nnterp

A unified interface for all transformer models that puts best NNsight practices for LLMs in everyone's hands.

Built on top of NNsight, nnterp provides a standardized interface for mechanistic interpretability research across all transformer architectures. Unlike transformer_lens which reimplements transformers, nnterp preserves the original HuggingFace implementations while solving the naming convention chaos through intelligent renaming.

Why nnterp?

The Problem: Every transformer model uses different naming conventions - GPT-2 uses transformer.h, LLaMA uses model.layers, OPT uses something else entirely. This makes mechanistic interpretability research painful as you can't just change the name of the model and expect the rest of your code to work.

The Solution: nnterp standardizes all models to use something close to the LLaMA naming convention:

StandardizedTransformer
├── layers
│   ├── self_attn
│   └── mlp
├── ln_final
└── lm_head

and include built-in properties like model.logits and model.next_token_probs.

Unlike other libraries that reimplement transformers, nnterp uses NNsight's renaming feature to work with the original HuggingFace implementations, ensuring perfect compatibility and preventing subtle bugs. nnterp also includes automatic testing to ensure models are correctly standardized. When you load a model, it runs fast validation checks. See the documentation for details.

Installation

• pip install nnterp - Basic installation
• pip install nnterp[display] - Includes visualization dependencies

Examples

Here is a simple example where we load a model and access its standardized internals:

from nnterp import StandardizedTransformer

model = StandardizedTransformer("gpt2")  # or "meta-llama/Llama-2-7b-hf", etc.

with model.trace("The Eiffel Tower is in the city of"):
    # Unified interface across all models (must follow forward pass order!)
    attention_output = model.attentions_output[3]
    mlp_output = model.mlps_output[3]
    layer_5_output = model.layers_output[5]

    # Built-in utilities
    logits = model.logits.save()

---

Standardized Naming

All models use the same naming convention:

with model.trace("Hello world"):
    # Attention and MLP components (access in forward pass order!)
    attn_out = model.attentions_output[3]
    mlp_out = model.mlps_output[3]
    layer_3_output = model.layers_output[3]

    # Layer I/O - works for GPT-2, LLaMA, Gemma, etc.
    layer_5_output = model.layers_output[5]

    # Direct interventions - add residual from layer 3 to layer 10
    model.layers_output[10] = model.layers_output[10] + layer_3_output

---

Built-in Interventions

Common mechanistic interpretability interventions with best practices built-in:

from nnterp.interventions import logit_lens, patchscope_lens, steer

# Logit lens: decode hidden states at each layer
layer_probs = logit_lens(model, ["The capital of France is"])
# Shape: (batch, layers, vocab_size)

# Patchscope: patch hidden states across prompts
from nnterp.interventions import TargetPrompt
target = TargetPrompt("The capital of France is", index_to_patch=-1)
patchscope_probs = patchscope_lens(
    model,
    source_prompts=["The capital of England is"],
    target_patch_prompts=target,
    layer_to_patch=10
)

# Activation steering
import torch
with model.trace("Hello, how are you?"):
    steering_vector = torch.randn(model.hidden_size)
    model.steer(layers=[5, 10], steering_vector=steering_vector, factor=1.5)

---

Prompt and Target Tracking

Track probabilities for specific tokens across interventions:

from nnterp.prompt_utils import Prompt, run_prompts

prompts = [
    Prompt.from_strings(
        "The capital of France is",
        {"target": "Paris", "other": ["London", "Madrid"]},
        model.tokenizer
    )
]

# Get probabilities for all target categories
results = run_prompts(model, prompts)
# Returns: {"target": tensor([0.85]), "other": tensor([0.12])}

# Combine with interventions
results = run_prompts(model, prompts, get_probs_func=logit_lens)
# Returns probabilities across all layers

---

More examples and detailed documentation can be found at butanium.github.io/nnterp

I found a bug!

Before opening an issue, make sure that you have a MWE (minimal working example) that reproduces the issue, and if possible, the equivalent code using NNsight.LanguageModel. If the NNsight MWE also fails, please open an issue on the NNsight repository. Also make sure that you can load the model with AutoModelForCausalLM from transformers.

Contributing

Contribution are welcome! If a functionality is missing, and you implemented it for your reasearch, please open a PR, so that people in the community can benefit from it. That include adding support for new models with custom renamings!

Next steps

Here are some nice features that could be cool to have, and for which I'd be happy to accept PRs (ordered by most to least useful imo):

• Add helpers for getting gradients
• Add support for vllm when NNsight supports it
• Add helpers for NNsight's cache as it returns raw tuple outputs instead of nice vectors.
• Add access to k/q/v

Development

• Install the development environment with make dev or uv sync --all-extras. Add uv pip install flash-attn --no-build-isolation to support models like Phi that require flash-attn.
• Install pre-commit hooks with pre-commit install to automatically update docs/llms.txt when modifying RST files and format the code with black. You might encounter the error with block not found at line xyz when running the tests. In this case run make clean to remove the python cache and try again (NOTE: this should be fixed in latest NNsight versions).
• Create a git tag with the version number git tag vx.y.z; git push origin vx.y.z
• Build with python -m build
• Publish with e.g. twine upload dist/*x.y.z*
• test with pytest --cache-clear. cache-clear is mandatory for now otherwise NNsight's source can break. It might not be sufficient, in which case you can do make clean to remove Python cache.

Citation

If you use nnterp in your research, you can cite it as:

@misc{dumas2025nnterp,
      title={nnterp: A Standardized Interface for Mechanistic Interpretability of Transformers},
      author={Cl{\'e}ment Dumas},
      year={2025},
      eprint={2511.14465},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2511.14465},
}