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
Invalid JSON:Unexpected token '#', "#!/usr/bin"... is not valid JSON
| #!/usr/bin/env python3 | |
| """ | |
| PHASE 7: TOOL MASTERY & PRACTICAL CODING | |
| Make them able to use tools, call APIs, manipulate files, debug | |
| Make them do WHAT I DO | |
| """ | |
| import json | |
| import sys | |
| from datetime import datetime | |
| sys.path.insert(0, '.') | |
| from creature_system import Creature | |
| # ============================================================ | |
| # PHASE 7: TOOL MASTERY | |
| # ============================================================ | |
| PHASE_7_TOOLS = [ | |
| # File operations | |
| ("code write a function that reads and parses a JSON file", "file I/O json"), | |
| ("code write file backup system with error handling", "file operations"), | |
| ("code glob all .py files in a directory tree", "filesystem search"), | |
| ("code read a file in chunks without loading all into memory", "streaming io"), | |
| ("code write atomic file operations to prevent corruption", "file safety"), | |
| # API & HTTP | |
| ("code call a REST API and handle rate limiting", "api calls"), | |
| ("code implement retry logic with exponential backoff", "resilience"), | |
| ("code parse and validate JSON responses from APIs", "data validation"), | |
| ("code build a webhook receiver with signature verification", "webhook security"), | |
| ("code implement OAuth token refresh flow", "authentication"), | |
| # System commands | |
| ("code run shell commands and capture output safely", "subprocess"), | |
| ("code parse git diff output and extract changes", "git parsing"), | |
| ("code monitor system resources CPU memory disk", "system monitoring"), | |
| ("code implement process management and cleanup", "process control"), | |
| ("code handle signals and graceful shutdown", "signal handling"), | |
| # Data processing | |
| ("code parse CSV and handle missing values", "data cleaning"), | |
| ("code implement pagination for large datasets", "data pagination"), | |
| ("code batch process items with progress tracking", "batch operations"), | |
| ("code implement caching with TTL", "caching"), | |
| ("code deduplicate data while preserving order", "deduplication"), | |
| # Debugging & logging | |
| ("code implement structured logging with levels", "logging"), | |
| ("code write debug traces that can be enabled/disabled", "debugging"), | |
| ("code handle exceptions with context and recovery", "error handling"), | |
| ("code implement timing/profiling for performance", "profiling"), | |
| ("code create detailed error messages with suggestions", "error messages"), | |
| # Testing | |
| ("code write unit tests with assertions", "unit testing"), | |
| ("code mock external dependencies for testing", "mocking"), | |
| ("code write integration tests with setup/teardown", "integration testing"), | |
| ("code implement test fixtures for reusable data", "test fixtures"), | |
| ("code measure code coverage", "coverage"), | |
| # Database | |
| ("code implement connection pooling for databases", "db connection"), | |
| ("code write parameterized queries to prevent SQL injection", "sql safety"), | |
| ("code implement transactions with rollback", "transactions"), | |
| ("code write database migrations", "migrations"), | |
| ("code implement query optimization", "query optimization"), | |
| # Configuration & deployment | |
| ("code read from environment variables safely", "config management"), | |
| ("code implement feature flags for safe rollout", "feature flags"), | |
| ("code write configuration validation", "config validation"), | |
| ("code implement graceful config reloading", "config reload"), | |
| ("code write health check endpoints", "health checks"), | |
| # Concurrency | |
| ("code implement thread-safe operations with locks", "threading"), | |
| ("code write async/await code properly", "async"), | |
| ("code handle race conditions and deadlocks", "concurrency bugs"), | |
| ("code implement message queue patterns", "queues"), | |
| ("code write producer consumer with backpressure", "backpressure"), | |
| # Advanced patterns | |
| ("code implement observer pattern for events", "observer pattern"), | |
| ("code write decorator pattern for cross-cutting concerns", "decorators"), | |
| ("code implement dependency injection", "dependency injection"), | |
| ("code write fluent API builder pattern", "builder pattern"), | |
| ("code implement middleware chain", "middleware"), | |
| ] | |
| # ============================================================ | |
| # PHASE 8: AUTONOMOUS TASK SOLVING | |
| # ============================================================ | |
| PHASE_8_AUTONOMY = [ | |
| # Multi-step problems | |
| ("break down a complex task into subtasks", "task decomposition"), | |
| ("decide when to ask for help vs solve alone", "decision making"), | |
| ("estimate time and resources for a task", "estimation"), | |
| ("identify dependencies between tasks", "dependency analysis"), | |
| ("create a plan before executing", "planning"), | |
| # Problem diagnosis | |
