Codette LoRA Adapters - 9 Perspective Lenses

9 specialized LoRA adapters for the Codette Multi-Perspective Reasoning System, trained on Llama 3.1 8B Instruct.

These adapters enable instant perspective-switching via hot-swap at inference time. Each adapter specializes in a distinct cognitive reasoning style.

Adapters

Adapter	Description	Examples	Epochs	GGUF File
newton	Analytical physics, systematic reasoning, empirical evidence	3000	3	newton-lora-f16.gguf
davinci	Creative invention, cross-domain connections, visual thinking	2500	3	davinci-lora-f16.gguf
empathy	Emotional intelligence, human experience, compassion	2500	3	empathy-lora-f16.gguf
philosophy	Conceptual analysis, ethical reasoning, fundamental questions	2000	3	philosophy-lora-f16.gguf
quantum	Probabilistic thinking, superposition, complementarity	2000	3	quantum-lora-f16.gguf
consciousness	Recursive cognition (RC+xi), meta-cognition, epistemic tension	3000	3	consciousness-lora-f16.gguf
multi_perspective	Cross-lens synthesis, integrative reasoning	2500	3	multi_perspective-lora-f16.gguf
systems_architecture	Modularity, scalability, engineering principles	2000	3	systems_architecture-lora-f16.gguf
orchestrator	Query routing, multi-agent debate, coherence monitoring	4000	4	orchestrator-lora-f16.gguf

Training Configuration

Parameter	Value
Base Model	meta-llama/Llama-3.1-8B-Instruct
Method	QLoRA (4-bit NF4 + double quantization)
LoRA Rank	16
LoRA Alpha	32
Dropout	0.05
Target Modules	q_proj, k_proj, v_proj, o_proj
Learning Rate	2e-4
Max Sequence Length	2048
Batch Size	2 (effective 8 with grad accumulation)
GPU	NVIDIA A10G (24GB)

Phase 6+ Framework

All adapters are trained with awareness of the Codette Phase 6+ framework:

Semantic Tension Engine: Epistemic tension (xi) measurement between perspectives
Coherence Field (Gamma): Monitors reasoning health, detects collapse patterns
Quantum Spiderweb: Belief propagation network across adapter perspectives
AEGIS Ethical Governance: 6-framework ethical validation layer
Specialization Tracking: Domain expertise tracking per adapter
Pre-flight Prediction: Conflict prediction before multi-agent debate

File Structure

codette-lora-adapters/
  newton-lora-f16.gguf          # 27 MB each
  davinci-lora-f16.gguf
  empathy-lora-f16.gguf
  philosophy-lora-f16.gguf
  quantum-lora-f16.gguf
  consciousness-lora-f16.gguf
  multi_perspective-lora-f16.gguf
  systems_architecture-lora-f16.gguf
  orchestrator-lora-f16.gguf
  newton/                       # SafeTensors format (each ~27 MB)
  davinci/
  ...etc

Usage

Hot-Swap with llama-cpp-python

from llama_cpp import Llama

# Load base model
llm = Llama(model_path="codette-orchestrator-Q4_K_M.gguf", n_ctx=4096, n_gpu_layers=35)

# Apply a LoRA adapter
llm.load_lora("newton-lora-f16.gguf")

response = llm.create_chat_completion(
    messages=[{"role": "user", "content": "Explain gravity"}],
    max_tokens=512,
)

With Codette Orchestrator

from codette_orchestrator import CodetteOrchestrator

orch = CodetteOrchestrator()
result = orch.generate("What is consciousness?", adapters=["consciousness", "philosophy"])