# Narrow AI: Experimental Model Repository

This repository contains experimental model checkpoints and data from the paper **"On the creation of narrow AI: hierarchy and nonlocality of neural network skills"** by Eric Michaud, Asher Parker-Sartori, and Max Tegmark.

## Repository Contents

This dataset provides the hard-to-reproduce LLM experimental artifacts that support the paper's key figures, particularly training curves and model performance data for scaling analysis and pruning studies.

### Experiments Included

#### 1. **trainscratch01/** - LLMs Trained from Scratch
- **Purpose**: Training small to medium LLMs from scratch for scaling analysis
- **Models**: 9 architectures ranging from 23M to 1.6B parameters
- **Architecture format**: `d{hidden_size}_l{num_layers}_h{num_heads}`
- **Key models included**:
  - `d768_l12_h12/` - 338M parameters (representative medium model)
  - `d2048_l32_h32/` - 1.6B parameters (large model for scaling)
- **Training**: 100K steps on GitHub code dataset
- **Paper figures**: Figure 6, Figure 12

#### 2. **pruneandtrain01/** - Attribution-Based Pruning
- **Purpose**: Pruning LLaMA-3.2-1B using gradient attribution, then recovery training
- **Base model**: NousResearch/Llama-3.2-1B
- **Configurations**: Various neuron and residual sparsity levels
- **Key configurations included**:
  - `n0.50_r0.50/` - 50% neuron, 50% residual pruning (moderate)
  - `n0.90_r0.50/` - 90% neuron, 50% residual pruning (aggressive)
- **Unique files**:
  - `pruning_mask.pt` - Binary masks indicating pruned neurons
  - `pruning_stats.json` - Detailed attribution scores and pruning decisions
  - `experiment_metadata.json` - Sparsity levels and run metadata
- **Paper figures**: Figure 6, Figure 12, Figure 13

#### 3. **pruneandtrainrandom00/** - Random Pruning Baseline
- **Purpose**: Random pruning comparison for attribution-based methods
- **Configuration**: `n0.50_r0.20/` for direct comparison with attribution methods
- **Paper figures**: Figure 13

#### 4. **distillscratch00/** - Knowledge Distillation (Selected)
- **Purpose**: Training small models via knowledge distillation
- **Teacher models**: Meta-Llama-3.1-8B, Llama-3.2-3B
- **Student**: `d768_l12_h12/` architecture for comparison
- **Paper figures**: Figure 6, Figure 12

#### 5. **tuneprune15-redo/** - Group-Sparsity Regularized Training
- **Purpose**: Training Llama-3.2-1B on Python code with group-sparsity penalty to induce structured sparsity
- **Base model**: NousResearch/Llama-3.2-1B (1.2B parameters)
- **Method**: L1 norm of L2 norm of MLP neuron parameters (encourages entire neurons to become zero)
- **Dataset**: `codeparrot/github-code` (Python subset)
- **Training**: 70,000 steps with various regularization strengths
- **Configurations**:
  - `lambda_0.0003_bs_18_acc_6/` - Light regularization (λ=0.0003)
  - `lambda_0.0005_bs_18_acc_6/` - Moderate regularization (λ=0.0005)
  - `lambda_0.001_bs_18_acc_6/` - Strong regularization (λ=0.001)
- **Unique files**:
  - `experiment_metadata.json` - Complete training setup and regularization details
  - `trainer_state.json` - Full training curves including data loss and regularization loss
- **Training script**: Located in `$HOME/narrow/experiments/tuneprune15-redo`
- **Key feature**: Subdistribution training (Python only) with explicit sparsity induction

## Model Architecture Details

### Parameter Scaling
| Model | Hidden Size | Layers | Heads | Intermediate | Parameters |
|-------|-------------|--------|-------|--------------|------------|
| d256_l4_h4 | 256 | 4 | 4 | 1024 | ~23M |
| d512_l8_h8 | 512 | 8 | 8 | 2048 | ~92M |
| d768_l12_h12 | 768 | 12 | 12 | 3072 | ~338M |
| d2048_l32_h32 | 2048 | 32 | 32 | 8192 | ~1.6B |

### Pruning Configurations
| Config | Neuron Sparsity | Residual Sparsity | Description |
|--------|-----------------|-------------------|-------------|
| n0.50_r0.50 | 50% | 50% | Moderate pruning |
| n0.90_r0.50 | 90% | 50% | Aggressive neuron pruning |
| n0.50_r0.20 | 50% | 20% | Light residual pruning |

