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jacobcd52/
ss_bridges_d1024_f0.015625_resid

Name: ss_bridges_d1024_f0.015625_resid
Availability: InStock

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ss_bridges_d1024_f0.015625_resid

Weight-sparse transformer with bridges, trained with the procedure from Gao et al. (2025).

Model Details (Sparse Model)

Layers: 2
Model Dimension: 1024
Context Length: 512
Head Dimension: 16
Vocabulary Size: 4096

Bridges

Dense Model: jacobcd52/ss_d128_f1
Encoder Activation Fraction: 0.25

Sparsity

Weight Sparsity: True
Target L0 Fraction: 0.015625
Activation Sparsity: True

Training

Dataset: data/simplestories-tokenized
Tokenizer: SimpleStories/SimpleStories-1.25M
Total Tokens: 2,000,000,000

Training Run

W&B Run: https://wandb.ai/training-saes/bridges_training/runs/0g7bw4i5

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download model and bridges
sparse_model_path = hf_hub_download(repo_id="jacobcd52/ss_bridges_d1024_f0.015625_resid", filename="sparse_model.bin")
bridges_path = hf_hub_download(repo_id="jacobcd52/ss_bridges_d1024_f0.015625_resid", filename="bridges.bin")
config_path = hf_hub_download(repo_id="jacobcd52/ss_bridges_d1024_f0.015625_resid", filename="config.json")

# Load (requires the SparseGPT and BridgeSet classes from this repo)
sparse_state_dict = torch.load(sparse_model_path)
bridges_state_dict = torch.load(bridges_path)