---
title: "Codette LoRA Adapters"
authors:
  - name: Jonathan Harrison
    orcid: 0009-0003-7005-8187
    affiliation: "Raiff's Bits LLC, Bridge City, Texas, USA"
tags:
  - lora
  - peft
  - llama
  - cognitive-architecture
  - multi-agent
  - ethical-ai
  - recursive-convergence
  - qlora
license: cc-by-4.0
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: peft
pipeline_tag: text-generation
---

# Codette LoRA Adapters

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18913936.svg)](https://doi.org/10.5281/zenodo.18913936)

**8 domain-specialized LoRA adapters** for the [Codette cognitive architecture](https://huggingface.co/Raiff1982/codette-paper) — a sovereign modular AI framework for ethical multi-agent reasoning.

**Author:** Jonathan Harrison · [ORCID](https://orcid.org/0009-0003-7005-8187) · Raiff's Bits LLC

---

## Base Model

**meta-llama/Llama-3.1-8B-Instruct** with QLoRA (4-bit quantization)

## Adapter Configuration

| Parameter | Value |
|-----------|-------|
| PEFT Type | LoRA |
| Rank (r) | 16 |
| Alpha | 32 |
| Dropout | 0.05 |
| Target Modules | `q_proj`, `k_proj`, `v_proj`, `o_proj` |
| Bias | none |
| Task Type | CAUSAL_LM |
| Quantization | 4-bit (QLoRA) |

## Adapters

Each adapter specializes in a distinct cognitive perspective, trained on curated perspective-tagged datasets:

| Adapter | Description | Training Examples | Status |
|---------|-------------|-------------------|--------|
| `newton/` | Analytical physics reasoning — Newtonian precision and scientific method | 3,000 | ✅ Uploaded |
| `davinci/` | Creative invention thinking — DaVinci's cross-disciplinary creativity | 2,500 | ✅ Uploaded |
| `empathy/` | Emotional understanding and compassionate reasoning | 2,500 | ✅ Uploaded |
| `philosophy/` | Conceptual and philosophical reasoning — depth and rigor | 2,000 | ✅ Uploaded |
| `quantum/` | Probabilistic and quantum-inspired reasoning | 2,000 | ✅ Uploaded  |
| `consciousness/` | Recursive cognition and RC+ξ framework reasoning | 3,000 | ✅ Uploaded  |
| `multi_perspective/` | Multi-perspective synthesis across analytical lenses | 2,500 | ✅ Uploaded  |
| `systems_architecture/` | AI systems architecture and design reasoning | 2,000 | 🔄 Training |

**Total: 20,500 training examples across 8 cognitive domains**

## Training Details

- **Epochs**: 3 per adapter
- **Hardware**: NVIDIA A10G (cloud) + Intel Arc 140V / CPU (local)
- **Framework**: Hugging Face TRL (SFTTrainer) + PEFT
- **Training Pipeline**: [`Raiff1982/codette-training-lab`](https://huggingface.co/Raiff1982/codette-training-lab)
- **Novel contribution**: Two GPU-free CPU training pipelines validated on consumer laptops (see paper)

### Training Metrics (Newton adapter example)

| Metric | Value |
|--------|-------|
| Final Loss | ~0.071 |
| Mean Token Accuracy | 97.4% |
| Gradient Norm | ~0.05–0.13 |

## Usage

### Load a single adapter

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-3.1-8B-Instruct",
    load_in_4bit=True,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")

# Load the newton adapter
model = PeftModel.from_pretrained(base_model, "Raiff1982/codette-lora-adapters", subfolder="newton")
```

### Load multiple adapters (multi-perspective reasoning)

```python
from peft import PeftModel

# Load base
model = PeftModel.from_pretrained(base_model, "Raiff1982/codette-lora-adapters", subfolder="newton", adapter_name="newton")

# Add additional perspectives
model.load_adapter("Raiff1982/codette-lora-adapters", subfolder="empathy", adapter_name="empathy")
model.load_adapter("Raiff1982/codette-lora-adapters", subfolder="davinci", adapter_name="davinci")

# Switch between perspectives
model.set_adapter("empathy")
```

## How Adapters Fit in the Codette Architecture

```
┌─────────────────────────────────────────────────────┐
│  Codette Orchestrator                               │
├─────────────────────────────────────────────────────┤
│  Reasoning Forge (6 agents + Critic + Synthesis)    │
│    ┌─────────┐ ┌─────────┐ ┌─────────┐            │
│    │ Newton  │ │ DaVinci │ │ Empathy │  ...        │  ← LoRA adapters
│    └────┬────┘ └────┬────┘ └────┬────┘            │
│         └───────────┼───────────┘                  │
│                     ▼                              │
│         RC+ξ Attractor Convergence                 │
│         Phase Coherence Γ → 0.99                   │
├─────────────────────────────────────────────────────┤
│  AEGIS Ethical Governance (η = 0.961)              │
├─────────────────────────────────────────────────────┤
│  QuantumSpiderweb · CognitionCocooner · Memory     │
└─────────────────────────────────────────────────────┘
```

Each adapter represents a specialized cognitive perspective. The Reasoning Forge orchestrates them through shared attractor dynamics, achieving multi-agent phase coherence (Γ = 0.99) within 10 recursive iterations.

## Directory Structure

```
codette-lora-adapters/
├── newton/
│   ├── adapter_config.json
│   ├── adapter_model.safetensors
│   ├── tokenizer.json
│   ├── tokenizer_config.json
│   ├── chat_template.jinja
│   ├── checkpoint-500/
│   └── checkpoint-1125/
├── davinci/
│   ├── adapter_config.json
│   ├── adapter_model.safetensors
│   ├── ...
│   ├── checkpoint-500/
│   └── checkpoint-939/
├── empathy/
│   ├── adapter_config.json
│   ├── adapter_model.safetensors
│   ├── ...
│   ├── checkpoint-500/
│   └── checkpoint-939/
├── philosophy/          (coming soon)
├── quantum/             (coming soon)
├── consciousness/       (coming soon)
├── multi_perspective/   (coming soon)
└── systems_architecture/ (coming soon)
```

## Related Resources

| Resource | Link |
|----------|------|
| Paper | [Raiff1982/codette-paper](https://huggingface.co/Raiff1982/codette-paper) |
| Training Lab | [Raiff1982/codette-training-lab](https://huggingface.co/Raiff1982/codette-training-lab) |
| Training Data | [Raiff1982/codette-training-data](https://huggingface.co/datasets/Raiff1982/codette-training-data) |
| Zenodo DOI | [10.5281/zenodo.18913936](https://doi.org/10.5281/zenodo.18913936) |
| GitHub | [Raiff1982/codette-training-lab](https://github.com/Raiff1982/codette-training-lab) |
| ORCID | [0009-0003-7005-8187](https://orcid.org/0009-0003-7005-8187) |

## Citation

```bibtex
@article{harrison2026codette,
  title={Codette: A Sovereign Modular Cognitive Architecture for Ethical Multi-Agent AI},
  author={Harrison, Jonathan},
  year={2026},
  doi={10.5281/zenodo.18913936},
  publisher={Raiff's Bits LLC},
  url={https://huggingface.co/Raiff1982/codette-paper}
}
```

## License

CC BY 4.0 — [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)