---
title: "Saelens — Train sparse autoencoders to interpret model features"
sidebar_label: "Saelens"
description: "Train sparse autoencoders to interpret model features"
---

{/* This page is auto-generated from the skill's SKILL.md by website/scripts/generate-skill-docs.py. Edit the source SKILL.md, not this page. */}

# Saelens

Train sparse autoencoders to interpret model features.

## Skill metadata

| | |
|---|---|
| Source | Optional — install with `hermes skills install official/mlops/saelens` |
| Path | `optional-skills/mlops\saelens` |
| Version | `1.0.1` |
| Author | Orchestra Research |
| License | MIT |
| Dependencies | `sae-lens>=6.0.0`, `transformer-lens>=2.0.0`, `torch>=2.0.0` |
| Platforms | linux, macos, windows |
| Tags | `Sparse Autoencoders`, `SAE`, `Mechanistic Interpretability`, `Feature Discovery`, `Superposition` |

## Reference: full SKILL.md

:::info
The following is the complete skill definition that Hermes loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.
:::

# SAELens: Sparse Autoencoders for Mechanistic Interpretability

SAELens is the primary library for training and analyzing Sparse Autoencoders (SAEs) - a technique for decomposing polysemantic neural network activations into sparse, interpretable features. Based on Anthropic's groundbreaking research on monosemanticity.

**GitHub**: [jbloomAus/SAELens](https://github.com/jbloomAus/SAELens) (1,100+ stars)

## The Problem: Polysemanticity & Superposition

Individual neurons in neural networks are **polysemantic** - they activate in multiple, semantically distinct contexts. This happens because models use **superposition** to represent more features than they have neurons, making interpretability difficult.

**SAEs solve this** by decomposing dense activations into sparse, monosemantic features - typically only a small number of features activate for any given input, and each feature corresponds to an interpretable concept.

## When to Use SAELens

**Use SAELens when you need to:**
- Discover interpretable features in model activations
- Understand what concepts a model has learned
- Study superposition and feature geometry
- Perform feature-based steering or ablation
- Analyze safety-relevant features (deception, bias, harmful content)

**Consider alternatives when:**
- You need basic activation analysis → Use **TransformerLens** directly
- You want causal intervention experiments → Use **pyvene** or **TransformerLens**
- You need production steering → Consider direct activation engineering

## Installation

```bash
pip install sae-lens
```

Requirements: Python 3.10+, transformer-lens>=2.0.0

## Core Concepts

### What SAEs Learn

SAEs are trained to reconstruct model activations through a sparse bottleneck:

```
Input Activation → Encoder → Sparse Features → Decoder → Reconstructed Activation
    (d_model)       ↓        (d_sae >> d_model)    ↓         (d_model)
                 sparsity                      reconstruction
                 penalty                          loss
```

**Loss Function**: `MSE(original, reconstructed) + L1_coefficient × L1(features)`

### Key Validation (Anthropic Research)

In "Towards Monosemanticity", human evaluators found **70% of SAE features genuinely interpretable**. Features discovered include:
- DNA sequences, legal language, HTTP requests
- Hebrew text, nutrition statements, code syntax
- Sentiment, named entities, grammatical structures

## Workflow 1: Loading and Analyzing Pre-trained SAEs

### Step-by-Step

```python
from transformer_lens import HookedTransformer
from sae_lens import SAE

# 1. Load model and pre-trained SAE
model = HookedTransformer.from_pretrained("gpt2-small", device="cuda")
# In sae-lens v6, SAE.from_pretrained() returns JUST the SAE (not a tuple).
sae = SAE.from_pretrained(
    release="gpt2-small-res-jb",
    sae_id="blocks.8.hook_resid_pre",
    device="cuda"
)
# If you also need the cfg dict and feature sparsity, use:
# sae, cfg_dict, sparsity = SAE.from_pretrained_with_cfg_and_sparsity(...)

# 2. Get model activations
tokens = model.to_tokens("The capital of France is Paris")
_, cache = model.run_with_cache(tokens)
activations = cache["resid_pre", 8]  # [batch, pos, d_model]

# 3. Encode to SAE features
sae_features = sae.encode(activations)  # [batch, pos, d_sae]
print(f"Active features: {(sae_features > 0).sum()}")

# 4. Find top features for each position
for pos in range(tokens.shape[1]):
    top_features = sae_features[0, pos].topk(5)
    token = model.to_str_tokens(tokens[0, pos:pos+1])[0]
    print(f"Token '{token}': features {top_features.indices.tolist()}")

# 5. Reconstruct activations
reconstructed = sae.decode(sae_features)
reconstruction_error = (activations - reconstructed).norm()
```