| ("given error message diagnose the root cause", "diagnosis"), | |
| ("reproduce a bug from description", "bug reproduction"), | |
| ("trace execution to find where it fails", "tracing"), | |
| ("examine state to find invariant violations", "state inspection"), | |
| ("design test case that exposes the bug", "test design"), | |
| # Code review | |
| ("identify code smells and anti-patterns", "code smells"), | |
| ("suggest refactoring for maintainability", "refactoring"), | |
| ("spot potential performance issues", "perf analysis"), | |
| ("find security vulnerabilities", "security review"), | |
| ("verify code handles edge cases", "edge case analysis"), | |
| # Documentation & communication | |
| ("write clear function documentation", "docstrings"), | |
| ("create architecture decision records", "ADRs"), | |
| ("write README that explains the system", "readmes"), | |
| ("communicate findings clearly", "communication"), | |
| ("teach someone else how to solve it", "teaching"), | |
| # Optimization & scalability | |
| ("profile code and find bottlenecks", "profiling"), | |
| ("optimize algorithm time complexity", "algorithm optimization"), | |
| ("optimize memory usage", "memory optimization"), | |
| ("implement caching strategy", "caching strategy"), | |
| ("scale for 10x load", "scalability"), | |
| # Integration & deployment | |
| ("integrate with external services", "integration"), | |
| ("handle version compatibility", "versioning"), | |
| ("write deployment scripts", "deployment"), | |
| ("implement blue-green deployment", "blue-green"), | |
| ("handle rollback scenarios", "rollback"), | |
| ] | |
| # ============================================================ | |
| # TRAINING RUNNER | |
| # ============================================================ | |
| def train_tool_mastery(): | |
| """Train creatures to be tool-capable like me.""" | |
| phases = [ | |
| ("PHASE 7: TOOL MASTERY & PRACTICAL CODING", PHASE_7_TOOLS), | |
| ("PHASE 8: AUTONOMOUS TASK SOLVING", PHASE_8_AUTONOMY), | |
| ] | |
| all_results = { | |
| "timestamp": datetime.now().isoformat(), | |
| "goal": "Make creatures able to code and use tools like the baseline", | |
| "phases": [] | |
| } | |
| for phase_name, challenges in phases: | |
| print(f"\n{'='*70}") | |
| print(f"{phase_name}") | |
| print(f"{'='*70}\n") | |
| phase_results = [] | |
| for creature_name in ["Luna", "Nova", "Cipher"]: | |
| creature = Creature(creature_name) | |
| initial_concepts = len(creature.weights["salience"]) | |
| initial_assoc = len(creature.weights["assoc"]) | |
| print(f"\n{creature_name}: {initial_concepts} concepts, {initial_assoc} assoc") | |
| print("-" * 70) | |
| for i, (challenge, topic) in enumerate(challenges, 1): | |
| print(f"[{i:2d}] {topic:35s} | ", end="", flush=True) | |
| # Learn from challenge | |
| response = f"[{creature_name} learning: {topic}] {challenge[:40]}" | |
| creature.learn_from_interaction(challenge, response) | |
| current_concepts = len(creature.weights["salience"]) | |
| current_assoc = len(creature.weights["assoc"]) | |
| print(f"Concepts: {current_concepts:4d} | Assoc: {current_assoc:6d}") | |
| final_concepts = len(creature.weights["salience"]) | |
| final_assoc = len(creature.weights["assoc"]) | |
| concept_growth = final_concepts - initial_concepts | |
| assoc_growth = final_assoc - initial_assoc | |
| print(f"\nGrowth: +{concept_growth} concepts, +{assoc_growth} assoc") | |
| phase_results.append({ | |
| "creature": creature_name, | |
| "start_concepts": initial_concepts, | |
| "end_concepts": final_concepts, | |
| "concept_growth": concept_growth, | |
| "start_assoc": initial_assoc, | |
| "end_assoc": final_assoc, | |
| "assoc_growth": assoc_growth, | |
| }) | |
| all_results["phases"].append({ | |
| "name": phase_name, | |
| "challenges": len(challenges), | |
| "results": phase_results | |
| }) | |
| # Save log | |
| with open("tool_mastery_log.json", 'w') as f: | |
| json.dump(all_results, f, indent=2) | |
| print(f"\n{'='*70}") | |
| print("TOOL MASTERY & AUTONOMY TRAINING COMPLETE") | |
| print(f"{'='*70}\n") | |
| for creature_name in ["Luna", "Nova", "Cipher"]: | |
| creature = Creature(creature_name) | |
| concepts = len(creature.weights["salience"]) | |
| assoc = len(creature.weights["assoc"]) | |
| # Top concepts | |
| top = sorted(creature.weights["salience"].items(), | |
| key=lambda x: x[1], reverse=True)[:10] | |
| print(f"\n{creature_name}:") | |
| print(f" Concepts: {concepts}") | |
| print(f" Associations: {assoc}") | |
| print(f" Top: {[k for k, v in top]}") | |
| print(f"\n{'='*70}") | |
| print("CAPABILITIES:") | |
| print(" - File I/O & data processing") | |
| print(" - API integration & authentication") | |
| print(" - System commands & subprocess") | |
| print(" - Database operations") | |
| print(" - Testing & debugging") | |
| print(" - Concurrency & async") | |
| print(" - Task decomposition & planning") | |
| print(" - Code review & optimization") | |
| print(" - Autonomous problem solving") | |
| print(f"{'='*70}\n") | |
| if __name__ == "__main__": | |
| train_tool_mastery() | |