Step 1: Download BeastBullet Package

# Clone from Hugging Face
git clone https://huggingface.co/SetMD/beastbullet-experts
cd beastbullet-experts

# Verify download (288 MB total)
du -sh .
# Should show: 288M

What you get:

• ✅ 18 specialized expert models (~270 MB)
• ✅ Complete reasoning pipeline code
• ✅ Training infrastructure
• ✅ Architecture documentation

Step 2: Install Dependencies (Lightweight)

# Install Python dependencies
pip install torch transformers huggingface_hub

# Install TinyLlama via Ollama (recommended)
curl -fsSL https://ollama.com/install.sh | sh
ollama pull tinyllama

# OR use Hugging Face (auto-downloads)
# No additional action needed

System Requirements:

• CPU: 4+ cores (no GPU needed!)
• RAM: 4 GB minimum
• Storage: 500 MB
• OS: Linux, macOS, Windows

Step 3: Load Sonnet-Quality Configuration

from code.beastbullet_config import BeastBulletConfig, create_high_quality_system
import json

# Load Sonnet-quality settings
with open("configs/config_sonnet_quality.json") as f:
    config = json.load(f)

# Create system with high-quality config
grok, config_obj = create_high_quality_system()

print("✅ BeastBullet loaded with Sonnet-quality settings")

Key Settings:

• Confidence threshold: 0.85 (high quality)
• Chain-of-thought: Enabled
• Validator: Strict mode (0% false positives)
• Evidence sources: Minimum 2

Step 4: Integrate with TinyLlama

from code.integration_example import TinyLlamaIntegration, ExpertLoader

# Load all 18 experts
loader = ExpertLoader("experts/")
loader.load_encoder()
loader.load_all_experts()

# Create TinyLlama integration
integration = TinyLlamaIntegration(
    experts=loader.experts,
    encoder=loader.encoder
)

print("✅ TinyLlama integration ready")

Step 5: Query with Quality Assurance

from code.beastbullet_config import query_with_quality_assurance

# Example query
query = "What is 15% of 240? Explain your reasoning."

# Query with full quality assurance
result = query_with_quality_assurance(grok, query, config)

# Display results
print(f"Answer: {result['answer']}")
print(f"Confidence: {result['confidence']:.2%}")
print(f"Quality Score: {result['quality_score']:.2%}")
print(f"Experts Used: {', '.join(result['experts_used'])}")
print(f"\nReasoning Trace:")
for step in result['reasoning_trace']:
    print(f"  - {step}")

Expected Output:

Answer: 36. Here's the calculation: 15% = 0.15, and 0.15 × 240 = 36.
Confidence: 95%
Quality Score: 92%
Experts Used: math_expert, validator_expert

Reasoning Trace:
  - Convert percentage to decimal: 15% = 0.15
  - Multiply: 0.15 × 240 = 36
  - Verified by validator expert

Step 6: Optimize for Your Hardware

# Edit config for your CPU
config["performance"]["num_threads"] = 4  # Your CPU cores
config["performance"]["max_memory_mb"] = 4096  # Your RAM
config["performance"]["enable_onnx_optimization"] = True

# Enable aggressive caching
config["isl_router"]["cache_size"] = 20000

# Save custom config
with open("my_config.json", "w") as f:
    json.dump(config, f, indent=2)

Performance Tips:

• More CPU cores = faster expert execution
• Larger cache = better response time
• ONNX optimization = 2-3× speedup

Step 7: Run Benchmark

# Test on various queries
test_queries = [
    "Calculate 25% of 500",
    "If A implies B, and B implies C, what can we conclude?",
    "Write a Python function to reverse a string",
]

results = []
for query in test_queries:
    result = query_with_quality_assurance(grok, query, config)
    results.append(result)
    print(f"Query: {query}")
    print(f"Confidence: {result['confidence']:.2%}\n")

# Calculate average quality
avg_quality = sum(r['quality_score'] for r in results) / len(results)
print(f"Average Quality Score: {avg_quality:.2%}")