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Model: Raiff1982/codette-llama-3.1-8b-merged Source: Original Platform
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README.md
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README.md
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---
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- codette
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- llama-3.1
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- merged
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- multi-perspective
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- reasoning
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- orchestrator
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language:
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- en
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pipeline_tag: text-generation
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---
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# Codette Llama 3.1 8B — Merged Orchestrator Base
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Llama 3.1 8B Instruct with the **Codette Orchestrator LoRA** permanently merged into the base weights. This is the inference base for the Codette multi-perspective reasoning system — pair it with the [perspective LoRA adapters](https://huggingface.co/Raiff1982/codette-lora-adapters) for full multi-agent synthesis.
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**Paper:** [Codette: Multi-Perspective Reasoning as a Convergent Dynamical System](https://doi.org/10.21203/rs.3.rs-9362560/v1)
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**GitHub:** [Raiff1982/Codette-Reasoning](https://github.com/Raiff1982/Codette-Reasoning)
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**ORCID:** [0009-0003-7005-8187](https://orcid.org/0009-0003-7005-8187)
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---
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## Benchmark Results (May 2026)
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17-problem benchmark across 6 cognitive categories, 4-condition ablation:
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| Condition | Composite Score | vs. Baseline |
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|-----------|----------------|--------------|
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| SINGLE (baseline) | 0.357 | — |
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| MULTI (6 perspectives) | 0.521 | +46.1% |
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| MEMORY (+ cocoon store) | 0.574 | +60.8% |
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| **CODETTE (full system)** | **0.744** | **+108.8%** |
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- Cohen's *d* = 8.31 (large effect; *d* > 0.8 is large by convention)
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- Paired *t*-test: *p* < 0.0001
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- Turing naturalness: 0.245 → 0.820 (+235%) — depth–naturalness tradeoff resolved
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- Coherence: 0.477 → 0.700
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**GPQA (graduate-level science, 0-shot, 198-question diamond set):**
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| Run | Accuracy | Environment |
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|-----|----------|-------------|
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| Base model + adapters (Kaggle cloud, June 17 2026) | **27.8%** (55/198) | Direct transformers+PEFT, no orchestration |
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| Full Codette system (local server, June 6 2026) | **30.8%** (61/198) | Multi-agent debate + coherence tracking + cocoon memory |
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Baselines: random 25%, GPT-4 0-shot 39%, human expert 65%.
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The ~3pp gap between runs quantifies the system layer's contribution on GPQA specifically.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base Model | meta-llama/Llama-3.1-8B-Instruct |
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| Merged Adapter | Orchestrator LoRA |
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| Format | SafeTensors (full precision, ~16 GB) |
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| Context Length | 8192 tokens |
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| Quantized version | [codette-llama-3.1-8b-gguf](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-gguf) |
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---
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## System Architecture
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```
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Query
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│
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▼
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Executive Controller (complexity routing)
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│
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▼
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Merged Orchestrator Base ◄── this repo
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│
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▼
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LoRA Hot-Swap (newton / davinci / empathy / philosophy /
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quantum / consciousness / multi_perspective /
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systems_architecture)
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│
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▼
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Multi-Agent Debate + Semantic Tension (RC+ξ)
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│
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▼
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AEGIS Ethical Governance (6 frameworks)
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│
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▼
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Synthesized Response + Cocoon Memory
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```
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The RC+ξ (Recursive Convergence + Epistemic Tension) formalism models cognitive state evolution as a convergent dynamical system:
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```
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Ψ(t+1) = Ψ(t) + α·∇Coherence(Ψ(t)) − β·ξ(t)·∇Tension(Ψ(t))
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```
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---
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## Quick Start
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### With Transformers (full precision)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("Raiff1982/codette-llama-3.1-8b-merged")
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tokenizer = AutoTokenizer.from_pretrained("Raiff1982/codette-llama-3.1-8b-merged")
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inputs = tokenizer("Explain the nature of consciousness", return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### With 4-bit quantization (recommended for 8–16 GB VRAM)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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import torch
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bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16)
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model = AutoModelForCausalLM.from_pretrained(
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"Raiff1982/codette-llama-3.1-8b-merged",
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quantization_config=bnb, device_map="auto"
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)
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```
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### With perspective adapters (multi-agent mode)
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```python
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from peft import PeftModel
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# Apply a perspective adapter on top of the base
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model = PeftModel.from_pretrained(model, "Raiff1982/codette-lora-adapters",
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subfolder="newton_v2")
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```
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### Full local server
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```bash
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git clone https://github.com/Raiff1982/Codette-Reasoning
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cd Codette-Reasoning
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python inference/codette_server.py # serves on :7860
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```
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---
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## Related Resources
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| Resource | Link |
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|----------|------|
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| Perspective LoRA adapters | [codette-lora-adapters](https://huggingface.co/Raiff1982/codette-lora-adapters) |
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| Quantized GGUF | [codette-llama-3.1-8b-gguf](https://huggingface.co/Raiff1982/codette-llama-3.1-8b-gguf) |
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| Training datasets | [codette-training-data](https://huggingface.co/datasets/Raiff1982/codette-training-data) |
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| GitHub | [Raiff1982/Codette-Reasoning](https://github.com/Raiff1982/Codette-Reasoning) |
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| Paper (preprint) | [Research Square DOI](https://doi.org/10.21203/rs.3.rs-9362560/v1) |
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| Zenodo archive | [10.5281/zenodo.19480004](https://doi.org/10.5281/zenodo.19480004) |
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| Kaggle AGI benchmark | [RC+ Diagnostic Suite](https://kaggle.com/competitions/kaggle-measuring-agi/writeups/codette-rc-diagnostic-suite) |
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---
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## License
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Subject to the [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE).
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Created by Jonathan Harrison (Raiff's Bits LLC) — independent research.
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