deprecate remaining hooked entry points - #1592

* Add Jacobian lens fitting guide (#1544)

* Quantify Jacobian Lens causal swap success (#1545)

Co-authored-by: Dreamer431 <113128214+Dreamer431@users.noreply.github.com>

* test(integration): add oracle parity test for JacobianLens (#1539 Tier-1) (#1543)

* test(integration): add oracle parity test for JacobianLens (#1539 Tier-1)

Compares TransformerBridge JacobianLens.readout() against the reference
anthropics/jacobian-lens oracle (pinned to 581d398) on google/gemma-2-2b-it
across 75 layer x prompt cells (5 prompts x 15 sampled layers).

Pass criteria per the #1539 spec (matching #1505 spike numbers):
  - Worst-case top-8 token overlap >= 7/8 in every cell
  - Spearman rank-correlation >= 0.95 on the top-64 logit union per cell

The oracle is installed at test time via pip from the pinned commit so
the threshold is reproducible independent of upstream drift. Reuses the
bridge's original_model + tokenizer to avoid a second model copy in RAM.

* style: apply black formatting (line-length=100)

* fix: remove unused Dict, Tuple typing imports (pycln)

* style: fix black formatting for py310 target (double blank lines)

* test: use pytest.importorskip for oracle dep; add oracle-parity CI workflow

Replace subprocess pip-install fixture with pytest.importorskip so the
test skips gracefully in standard uv venvs (no pip present) and does not
mutate the developer environment with no cleanup.

Add .github/workflows/oracle-parity.yml — a dedicated workflow that reads
ORACLE_COMMIT from the test file (single source of truth) and installs
the oracle out-of-band before running the @pytest.mark.slow suite.
Triggers on workflow_dispatch and on pushes that touch the test or
workflow file, keeping oracle runs opt-in for PR checks.

* Visual Encoders (ViT, DeiT) Support Rollout (#1546)

* Add ViTArchitectureAdapter to supported architectures

* Add ViTArchitectureAdapter to architecture factory

* Add ViT and DeiT models to model registry

* Add new model descriptions for Vision Transformers and Wav2Vec2

* Add ViTArchitectureAdapter for vision models

Implement ViT/DeiT architecture adapter for model bridging.

* Create vision_embedings.py

* Add VisionClassifierHeadBridge for CLS token classification

Implement VisionClassifierHeadBridge to handle CLS token slicing for classification.

* Add visual model configuration to transformer bridge

* Clarify pixel_values usage for multimodal and vision models

Updated documentation for pixel_values parameter to clarify its use with vision models.

* Update bridge.py

* Update bridge.py

* Rename vision_embedings.py to vision_embeddings.py

* Define vision model and classification architectures

Added vision model architectures and classification heads.

* Add support for vision architectures in transformers

* Refactor VisionClassifierHeadBridge to use pooled output

Updated the VisionClassifierHeadBridge to directly use an already-pooled CLS token instead of slicing from the sequence output. Adjusted the forward method to reflect this change and improved error handling for the original component.

* Update vit.py

* Add unit tests for ViTArchitectureAdapter

This file contains unit tests for the ViTArchitectureAdapter, covering component mapping, configuration flags, weight conversions, and model preparation methods.

* Create test_vit_adapter.py

* Update transformers.py

* Update vit.py

* Update vit.py

* Update vit.py

* Fix type hint for get_remote_component method

* Fix type hint for get_remote_component method

* Change import of torch to torch.nn in vit.py

* Update vit.py

* Re-add dummy 'mlp' attribute injection for ViTLayer

Reintroduce a patch_layers function to inject a dummy 'mlp' attribute into ViTLayer blocks for MLPBridge compatibility.

* Refactor ViTLayer handling by removing patch_layers

Removed the patch_layers function and its call, which injected a dummy 'mlp' attribute into ViTLayer blocks. Updated comments for clarity regarding the MLPBridge container.

* Add dummy 'mlp' attribute to ViTLayer blocks

Inject a dummy 'mlp' attribute into ViTLayer blocks to satisfy hasattr check for TransformerLens.

* Update vit.py

* Remove TestViTConfigNCtx and related test case

Removed deprecated TestViTConfigNCtx class and its test case for n_ctx.