## File Structure

Each model directory contains:

### Standard Checkpoints
- `final_model/` - Final trained model
- `checkpoint-{step}/` - Intermediate checkpoints (every 5K steps)
- `model_stats.json` - Parameter counts and architecture info

### Files per Checkpoint
- `model.safetensors` - Model weights in SafeTensors format
- `config.json` - Model configuration
- `tokenizer.json` - Tokenizer configuration
- `trainer_state.json` - Training history and loss curves
- `training_args.bin` - Training arguments

### Pruning-Specific Files
- `pruning_mask.pt` - Binary masks for pruned parameters (~5GB)
- `pruning_stats.json` - Attribution scores and pruning decisions (~8MB)
- `experiment_metadata.json` - Run metadata and sparsity settings

## Usage Examples

### Loading a Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load a trained-from-scratch model
model = AutoModelForCausalLM.from_pretrained("ericjm/narrow-data/trainscratch01/d768_l12_h12/final_model")
tokenizer = AutoTokenizer.from_pretrained("ericjm/narrow-data/trainscratch01/d768_l12_h12/final_model")
```

### Loading Pruning Data
```python
import torch
import json

# Load pruning mask and statistics
config = "n0.50_r0.50"
mask = torch.load(f"ericjm/narrow-data/pruneandtrain01/{config}/pruning_mask.pt")
with open(f"ericjm/narrow-data/pruneandtrain01/{config}/pruning_stats.json") as f:
    stats = json.load(f)
```

### Analyzing Training Curves
```python
import json

# Load training history
with open("ericjm/narrow-data/trainscratch01/d768_l12_h12/final_model/trainer_state.json") as f:
    trainer_state = json.load(f)

training_loss = [entry['train_loss'] for entry in trainer_state['log_history'] if 'train_loss' in entry]
```

### Loading Group-Sparsity Models (tuneprune15-redo)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import json

# Load a model trained with group-sparsity regularization
lambda_config = "lambda_0.0005_bs_18_acc_6"
model_path = f"ericjm/narrow-data/tuneprune15-redo/{lambda_config}/checkpoint-70000"
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Load experiment metadata
with open("ericjm/narrow-data/tuneprune15-redo/experiment_metadata.json") as f:
    metadata = json.load(f)

# Analyze training curves including regularization loss
with open(f"{model_path}/trainer_state.json") as f:
    trainer_state = json.load(f)
    data_loss = [x['data_loss'] for x in trainer_state['log_history'] if 'data_loss' in x]
    reg_loss = [x['reg_loss'] for x in trainer_state['log_history'] if 'reg_loss' in x]
```

## Reproducing Paper Figures

### Figure 6 & 12: LLM Training Frontiers
- **Data**: Training curves from `trainscratch01/`, `distillscratch00/`, `pruneandtrain01/`
- **Analysis**: Compare training efficiency and final performance across methods
- **Notebook**: See paper repository for analysis code

### Figure 13: Attribution vs Random Pruning
- **Data**: Recovery curves from `pruneandtrain01/` vs `pruneandtrainrandom00/`
- **Key comparison**: `n0.50_r0.20` configuration in both experiments

## Technical Details

### Training Setup
- **Dataset**: `codeparrot/github-code` (Python subset)
- **Sequence length**: 1024 tokens
- **Tokenizer**: Meta-Llama-3.1-8B tokenizer
- **Training steps**: 100K for scratch training, 20K for pruning recovery
- **Learning rate**: 5e-4 (scratch), 5e-5 (pruning recovery)

### Pruning Method
- **Attribution**: Gradient-based neuron importance scoring
- **Sparsity**: Separate control of neuron and residual stream dimensions
- **Recovery**: Fine-tuning with masked gradients to recover performance

### Computational Requirements
- **Training**: NVIDIA A100 80GB
- **Storage**: ~50GB for essential models, ~1TB for complete archive
- **Memory**: Models range from 23M to 1.6B parameters

## Citation

If you use this data in your research, please cite:

```bibtex
@article{michaud2024narrow,
  title={On the creation of narrow AI: hierarchy and nonlocality of neural network skills},
  author={Michaud, Eric and Parker-Sartori, Asher and Tegmark, Max},
  journal={arXiv preprint},
  year={2024}
}
```

## License

This dataset is released under the same license as the paper. Please see the paper repository for detailed licensing information.

## Contact

For questions about this dataset, please contact Eric Michaud or open an issue in the paper's repository.