Property	Value
Base model	`meta-llama/Llama-3.2-1B-Instruct`
Training variant	B — Baseline control (1000 single-turn examples without self-referential content)
Fine-tuning method	Full fine-tuning (all parameters)
Random seed	123
Training data	SRD v2 curated dataset

Paper

Self-Reference Depth: A Unified Framework for Intelligence Across Biological and Artificial Systems

This model is part of a 36-model experiment (3 variants × 4 architectures × 3 seeds) testing whether fine-tuning can increase a language model's self-referential depth — its capacity for genuine recursive self-evaluation rather than surface-level self-reference.

Repository

Full code, data, and analysis: oyoungforever/SelfReferenceDepth

Training Metadata

json

{
  "run_name": "srd_B_llama-1B_fullft_seed123",
  "model_name": "meta-llama/Llama-3.2-1B-Instruct",
  "model_key": "llama-1B",
  "variant": "B",
  "seed": 123,
  "data_path": "data/variant_b_v2/train.jsonl",
  "data_size": 1000,
  "compute_dtype": "bfloat16",
  "method": "full_finetune",
  "learning_rate": 5e-06,
  "total_params": 1235814400,
  "trainable_params": 1235814400
}

Citation

bibtex

@article{ouyang2026srd,
  title={Self-Reference Depth: A Unified Framework for Intelligence Across Biological and Artificial Systems},
  author={Ouyang, Shumiao},
  year={2026}
}

simonleee/srd_B_llama-1B_fullft_seed123

srd_B_llama-1B_fullft_seed123

Overview

Paper

Repository

Training Metadata

Citation

simonleee/
srd_B_llama-1B_fullft_seed123