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SKwra/
toolcalling-sae

Name: toolcalling-sae
Availability: InStock

sparse-autoencoder

mechanistic-interpretability

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toolcalling-sae

TopK Sparse Autoencoder checkpoints from To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents.

Checkpoints

Model	Layer	Dict Size	k	Stage 1	Stage 2
gemma-3-1b-it	L17	9 216	128	50M tokens	5M tokens
gemma-3-4b-it	L29	20 480	128	50M tokens	5M tokens
gemma-4-E2B-it	L30	12 288	128	50M tokens	5M tokens
gemma-4-E4B-it	L30	20 480	128	50M tokens	5M tokens
Ministral-3-3B-Instruct-2512	L21	24 576	128	50M tokens	5M tokens
Ministral-3-8B-Instruct-2512	L31	32 768	128	50M tokens	5M tokens
Qwen3.5-4B	L25	20 480	128	50M tokens	5M tokens
Qwen3.5-9B	L25	32 768	128	50M tokens	5M tokens

Stage 1: Pre-trained on OpenWebText2.
Stage 2: Fine-tuned on tool-calling activations from the When2Call benchmark.
All checkpoints use bfloat16 precision.

Usage

python

from huggingface_hub import hf_hub_download
from sae_model import TopKSAE

ckpt_path = hf_hub_download(
    repo_id="SKwra/toolcalling-sae",
    filename="gemma-3-1b-it/stage2/gemma-3-1b-it-L17-d9216-5M-stage2.pt"
)
sae = TopKSAE.load(ckpt_path, device="cuda")

sae_model.py is included in this repo. Full code at GitHub.