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jacobian-lens
neuronpedia/jacobian-lens
jacobian-lens is a machine learning model from neuronpedia. 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.
This folder contains pre-fitted Jacobian Lenses and J++ Lenses for specific models.
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Updated Oct 15, 2026
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At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Jun 17, 2026
- Updated
- Oct 15, 2026
- SHA
- e166deb5
Model card
From the Hugging Face model README
This folder contains pre-fitted Jacobian Lenses and J++ Lenses for specific models.
Jacobian Lens and J++ Lens can be explored on Neuronpedia at https://neuronpedia.org/jlens.
Jacobian Lens
The Jacobian Lenses (<model>/jlens/) were trained using Anthropic's Jacobian Lens library. The same library can be used to load the lens and local visualizer.
J++ Lens
The J++ Lenses (<model>/jpp/) are from J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses (Ayonrinde & Lindsey, 2026). J++ Lens is an improved, drop-in replacement for the Jacobian Lens. It is more coherent on earlier layers, where the Jacobian Lens is often unreliable.
Credit for the method and for the lenses marked "Ayonrinde & Lindsey" goes to the authors. Their original lens files are at koayon/jpp-lenses. We changed those files to the format that the Neuronpedia inference server and the Jacobian Lens files use. We cast the maps from fp32 to fp16 (relative error 2.7e-4 or less; 1.1e-3 for Gemma 4 31B, which has very small values in some layers). We did not change them in other ways. We fitted the lenses marked "Neuronpedia" with the paper's code and recipe.
| Model | File | Fitted by | Recall@10 |
|---|---|---|---|
| Qwen/Qwen3.6-27B | qwen3.6-27b/jpp/Salesforce-wikitext/Qwen3.6-27B_jpp_lens.pt | Ayonrinde & Lindsey | 55.2% |
| Qwen/Qwen3.5-9B | qwen3.5-9b/jpp/Salesforce-wikitext/Qwen3.5-9B_jpp_lens.pt | Ayonrinde & Lindsey | 55.0% |
| google/gemma-4-31B | gemma-4-31b/jpp/Salesforce-wikitext/gemma-4-31B_jpp_lens.pt | Ayonrinde & Lindsey | 58%* |
| allenai/Olmo-3-1125-32B | olmo-3-1125-32b/jpp/Salesforce-wikitext/Olmo-3-1125-32B_jpp_lens.pt | Ayonrinde & Lindsey | 50.0% |
| Qwen/Qwen3.5-122B-A10B | qwen3.5-122b-a10b/jpp/Salesforce-wikitext/Qwen3.5-122B-A10B_jpp_lens.pt | Ayonrinde & Lindsey | 48.4% |
| deepseek-ai/DeepSeek-V4-Flash | deepseek-v4-flash/jpp/Salesforce-wikitext/DeepSeek-V4-Flash_jpp_lens.pt | Ayonrinde & Lindsey | 61.4% |
| Qwen/Qwen3.5-0.8B | qwen3.5-0.8b/jpp/Salesforce-wikitext/Qwen3.5-0.8B_jpp_lens.pt | Neuronpedia | 44.9% |
| Qwen/Qwen3.5-2B | qwen3.5-2b/jpp/Salesforce-wikitext/Qwen3.5-2B_jpp_lens.pt | Neuronpedia | 51.5% |
| Qwen/Qwen3-4B | qwen3-4b/jpp/Salesforce-wikitext/Qwen3-4B_jpp_lens.pt | Neuronpedia | 53.8% |
Recall@10 is macro recall on the paper's labelled readout items. The scores for the "Ayonrinde & Lindsey" lenses are from koayon/jpp-lenses. We scored the "Neuronpedia" lenses with the paper's evaluation, on its held-out items.
*The poetry task is dropped for Gemma 4 31B, where only 1 of its 98 items passes the correctness filter.
File format
Each <model>_jpp_lens.pt is a torch.save dictionary, loadable with torch.load(path, weights_only=True):
| Key | Value |
|---|---|
J | {layer: fp16 Tensor[d_model, d_model]}, the map from that layer's residual stream to the final block's |
source_layers | the layers in J: every block except the final one |
d_model | the residual width |
n_prompts | the number of WikiText-103 prompts in the fit |
provenance | the source file, the method, the fitting configuration, and the activation that the maps read (capture_point, stream_reduce) |
The readout at layer ℓ for a residual h is logits = unembed(h @ J[ℓ].T), where unembed is the model's final norm followed by its LM head. For DeepSeek-V4-Flash, h is the mean of the four residual streams.
The config.yaml beside each file records the source, the fitting configuration, and the command that wrote the file.
Licence
The J++ Lens files (<model>/jpp/) are Apache-2.0, as the source lenses. Each lens is derived from its base model. The method builds on the Jacobian Lens of Gurnee et al. (2026) and the R-Lens of Camila Blank and Agam Bhatia.
Citation
Citation information for the J++ Lens paper is coming soon.
- License
- apache-2.0
- Created
- Jun 17, 2026
- Updated
- Oct 15, 2026