simplestories-bilinear-attn

SimpleStories Bilinear Attention Models

Collection of ToyTransformer models with CausalBilinearSelfAttention trained on SimpleStories dataset.

Models

Architecture

These models use CausalBilinearSelfAttention which computes squared attention patterns:

• Two QK pairs: (Q1, K1) and (Q2, K2)
• Attention scores: (Q1 @ K1.T) * (Q2 @ K2.T) (element-wise product)
• Causal masking applied
• RoPE (Rotary Positional Embeddings)

Training Details

• Dataset: SimpleStories (4096 vocab)
• d_model: 256
• n_head: 4
• n_ctx: 512
• Optimizer: Muon
• Batches: 100,000
• Batch size: 128

Usage

from huggingface_hub import hf_hub_download
import torch
import json

# Download a model
model_name = "attention_only_1L_bilinear_attn"
config_path = hf_hub_download(repo_id="Elriggs/simplestories-bilinear-attn", filename=f"{model_name}/config.json")
weights_path = hf_hub_download(repo_id="Elriggs/simplestories-bilinear-attn", filename=f"{model_name}/pytorch_model.bin")

# Load config
with open(config_path) as f:
    config_dict = json.load(f)

# Use with ToyTransformer from this repo
from utils import ToyTransformer, ModelConfig
for key in ['num_batches', 'final_train_loss', 'final_val_loss', 'learning_rate', 'batch_size']:
    config_dict.pop(key, None)
config = ModelConfig(**config_dict)
model = ToyTransformer(config)
model.load_state_dict(torch.load(weights_path, map_location='cpu'), strict=False)

Repository

Source code: toy_models_of_tensor_networks