* Enhance ViTLayer with MLP wrapper and fix forward method

Added a non-circular MLP wrapper to ViTLayer blocks and fixed tuple-chaining bug in forward method.

* Refactor ViT layer forward pass handling

Refactor forward pass handling for ViT layers to safely unpack tuple outputs and ensure compatibility with the model's internal structure.

* Refactor ViTLayer forward pass handling

Refactor forward pass handling for ViTLayer to fix tuple-chaining bug and ensure compatibility with HF model outputs.

* Fix tuple handling in ViTLayer forward method

Modified the forward method to handle tuple inputs and outputs for ViTLayer, ensuring compatibility with Tensor expectations.

* Reorder model prefix checks for better clarity

* Update vit.py

* Detect model class name in prepare_model method

Added detection for model class name in prepare_model method.

* Simplify prefix determination for ViT models

Refactor model prefix detection logic for ViT and DeiT models.

* Implement fixture for distilled DeiT model testing

Added a fixture to load the distilled DeiT model for testing.

* Update DeiT bridge tests for bare model handling

Refactor tests for DeiT bridge to accommodate bare model behavior and update assertions accordingly.

* Set architecture in Hugging Face model configuration

* Support DeiTLayer in patch_layers function

* sort

* Replace direct attribute assignment with setattr

* black fix

* fix formatting after merge

* Update vit.py

* Update ViT adapter test paths for consistency

* Remove redundant test for n_ctx in prepare_loading

Removed test for prepare_loading not affecting n_ctx.

* black sorted

* black reorder

* Refactor vit_bridge and vit_bare_bridge fixtures

* temp support up to transformers 5.8.0

* support transformers 5.13.0

* format fixed. Unit test all passed. Intergration test all passed. should be good to go

* Update vit.py

* Clarify tokenizer support in ViTArchitectureAdapter

Added comment to clarify the lack of tokenizer support for vision models.

* Remove head_dim assignment from hf_config

Removed unused head_dim assignment from hf_config.

* Update vit.py

* Update bridge.py

* Add VisionEmbeddingsBridge and VisionClassifierHeadBridge

* Update test_vit_adapter.py

* Update test_vit_adapter.py

* Update test_vit_adapter.py

* Update test_vit_adapter.py

* Improve compatibility mode error and output handling

Updated error message for clarity and added handling for last_hidden_state in output.

* Refactor test to check output type and shape

Update test to verify that the forward method returns a tensor instead of a raw HF output object. Adjust assertions to match the expected behavior after changes in bridge.py.

* Update bridge.py

* Update test_vit_adapter.py

* Remove obsolete tests from TestViTPrepareLoading

Removed deprecated tests for prepare_loading() in TestViTPrepareLoading.

* Update test_vit_adapter.py

* Update test_vit_adapter.py

* formatted

---------

Co-authored-by: Jonah Larson <jonahalarson@comcast.net>

* Add Starcoder2 architecture adapter (#1533)

Co-authored-by: jlarson4 <jonahalarson@comcast.net>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>

* Docs: Jlens Qwen3.5-4b Demo (#1547)

* Run experiments with Qwen-3.5 architecture support

* Run notebook cells

* Add lfm2 tiny integration test (#1552)

* Add lfm2 tiny integration test

* Fix formatting

* Add AST (Audio Spectrogram Transformer) Adapter (#1484)

* chore: save WIP on V3 transformerbridge migration

re add ast import and add to factory
:wq
y

wq
:wq

* feat(ast): migrate AST adapter to V3 TransformerBridge and component_mapping

* refactor(ast): resolve PR feedback for docstrings, prefix-awareness, unit tests

* test(ast): split parity to integration folder, add load_weights boot test, fix audio classification load path, and add to ARCHITECTURE_DESCRIPTIONS

* fix(ast): union audio classification sets, specific boot test assertions and two comment typo fixes

* Verificaiton for lapa (#1556)

* ViT and AST model verification (#1582)

* verified a few models for ViT and AST

* improved vision testing for ViT models

* fix(bridge): return W_in/W_out/W_gate in TL orientation for nn.Linear-backed models (#1558)

* feat: respect prepend_bos and add return_input_tokens flag

* fix torch orientation bug

* review changes

* review changes

* pipeline fix

* deprecate remaining hooked entry points (#1592)

* deprecate remaining hooked entry points

* test: account for hooked transformer warning in notebook

* fix: address deprecation warning review feedback

* fix: correct encoder deprecation warning stacklevel

* support loading fit checkpoints in JacobianLens.load() (#1574)

* support loading fit checkpoints in JacobianLens.load()

* fix: black formatting and update conflicting test for checkpoint load

* address jlarson4 review: preserve target_layer, add reference fixture, fix tuned-lens note

- Remove "target_layer" from _FIT_RESERVED_KEYS so it survives checkpoint
  conversion and validate_model() can refuse non-final-target lenses
- Add test_load_checkpoint_mirrors_fit_payload_schema: fixture matches the
  exact keys fit() produces so format drift causes a test failure
- Fix tuned-lens note: it is the Jacobian artifact format that has no bias
  slot, not the tuned-lens format; tuned-lens translators are affine (weight + bias)

* address jlarson4 review: read n_done key in _from_checkpoint_payload

Real checkpoint writers (reference package) store the prompt count as
n_done, not n_prompts. Prefer n_done with n_prompts as fallback so
genuine checkpoints are not rejected with n_prompts=0.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NiwNUm3YFj9yAuSBuGDnd8

* address jlarson4 review: align checkpoint loader with reference write_checkpoint() schema

- _from_checkpoint_payload: read n_done first (real checkpoints use n_done, not n_prompts)
- _from_checkpoint_payload: infer d_model from jacobian_sum matrix shape (real checkpoints have no d_model key)
- _from_checkpoint_payload: harvest top-level target_layer into metadata (reference format stores it at top level, not nested)
- _from_checkpoint_payload: guard empty jacobian_sum with a clear ValueError before attempting shape derivation
- load() docstring: update Fit checkpoint schema to reflect the real 6-key reference format
- tests: replace test_load_checkpoint_with_zero_n_prompts_raises with two tests
- tests: rewrite test_load_checkpoint_mirrors_fit_payload_schema to use verbatim 6-key reference payload
- tests: add test_load_checkpoint_harvests_flat_provenance_and_strips_fit_keys
- docs: update schema table — replace n_prompts/d_model with real 6-key format
- docs: note d_model inferred from matrix shape
- docs: document target_layer as deliberate exception to fit-key stripping

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

* Drop optional unused aliases on hybrid architectures (#1579)

* Drop optional unused aliases

* feat: assign fallbacks on pruned

* Improve unittests

* Add kurtosis-profile validation test for JacobianLens (#1539 Tier-1) (#1616)

* Add kurtosis-profile validation test for JacobianLens (#1539 Tier-1)

Asserts the workspace-band signature as relative structure rather than
absolute levels, per the cross-family measurement in #1539: band rise vs
the model's own early-third baseline, lens-specificity vs the logit-lens
control through the identical code path, a gpt2-small negative control,
and final-layer identity-transport agreement between arms.

* Fix gpt oss olmo3 parity (#1621)

* Resolution for issue 1619

* Updated for 1620

* Add verification script

* Fixed 1619 on HF

* Verification script repair

* fixing per-layer olmo

* cleanup

---------

Co-authored-by: abhi <abhinavbellapu@berkeley.edu>
Co-authored-by: emerardd <113128214+emerardd@users.noreply.github.com>
Co-authored-by: Dreamer431 <113128214+Dreamer431@users.noreply.github.com>
Co-authored-by: Mukund Pandey <mukund.pandey@gmail.com>
Co-authored-by: Jiankun Wei <72998341+david-wei-01001@users.noreply.github.com>
Co-authored-by: SanjidMzi <56235075+SanjidMzi@users.noreply.github.com>
Co-authored-by: Claude Opus 5 (1M context) <noreply@anthropic.com>
Co-authored-by: Kyle Yin Xu <123780557+KYinXu@users.noreply.github.com>
Co-authored-by: Syed Adil Ahmed <tensorcruncher@gmail.com>
Co-authored-by: Dylan <159935143+dylanberens@users.noreply.github.com>
Co-authored-by: Md.Sadiq <mohammadsadiq4950@gmail.com>
Co-authored-by: msaule <sau24006@byui.edu>
Co-authored-by: Priyanka Bajaj <42418272+priyanka25aug@users.noreply.github.com>