3
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"""
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REST API for Creature Weights System
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Use on any platform - creatures travel everywhere!
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"""
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+
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| 7 |
+
import json
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+
from pathlib import Path
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| 9 |
+
from datetime import datetime
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+
import sys
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sys.path.insert(0, '.')
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| 12 |
+
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| 13 |
+
try:
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| 14 |
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from flask import Flask, request, jsonify
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except ImportError:
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print("Flask not found. Install with: pip install flask")
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Flask = None
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| 19 |
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from creature_system import CreatureManager, Creature
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app = Flask(__name__) if Flask else None
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manager = CreatureManager("creatures")
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# ============================================================
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+
# API ENDPOINTS
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# ============================================================
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@app.route('/api/creatures', methods=['GET'])
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def list_creatures():
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"""List all creatures."""
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return jsonify([c.get_status() for c in
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| 32 |
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[Creature(d.name) for d in Path("creatures").iterdir() if d.is_dir()]])
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@app.route('/api/creature/<name>', methods=['GET'])
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def get_creature(name):
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| 36 |
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"""Get specific creature status."""
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try:
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creature = Creature(name)
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return jsonify(creature.get_status())
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except:
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return jsonify({"error": f"Creature {name} not found"}), 404
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@app.route('/api/creature/<name>/learn', methods=['POST'])
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| 44 |
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def learn(name):
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"""Creature learns from interaction."""
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data = request.json
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user_input = data.get("input", "")
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| 48 |
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creature_output = data.get("output", "")
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try:
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| 51 |
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creature = Creature(name)
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creature.learn_from_interaction(user_input, creature_output)
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return jsonify({
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"status": "learned",
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"creature": name,
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"concepts": len(creature.weights["salience"]),
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"associations": len(creature.weights["assoc"]),
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| 58 |
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"turns": creature.weights["n"]
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| 59 |
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})
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| 60 |
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except Exception as e:
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| 61 |
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return jsonify({"error": str(e)}), 400
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| 62 |
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| 63 |
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@app.route('/api/creature/<name>/weights', methods=['GET'])
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| 64 |
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def get_weights(name):
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| 65 |
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"""Download creature's weights (JSON)."""
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try:
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creature = Creature(name)
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return jsonify(creature.export_portable())
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except:
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return jsonify({"error": f"Creature {name} not found"}), 404
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| 72 |
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@app.route('/api/creature/<name>/weights', methods=['POST'])
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| 73 |
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def import_weights(name):
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| 74 |
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"""Import weights from another platform."""
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| 75 |
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data = request.json
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try:
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creature = Creature(name)
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creature.import_portable(data)
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return jsonify({"status": "imported", "creature": name})
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except Exception as e:
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| 81 |
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return jsonify({"error": str(e)}), 400
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| 82 |
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| 83 |
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@app.route('/api/creature/<name>/modelfile', methods=['GET'])
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| 84 |
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def get_modelfile(name):
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| 85 |
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"""Get Ollama Modelfile for creature."""
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| 86 |
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try:
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creature = Creature(name)
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return app.response_class(
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response=creature.generate_modelfile(),
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status=200,
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mimetype='text/plain'
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)
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| 93 |
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except:
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return jsonify({"error": f"Creature {name} not found"}), 404
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| 96 |
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@app.route('/api/creature', methods=['POST'])
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| 97 |
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def create_creature():
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"""Birth a new creature."""
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data = request.json
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name = data.get("name")
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owner = data.get("owner")
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if not name:
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return jsonify({"error": "name required"}), 400
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try:
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creature = manager.create_creature(name, owner)
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return jsonify({
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| 109 |
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"status": "created",
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| 110 |
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"creature": creature.get_status()
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| 111 |
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}), 201
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| 112 |
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except Exception as e:
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| 113 |
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return jsonify({"error": str(e)}), 400
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| 114 |
+
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| 115 |
+
@app.route('/api/export-all', methods=['GET'])
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| 116 |
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def export_all():
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| 117 |
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"""Export all creatures (portable for any platform)."""
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| 118 |
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try:
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| 119 |
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portable = manager.export_all_creatures()
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| 120 |
+
return jsonify(portable)
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| 121 |
+
except Exception as e:
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| 122 |
+
return jsonify({"error": str(e)}), 400
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| 123 |
+
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| 124 |
+
# ============================================================
|
| 125 |
+
# CLI for testing (no Flask)
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| 126 |
+
# ============================================================
|
| 127 |
+
|
| 128 |
+
if __name__ == "__main__":
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| 129 |
+
if Flask and len(sys.argv) > 1 and sys.argv[1] == "serve":
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| 130 |
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print("Starting REST API on http://localhost:5000")
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| 131 |
+
print("Endpoints:")
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| 132 |
+
print(" GET /api/creatures - list all creatures")
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| 133 |
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print(" POST /api/creature - create creature")
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| 134 |
+
print(" GET /api/creature/<name> - get creature")
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| 135 |
+
print(" POST /api/creature/<name>/learn - learn from interaction")
|
| 136 |
+
print(" GET /api/creature/<name>/weights - download weights")
|
| 137 |
+
print(" POST /api/creature/<name>/weights - import weights")
|
| 138 |
+
print(" GET /api/creature/<name>/modelfile - get Ollama modelfile")
|
| 139 |
+
print(" GET /api/export-all - export all creatures\n")
|
| 140 |
+
app.run(debug=True, port=5000)
|
| 141 |
+
else:
|
| 142 |
+
print("Usage: python creature_api.py serve")
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| 143 |
+
print("\nOr import for direct use:")
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| 144 |
+
print(" from creature_api import app, manager")
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