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jacobcd52/
ss_d256_f1

Name: ss_d256_f1
Availability: InStock

pytorch

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ss_d256_f1

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

Model Details

Layers: 4
Model Dimension: 256
Context Length: 512
Head Dimension: 16
Vocabulary Size: 4096

Sparsity

Weight Sparsity: False
Target L0 Fraction: 1
Activation Sparsity: False

Training

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

Training Run

W&B Run: https://wandb.ai/training-saes/my_sparsity/runs/l0bvjtlw

Usage

python

import torch
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(repo_id="jacobcd52/ss_d256_f1", filename="pytorch_model.bin")
config_path = hf_hub_download(repo_id="jacobcd52/ss_d256_f1", filename="config.json")

# Load (requires the SparseGPT model class from this repo)
state_dict = torch.load(model_path)