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: 10,514 Bytes
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"""
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()
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