Downloads · 30 days
0
toolcalling-sae
SKwra/toolcalling-sae
toolcalling-sae is a machine learning model from SKwra. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
TopK Sparse Autoencoder checkpoints from To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents.
Access
Public
Updated May 21, 2026
Repo size
4.2 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt4.2 GB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- May 21, 2026
- Updated
- May 21, 2026
- SHA
- 67415914
Model card
From the Hugging Face model README
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
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.
- License
- apache-2.0
- Created
- May 21, 2026
- Updated
- May 21, 2026