### Available Pre-trained SAEs

| Release | Model | Layers |
|---------|-------|--------|
| `gpt2-small-res-jb` | GPT-2 Small | Multiple residual streams |
| `gemma-2b-res` | Gemma 2B | Residual streams |
| Various on HuggingFace | Search tag `saelens` | Various |

### Checklist
- [ ] Load model with TransformerLens
- [ ] Load matching SAE for target layer
- [ ] Encode activations to sparse features
- [ ] Identify top-activating features per token
- [ ] Validate reconstruction quality

## Workflow 2: Training a Custom SAE

### Step-by-Step

```python
from sae_lens import (
    LanguageModelSAETrainingRunner,
    LanguageModelSAERunnerConfig,
    StandardTrainingSAEConfig,
    LoggingConfig,
)

# 1. Configure training (v6 uses a NESTED config: SAE-specific options live in a
#    `sae=` sub-config, and logging options live in a `logger=` sub-config).
#    Note: `architecture`, `d_sae`, `l1_coefficient` etc. are now on the SAE sub-config,
#    and legacy flat options like `hook_layer`, `activation_fn`, `log_to_wandb` were removed.
cfg = LanguageModelSAERunnerConfig(
    # SAE architecture + sparsity (nested)
    sae=StandardTrainingSAEConfig(
        d_in=768,          # Model dimension
        d_sae=768 * 8,     # Expansion factor of 8
        l1_coefficient=8e-5,  # Sparsity penalty
        apply_b_dec_to_input=True,
        normalize_activations="expected_average_only_in",
    ),

    # Data-generating function (model + hook point)
    model_name="gpt2-small",
    hook_name="blocks.8.hook_resid_pre",  # layer is inferred from hook_name (no hook_layer)

    # Training
    lr=4e-4,
    l1_warm_up_steps=1000,
    train_batch_size_tokens=4096,
    training_tokens=100_000_000,

    # Data
    dataset_path="monology/pile-uncopyrighted",
    context_size=128,

    # Logging (nested)
    logger=LoggingConfig(
        log_to_wandb=True,
        wandb_project="sae-training",
    ),

    # Checkpointing
    checkpoint_path="checkpoints",
    n_checkpoints=5,
)

# 2. Train
trainer = LanguageModelSAETrainingRunner(cfg)  # SAETrainingRunner still works as an alias
sae = trainer.run()

# 3. Evaluate
print(f"L0 (avg active features): {trainer.metrics['l0']}")
print(f"CE Loss Recovered: {trainer.metrics['ce_loss_score']}")
```

> **v6 migration note:** For other SAE types swap the `sae=` sub-config —
> `GatedTrainingSAEConfig`, `TopKTrainingSAEConfig` (set `k` directly), or
> `JumpReLUTrainingSAEConfig` (uses `l0_coefficient`). Legacy flat options
> (`architecture`, `expansion_factor`, `hook_layer`, `activation_fn`/`activation_fn_kwargs`,
> `use_ghost_grads`, ghost grads, b_dec/decoder init options) were removed in v6.

### Key Hyperparameters

| Parameter | Typical Value | Effect |
|-----------|---------------|--------|
| `d_sae` | 4-16× d_model | More features, higher capacity |
| `l1_coefficient` | 5e-5 to 1e-4 | Higher = sparser, less accurate |
| `lr` | 1e-4 to 1e-3 | Standard optimizer LR |
| `l1_warm_up_steps` | 500-2000 | Prevents early feature death |

### Evaluation Metrics

| Metric | Target | Meaning |
|--------|--------|---------|
| **L0** | 50-200 | Average active features per token |
| **CE Loss Score** | 80-95% | Cross-entropy recovered vs original |
| **Dead Features** | &lt;5% | Features that never activate |
| **Explained Variance** | >90% | Reconstruction quality |

