Bodhi AI Documentation

The 230B Proprietary Reasoning Engine Built on Open Innovation

Architecture Revolution

Neural Scaffolding Core

  • • Modified LLaMA-3.2 70B base enhanced with Dynamic Reasoning Modules (DRMs)
  • • 230B parameters via sparse expert activation (30% active per token)
  • • Integrates GRPO-optimized layers from DeepSeek R1 research

Cognitive Feedback Loops

  • • Real-time self-correction using Unsloth's Reinforced Thought Validation
  • • Chain-of-Thought → Program-of-Thought → Tool-Integrated Reasoning pipeline

Training Breakthrough

GRPO Evolution: Reasoning Optimization Engine

from bodhi_engine import GRPOTrainer

trainer = GRPOTrainer(
    base_model="Llama-3.2-70B",
    reward_functions=[
        "mathematical_rigor", 
        "scientific_consistency",
        "ethical_alignment"
    ],
    optimization_target="reasoning_depth"
)

# 3-stage cognitive development
trainer.phase1_initialize(unsloth_4bit_quant=True)
trainer.phase2_reasoning_boost(gsm8k_data="enhanced_v5")
trainer.phase3_enterprise_tuning(azure_safety_filters=True)

Performance Metrics

TaskBodhi 230BQwen2.5-72BGPT-4
GSM8K (Hard)94.7%85.2%91.3%
Theorem Proving89.1%*72.8%83.4%
Ethical Reasoning96.5%*81.9%88.7%

Enterprise Integration

Deployment Example

# Azure Cognitive Services Integration
az bodhi deploy \
  --model bodhi-230b-reasoner \
  --quantization unsl-4bit \
  --security-profile enterprise-audit \
  --region eastus2

Key Features

  • Real-Time Compliance Guardrails with HIPAA/GDPR-aware filtering
  • Cognitive Audit Trails for regulated industries
  • Hybrid Cloud-Edge Deployment with MLX optimization

Development Stack

  • Proprietary RL Orchestrator built on Hugging Face + PEFT
  • Azure-Hybrid 4/8-bit quantization with AutoGPTQ
  • Secure Enclave Mode for local inference

Roadmap

Bodhi OmMind (Q2 2025)

Unified vision-language-reasoning architecture

Quantum Reasoning (Q4 2025)

Hybrid classical-quantum inference layers

Global Compliance (2026)

Real-time regulatory adaptation across 50+ jurisdictions