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jakelipner/
sae-grid-search-layer12

Name: sae-grid-search-layer12
Availability: InStock

sparse-autoencoder

mechanistic-interpretability

qwen

gsm8k

apache-2.0

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SAE Grid Search — Layer 12, instruct_base

Hyperparameter sweep across expansion factors, K values, and SAE architectures (BatchTopK, TopK, JumpReLU) trained on Michael's instruct_base layer 12 activations from OhhMoo/sae-rl-qwen05b-strict-activations.

Key Findings

BatchTopK beats TopK at every configuration on both MSE and dead latents
K=64 at 8x expansion leaves 57% of features dead — K should be at least 128-220
Sweet spot: 4x or 8x expansion, BatchTopK, K=220 — ~90-93% frac_rec, ~13-21% dead latents
JumpReLU achieves near-perfect reconstruction but only by firing on 10-45% of features — not truly sparse at low thresholds
16x expansion adds little over 8x at matched K — not worth the extra compute

Results

Full results including delta loss and frac_rec in results_full.csv. Original MSE/NMSE/dead latent results in results.csv.

BatchTopK (recommended)

Exp	K	Val MSE	NMSE	Dead %	Delta Loss	Frac Rec %
4x	220	0.0228	0.000479	12.9%	0.247	92.9%
8x	220	0.0245	0.000515	20.6%	0.354	90.1%
16x	220	0.0298	0.000627	33.0%	0.406	88.8%
4x	128	0.0340	0.000715	18.8%	0.634	83.5%
8x	128	0.0369	0.000776	31.3%	0.788	80.3%

JumpReLU

Exp	Threshold	L0	Val MSE	NMSE	Dead %	Delta Loss	Frac Rec %
4x	0.5	77	0.0435	0.000915	24.0%	0.813	79.8%
8x	0.5	100	0.0418	0.000880	25.5%	1.001	76.2%
4x	0.1	1596	0.0007	0.000014	8.6%	-0.014	100.4%
8x	0.1	1962	0.0008	0.000017	18.4%	0.043	98.7%

SAE Architecture

python

class BatchTopKSAE(nn.Module):
    def __init__(self, d_in, d_sae, k):
        super().__init__()
        self.k       = k
        self.b_pre   = nn.Parameter(torch.zeros(d_in))
        self.encoder = nn.Linear(d_in, d_sae, bias=True)
        self.decoder = nn.Linear(d_sae, d_in, bias=True)

    def encode(self, x):
        x_centered = x - self.b_pre
        pre_acts   = self.encoder(x_centered)
        n_keep     = int(pre_acts.numel() * self.k / pre_acts.shape[-1])
        threshold  = pre_acts.reshape(-1).topk(n_keep).values.min()
        acts       = pre_acts * (pre_acts >= threshold).float()
        return F.relu(acts)

    def decode(self, z):
        return self.decoder(z) + self.b_pre

    def forward(self, x):
        z     = self.encode(x)
        recon = self.decode(z)
        return recon, z

Loading SAEs

python

from huggingface_hub import hf_hub_download
import torch

path = hf_hub_download(
    repo_id="jakelipner/sae-grid-search-layer12",
    filename="BatchTopK_exp8x_k220.pt",
    repo_type="model"
)

sae = BatchTopKSAE(d_in=896, d_sae=7168, k=220)
sae.load_state_dict(torch.load(path, map_location="cpu"))
sae.eval()

x_hat, z = sae(x)  # x: (N, 896) float32

File naming

{Type}_exp{expansion}x_k{K}.pt for BatchTopK and TopK. JumpReLU_exp{expansion}x_t{threshold}.pt for JumpReLU.

results.csv — MSE, NMSE, dead latents (BatchTopK and TopK only)
results_full.csv — all metrics including delta loss and frac_rec for all three architectures

Training details

Base activations: OhhMoo/sae-rl-qwen05b-strict-activations instruct_base layer 12
Epochs: 10, LR: 1e-4, cosine annealing, batch size 512
Decoder weights normalized to unit norm after each step
Best checkpoint selected by val MSE
Delta loss computed on 50 GSM8K test prompts using Qwen2.5-0.5B-Instruct