### Checklist
- [ ] Choose target layer and hook point
- [ ] Set expansion factor (d_sae = 4-16× d_model)
- [ ] Tune L1 coefficient for desired sparsity
- [ ] Enable L1 warm-up to prevent dead features
- [ ] Monitor metrics during training (W&B)
- [ ] Validate L0 and CE loss recovery
- [ ] Check dead feature ratio

## Workflow 3: Feature Analysis and Steering

### Analyzing Individual Features

```python
from transformer_lens import HookedTransformer
from sae_lens import SAE
import torch

model = HookedTransformer.from_pretrained("gpt2-small", device="cuda")
sae = SAE.from_pretrained(  # v6 returns just the SAE
    release="gpt2-small-res-jb",
    sae_id="blocks.8.hook_resid_pre",
    device="cuda"
)

# Find what activates a specific feature
feature_idx = 1234
test_texts = [
    "The scientist conducted an experiment",
    "I love chocolate cake",
    "The code compiles successfully",
    "Paris is beautiful in spring",
]

for text in test_texts:
    tokens = model.to_tokens(text)
    _, cache = model.run_with_cache(tokens)
    features = sae.encode(cache["resid_pre", 8])
    activation = features[0, :, feature_idx].max().item()
    print(f"{activation:.3f}: {text}")
```

### Feature Steering

```python
def steer_with_feature(model, sae, prompt, feature_idx, strength=5.0):
    """Add SAE feature direction to residual stream."""
    tokens = model.to_tokens(prompt)

    # Get feature direction from decoder
    feature_direction = sae.W_dec[feature_idx]  # [d_model]

    def steering_hook(activation, hook):
        # Add scaled feature direction at all positions
        activation += strength * feature_direction
        return activation

    # Generate with steering
    output = model.generate(
        tokens,
        max_new_tokens=50,
        fwd_hooks=[("blocks.8.hook_resid_pre", steering_hook)]
    )
    return model.to_string(output[0])
```

### Feature Attribution

```python
# Which features most affect a specific output?
tokens = model.to_tokens("The capital of France is")
_, cache = model.run_with_cache(tokens)

# Get features at final position
features = sae.encode(cache["resid_pre", 8])[0, -1]  # [d_sae]

# Get logit attribution per feature
# Feature contribution = feature_activation × decoder_weight × unembedding
W_dec = sae.W_dec  # [d_sae, d_model]
W_U = model.W_U    # [d_model, vocab]

# Contribution to "Paris" logit
paris_token = model.to_single_token(" Paris")
feature_contributions = features * (W_dec @ W_U[:, paris_token])

top_features = feature_contributions.topk(10)
print("Top features for 'Paris' prediction:")
for idx, val in zip(top_features.indices, top_features.values):
    print(f"  Feature {idx.item()}: {val.item():.3f}")
```

## Common Issues & Solutions

> All examples below use the v6 nested config: SAE-specific options go in the `sae=`
> sub-config (`StandardTrainingSAEConfig` / `TopKTrainingSAEConfig` / etc.), training
> knobs stay on the top-level `LanguageModelSAERunnerConfig`.

### Issue: High dead feature ratio
```python
from sae_lens import LanguageModelSAERunnerConfig, StandardTrainingSAEConfig

# WRONG: no warm-up, features die early
cfg = LanguageModelSAERunnerConfig(
    sae=StandardTrainingSAEConfig(d_in=768, d_sae=768*8, l1_coefficient=1e-4),
    l1_warm_up_steps=0,  # Bad!
)

# RIGHT: warm up the L1 penalty (v6 removed ghost grads; warm-up is the lever now)
cfg = LanguageModelSAERunnerConfig(
    sae=StandardTrainingSAEConfig(d_in=768, d_sae=768*8, l1_coefficient=8e-5),
    l1_warm_up_steps=1000,  # Gradually increase
)
```

### Issue: Poor reconstruction (low CE recovery)
```python
# Reduce sparsity penalty and/or add capacity (both on the SAE sub-config)
cfg = LanguageModelSAERunnerConfig(
    sae=StandardTrainingSAEConfig(
        d_in=768,
        d_sae=768 * 16,       # More capacity
        l1_coefficient=5e-5,  # Lower = better reconstruction
    ),
)
```

### Issue: Features not interpretable
```python
from sae_lens import LanguageModelSAERunnerConfig, StandardTrainingSAEConfig, TopKTrainingSAEConfig

# Increase sparsity (higher L1)
cfg = LanguageModelSAERunnerConfig(
    sae=StandardTrainingSAEConfig(d_in=768, d_sae=768*8, l1_coefficient=1e-4),
)
# Or use a TopK SAE (k is set directly in v6, not via activation_fn_kwargs)
cfg = LanguageModelSAERunnerConfig(
    sae=TopKTrainingSAEConfig(d_in=768, d_sae=768*8, k=50),  # Exactly 50 active features
)
```

### Issue: Memory errors during training
```python
cfg = LanguageModelSAERunnerConfig(
    sae=StandardTrainingSAEConfig(d_in=768, d_sae=768*8, l1_coefficient=8e-5),
    train_batch_size_tokens=2048,  # Reduce batch size
    store_batch_size_prompts=4,    # Fewer prompts in buffer
    n_batches_in_buffer=8,         # Smaller activation buffer
)
```

## Integration with Neuronpedia

Browse pre-trained SAE features at [neuronpedia.org](https://neuronpedia.org):

```python
# Features are indexed by SAE ID
# Example: gpt2-small layer 8 feature 1234
# → neuronpedia.org/gpt2-small/8-res-jb/1234
```

## Key Classes Reference

| Class | Purpose |
|-------|---------|
| `SAE` | Sparse Autoencoder model |
| `LanguageModelSAERunnerConfig` | Top-level training configuration (nests `sae=` and `logger=`) |
| `StandardTrainingSAEConfig` / `TopKTrainingSAEConfig` / `GatedTrainingSAEConfig` / `JumpReLUTrainingSAEConfig` | SAE-type-specific sub-configs (v6) |
| `LoggingConfig` | Logging/W&B sub-config (v6) |
| `LanguageModelSAETrainingRunner` | Training loop manager (alias: `SAETrainingRunner`) |
| `ActivationsStore` | Activation collection and batching |
| `HookedSAETransformer` | TransformerLens + SAE integration |

## Reference Documentation

For detailed API documentation, tutorials, and advanced usage, see the `references/` folder:

| File | Contents |
|------|----------|
| [references/README.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops\saelens/references/README.md) | Overview and quick start guide |
| [references/api.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops\saelens/references/api.md) | Complete API reference for SAE, TrainingSAE, configurations |
| [references/tutorials.md](https://github.com/NousResearch/hermes-agent/blob/main/optional-skills/mlops\saelens/references/tutorials.md) | Step-by-step tutorials for training, analysis, steering |

## External Resources

### Tutorials
- [Basic Loading & Analysis](https://github.com/jbloomAus/SAELens/blob/main/tutorials/basic_loading_and_analysing.ipynb)
- [Training a Sparse Autoencoder](https://github.com/jbloomAus/SAELens/blob/main/tutorials/training_a_sparse_autoencoder.ipynb)
- [ARENA SAE Curriculum](https://www.lesswrong.com/posts/LnHowHgmrMbWtpkxx/intro-to-superposition-and-sparse-autoencoders-colab)

### Papers
- [Towards Monosemanticity](https://transformer-circuits.pub/2023/monosemantic-features) - Anthropic (2023)
- [Scaling Monosemanticity](https://transformer-circuits.pub/2024/scaling-monosemanticity/) - Anthropic (2024)
- [Sparse Autoencoders Find Highly Interpretable Features](https://arxiv.org/abs/2309.08600) - Cunningham et al. (ICLR 2024)

### Official Documentation
- [SAELens Docs](https://jbloomaus.github.io/SAELens/)
- [Neuronpedia](https://neuronpedia.org) - Feature browser

## SAE Architectures

| Architecture | Description | Use Case |
|--------------|-------------|----------|
| **Standard** | ReLU + L1 penalty | General purpose |
| **Gated** | Learned gating mechanism | Better sparsity control |
| **TopK** | Exactly K active features | Consistent sparsity |

```python
from sae_lens import LanguageModelSAERunnerConfig, TopKTrainingSAEConfig

# TopK SAE (exactly 50 features active) — `k` is set on the SAE sub-config in v6
cfg = LanguageModelSAERunnerConfig(
    sae=TopKTrainingSAEConfig(d_in=768, d_sae=768*8, k=50),
)
```
