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---
license: llama3.3
language:
- en
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---
## Model Information
The Goodfire SAE (Sparse Autoencoder) for Llama 3.3 70B is an interpreter model designed to analyze and understand
the internal representations of Llama-3.3-70B-Instruct. This SAE model is trained specifically on layer 50 of
Llama 3.3 70B and achieves an L0 count of 121, enabling the decomposition of complex neural activations
into interpretable features. The model is optimized for interpretability tasks and model steering applications,
allowing researchers and developers to gain insights into the model's internal processing and behavior patterns.
As an open-source tool, it serves as a foundation for advancing interpretability research and enhancing control
over large language model operations.
## Intended Use
By open-sourcing SAEs for leading open models, especially large-scale
models like Llama 3.3 70B, we aim to accelerate progress in interpretability research.
Our initial work with these SAEs has revealed promising applications in model steering,
enhancing jailbreaking safeguards, and interpretable classification methods (docs.goodfire.ai).
We look forward to seeing how the research community builds upon these
foundations and uncovers new applications.
#### Feature labels
## How to use
```python
import torch
from typing import Optional, Callable
import nnsight
from nnsight.intervention import InterventionProxy
# Autoencoder
class SparseAutoEncoder(torch.nn.Module):
def __init__(
self,
d_in: int,
d_hidden: int,
device: torch.device,
dtype: torch.dtype = torch.bfloat16,
):
super().__init__()
self.d_in = d_in
self.d_hidden = d_hidden
self.device = device
self.encoder_linear = torch.nn.Linear(d_in, d_hidden)
self.decoder_linear = torch.nn.Linear(d_hidden, d_in)
self.dtype = dtype
self.to(self.device, self.dtype)
def encode(self, x: torch.Tensor) -> torch.Tensor:
"""Encode a batch of data using a linear, followed by a ReLU."""
return torch.nn.functional.relu(self.encoder_linear(x))
def decode(self, x: torch.Tensor) -> torch.Tensor:
"""Decode a batch of data using a linear."""
return self.decoder_linear(x)
def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
"""SAE forward pass. Returns the reconstruction and the encoded features."""
f = self.encode(x)
return self.decode(f), f
def load_sae(
path: str,
d_model: int,
expansion_factor: int,
device: torch.device = torch.device("cpu"),
):
sae = SparseAutoEncoder(
d_model,
d_model * expansion_factor,
device,
)
sae_dict = torch.load(
path, weights_only=True, map_location=device
)
sae.load_state_dict(sae_dict)
return sae
# Lanngugae model
InterventionInterface = Callable[[InterventionProxy], InterventionProxy]
class ObservableLanguageModel:
def __init__(
self,
model: str,
device: str = "cuda",
dtype: torch.dtype = torch.bfloat16,
):
self.dtype = dtype
self.device = device
self._original_model = model
self._model = nnsight.LanguageModel(
self._original_model,
device_map=device,
torch_dtype=getattr(torch, dtype) if isinstance(dtype, str) else dtype
)
self.tokenizer = self._model.tokenizer
self.d_model = self._attempt_to_infer_hidden_layer_dimensions()
self.safe_mode = False # Nsight validation is disabled by default, slows down inference a lot. Turn on to debug.
def _attempt_to_infer_hidden_layer_dimensions(self):
config = self._model.config
if hasattr(config, "hidden_size"):
return int(config.hidden_size)
raise Exception(
"Could not infer hidden number of layer dimensions from model config"
)
def _find_module(self, hook_point: str):
submodules = hook_point.split(".")
module = self._model
while submodules:
module = getattr(module, submodules.pop(0))
return module
def forward(
self,
inputs: torch.Tensor,
cache_activations_at: Optional[list[str]] = None,
interventions: Optional[dict[str, InterventionInterface]] = None,
use_cache: bool = True,
past_key_values: Optional[tuple[torch.Tensor]] = None,
) -> tuple[torch.Tensor, tuple[torch.Tensor], dict[str, torch.Tensor]]:
cache: dict[str, torch.Tensor] = {}
with self._model.trace(
inputs,
scan=self.safe_mode,
validate=self.safe_mode,
use_cache=use_cache,
past_key_values=past_key_values,
):
# If we input an intervention
if interventions:
for hook_site in interventions.keys():
if interventions[hook_site] is None:
continue
module = self._find_module(hook_site)
if self.cleanup_intervention_layer:
last_layer = self._find_module(
self.cleanup_intervention_layer
)
else:
last_layer = None
intervened_acts, direct_effect_tensor = interventions[
hook_site
](module.output[0])
# Add direct effect tensor as 0 if it is None
if direct_effect_tensor is None:
direct_effect_tensor = 0
# We only modify module.output[0]
if use_cache:
module.output = (
intervened_acts,
module.output[1],
)
if last_layer:
last_layer.output = (
last_layer.output[0] - direct_effect_tensor,
last_layer.output[1],
)
else:
module.output = (intervened_acts,)
if last_layer:
last_layer.output = (
last_layer.output[0] - direct_effect_tensor,
)
if cache_activations_at is not None:
for hook_point in cache_activations_at:
module = self._find_module(hook_point)
cache[hook_point] = module.output.save()
if not past_key_values:
logits = self._model.output[0][:, -1, :].save()
else:
logits = self._model.output[0].squeeze(1).save()
kv_cache = self._model.output.past_key_values.save()
return (
logits.value.detach(),
kv_cache.value,
{k: v[0].detach() for k, v in cache.items()},
)
# Reading out features from the model
llama_3_1_8b = ObservableLanguageModel(
"meta-llama/Llama-3.1-8B-Instruct",
)
input_tokens = llama_3_1_8b.tokenizer.apply_chat_template(
[
{"role": "user", "content": "Hello, how are you?"},
],
return_tensors="pt",
)
logits, kv_cache, features = llama_3_1_8b.forward(
input_tokens,
cache_activations_at=["model.layers.19"],
)
print(features["model.layers.19"].shape)
# Intervention example
sae = load_sae(
path="./llama-3-8b-d-hidden.pth",
d_model=4096,
expansion_factor=16,
)
PIRATE_FEATURE_INDEX = 0
VALUE_TO_MODIFY = 0.1
def example_intervention(activations: nnsight.InterventionProxy):
features = sae.encode(activations).detach()
reconstructed_acts = sae.decode(features).detach()
error = activations - reconstructed_acts
# Modify feature at index 0 across all token positions
features[:, 0] += 0.1
# Very important to add the error term back in!
return sae.decode(features) + error
logits, kv_cache, features = llama_3_1_8b.forward(
input_tokens,
interventions={"model.layers.19": example_intervention},
)
print(llama_3_1_8b.tokenizer.decode(logits[-1].argmax(-1)))
```
## Responsibility & Safety
Safety is at the core of everything we do at Goodfire. As a public benefit
corporation, we’re dedicated to understanding AI models to enable safer, more reliable
generative AI. You can read more about our comprehensive approach to
safety and responsible development in our detailed [safety overview](https://www.goodfire.ai/blog/our-approach-to-safety/). |