/mechanistic-interpretability-transformer-lens

name: transformer-lens-interpretability
description: Provides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Mechanistic Interpretability, TransformerLens, Activation Patching, Circuit Analysis]
dependencies: [transformer-lens>=2.0.0, torch>=2.0.0]

TransformerLens: Mechanistic Interpretability for Transformers

TransformerLens is the de facto standard library for mechanistic interpretability research on GPT-style language models. Created by Neel Nanda and maintained by Bryce Meyer, it provides clean interfaces to inspect and manipulate model internals via HookPoints on every activation.

**GitHub**: [TransformerLensOrg/TransformerLens](https://github.com/TransformerLensOrg/TransformerLens) (2,900+ stars)

When to Use TransformerLens

**Use TransformerLens when you need to:**

• Reverse-engineer algorithms learned during training
• Perform activation patching / causal tracing experiments
• Study attention patterns and information flow
• Analyze circuits (e.g., induction heads, IOI circuit)
• Cache and inspect intermediate activations
• Apply direct logit attribution

**Consider alternatives when:**

• You need to work with non-transformer architectures → Use **nnsight** or **pyvene**
• You want to train/analyze Sparse Autoencoders → Use **SAELens**
• You need remote execution on massive models → Use **nnsight** with NDIF
• You want higher-level causal intervention abstractions → Use **pyvene**

Installation

pip install transformer-lens

For development version:

pip install git+https://github.com/TransformerLensOrg/TransformerLens

Core Concepts

HookedTransformer

The main class that wraps transformer models with HookPoints on every activation:

from transformer_lens import HookedTransformer

# Load a model
model = HookedTransformer.from_pretrained("gpt2-small")

# For gated models (LLaMA, Mistral)
import os
os.environ["HF_TOKEN"] = "your_token"
model = HookedTransformer.from_pretrained("meta-llama/Llama-2-7b-hf")

Supported Models (50+)

| Family | Models | |--------|--------| | GPT-2 | gpt2, gpt2-medium, gpt2-large, gpt2-xl | | LLaMA | llama-7b, llama-13b, llama-2-7b, llama-2-13b | | EleutherAI | pythia-70m to pythia-12b, gpt-neo, gpt-j-6b | | Mistral | mistral-7b, mixtral-8x7b | | Others | phi, qwen, opt, gemma |

Activation Caching

Run the model and cache all intermediate activations:

# Get all activations
tokens = model.to_tokens("The Eiffel Tower is in")
logits, cache = model.run_with_cache(tokens)

# Access specific activations
residual = cache["resid_post", 5]  # Layer 5 residual stream
attn_pattern = cache["pattern", 3]  # Layer 3 attention pattern
mlp_out = cache["mlp_out", 7]  # Layer 7 MLP output

# Filter which activations to cache (saves memory)
logits, cache = model.run_with_cache(
    tokens,
    names_filter=lambda name: "resid_post" in name
)

ActivationCache Keys

| Key Pattern | Shape | Description | |-------------|-------|-------------| | `resid_pre, layer` | [batch, pos, d_model] | Residual before attention | | `resid_mid, layer` | [batch, pos, d_model] | Residual after attention | | `resid_post, layer` | [batch, pos, d_model] | Residual after MLP | | `attn_out, layer` | [batch, pos, d_model] | Attention output | | `mlp_out, layer` | [batch, pos, d_model] | MLP output | | `pattern, layer` | [batch, head, q_pos, k_pos] | Attention pattern (post-softmax) | | `q, layer` | [batch, pos, head, d_head] | Query vectors | | `k, layer` | [batch, pos, head, d_head] | Key vectors | | `v, layer` | [batch, pos, head, d_head] | Value vectors |

Workflow 1: Activation Patching (Causal Tracing)

Identify which activations causally affect model output by patching clean activations into corrupted runs.

Step-by-Step

from transformer_lens import HookedTransformer, patching
import torch

model = HookedTransformer.from_pretrained("gpt2-small")

# 1. Define clean and corrupted prompts
clean_prompt = "The Eiffel Tower is in the city of"
corrupted_prompt = "The Colosseum is in the city of"

clean_tokens = model.to_tokens(clean_prompt)
corrupted_tokens = model.to_tokens(corrupted_prompt)

# 2. Get clean activations
_, clean_cache = model.run_with_cache(clean_tokens)

# 3. Define metric (e.g., logit difference)
paris_token = model.to_single_token(" Paris")
rome_token = model.to_single_token(" Rome")

def metric(logits):
    return logits[0, -1, paris_token] - logits[0, -1, rome_token]

# 4. Patch each position and layer
results = torch.zeros(model.cfg.n_layers, clean_tokens.shape[1])

for layer in range(model.cfg.n_layers):
    for pos in range(clean_tokens.shape[1]):
        def patch_hook(activation, hook):
            activation[0, pos] = clean_cache[hook.name][0, pos]
            return activation

        patched_logits = model.run_with_hooks(
            corrupted_tokens,
            fwd_hooks=[(f"blocks.{layer}.hook_resid_post", patch_hook)]
        )
        results[layer, pos] = metric(patched_logits)

# 5. Visualize results (layer x position heatmap)

Checklist

• [ ] Define clean and corrupted inputs that differ minimally
• [ ] Choose metric that captures behavior difference
• [ ] Cache clean activations
• [ ] Systematically patch each (layer, position) combination
• [ ] Visualize results as heatmap
• [ ] Identify causal hotspots

Workflow 2: Circuit Analysis (Indirect Object Identification)

Replicate the IOI circuit discovery from "Interpretability in the Wild".

Step-by-Step

from transformer_lens import HookedTransformer
import torch

model = HookedTransformer.from_pretrained("gpt2-small")

# IOI task: "When John and Mary went to the store, Mary gave a bottl