README.md

---
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
  - codette
  - llama-3.1
  - lora
  - peft
  - multi-perspective
  - reasoning
  - gguf
language:
  - en
pipeline_tag: text-generation
---

# 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
```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
```python
from codette_orchestrator import CodetteOrchestrator

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

## Related Repos

- [Raiff1982/codette-llama-3.1-8b-gguf](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-gguf) - Quantized base GGUF model
- [Raiff1982/codette-llama-3.1-8b-merged](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-merged) - Full-precision merged model
- [Raiff1982/Codette-Reasoning](https://huggingface.co/Raiff1982/Codette-Reasoning) - Training datasets

## License

Subject to the [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE).