Instructions to use persona-shattering-lasr/20Feb-n-plus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use persona-shattering-lasr/20Feb-n-plus with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("persona-shattering-lasr/20Feb-n-plus", dtype="auto") - Notebooks
- Google Colab
- Kaggle
20Feb n+ (neuroticism-increasing) LoRA adapters
Neuroticism-increasing LoRA adapters fine-tuned on Llama-3.1-8B-Instruct. Four independent reruns with different random seeds to verify training independence.
Checkpoints
| Folder | Seed | Global ||dW||_F |
|---|---|---|
| checkpoints/final/ | 42 (original) | 4.133 |
| rerun-seed123/ | 123 | 4.148 |
| rerun-seed456/ | 456 | 4.146 |
| rerun-seed789/ | 789 | 4.156 |
All 4 runs use identical training data and hyperparameters, only the random seed differs.
Training details
- Base model: meta-llama/Llama-3.1-8B-Instruct
- LoRA rank: 4, alpha=8
- Training data: 450 examples (50 held out for validation)
- Epochs: 3
- Learning rate: 2e-4
- Batch size: 4
- Max sequence length: 1024
Independence verification
All 6 pairwise comparisons between the 4 seeds show consistent independence:
| Metric | n+ vs n+ (mean across 6 pairs) | n+ vs n- (mean across 4 pairs) |
|---|---|---|
| NFD | ~1.33 | ~1.33 |
| Cosine Similarity | ~0.12 | ~0.03 |
| Principal Angles | ~56° | ~79° |
All adapters are genuinely independent solutions (near-orthogonal, similar magnitude).
See experiments/compare_checkpoints.py in the project repo for full analysis.
Model tree for persona-shattering-lasr/20Feb-n-plus
Base model
meta-llama/Llama-3.1-8B