---
pretty_name: llmdbg analysis artifacts
license: mit
tags:
  - interpretability
  - mechanistic-interpretability
  - r-lens
  - jacobian-lens
---

# llmdbg analysis artifacts

Versioned analysis data used by [llmdbg](https://github.com/liusida/llmdbg).

Method references:

- [Verbalizable Representations Form a Global Workspace in Language Models
  (J-Lens)](https://transformer-circuits.pub/2026/workspace/index.html)
- [R-Lens: Making J-Lens More Faithful on Early
  Layers](https://www.lesswrong.com/posts/nv8oedrnLXKRzNEL9/r-lens-making-j-lens-more-faithful-on-early-layers)

## J-Lens

J-Lens artifacts contain averaged ordinary Jacobians from each residual layer
to the final residual layer. They are fitted with Anthropic's reference
`jacobian-lens` implementation, without the RelP backward-rule substitutions
used for R-Lens.

## R-Lens

R-Lens artifacts contain a learned residual-transport matrix for each source
layer. Neuron Lens applies the appropriate transport to an MLP neuron's output
direction, then applies the target model's final normalization and
unembedding.

Each artifact directory contains:

- `manifest.json`: model compatibility, fitting provenance, tensor metadata,
  and SHA-256 integrity information.
- `lens.safetensors`: matrices named `J.0`, `J.1`, and so on.

The catalog includes locally fitted 0.8B and 2B Base maps. Their manifests
explicitly allow compatible use with the corresponding Instruct checkpoints.
That Base-to-Instruct reuse is a convenience approximation; an artifact fitted
on the exact checkpoint is preferred for rigorous analysis.

The Qwen3.5 9B and 27B entries reference checkpoint-matched J-Lens and R-Lens
maps from the R-Lens authors' pinned
[`workspace-lenses`](https://huggingface.co/camilablank/workspace-lenses)
release. The catalog records the immutable revision, file size, and SHA-256;
llmdbg verifies the downloaded file before loading it. This avoids duplicating
roughly 8.7 GB while keeping artifact resolution reproducible.