Conditionals allow you to define dynamic feature interventions that are applied based on the activation patterns of other features during model inference. This enables creating more sophisticated steering behaviors that respond to the content being generated.

Before using the Conditionals API, you’ll need to find the features you want to intervene on, and a model variant

​
Examples

​
Basic Conditional Intervention

Apply pirate-themed features only when whale-related content is detected:

​
Aborting Generation

Stop generation if certain content is detected:

​
Auto-Generated Conditionals

Use natural language to automatically generate conditional statements:

Auto-Generated Conditionals example
#create a variant
variant = goodfire.Variant("meta-llama/Llama-3.3-70B-Instruct")
# Generate conditional based on description - this will create conditions for both whales and penguins being present
conditional= goodfire.conditional.AutoConditional(
    "when the model talks about whales and penguins",
    client=client,
    model=variant
)
# Get pirate  feature
pirate_feature = client.features.search(
    "talk like a pirate", model=variant, top_k=1
)
# Make the model talk like a pirate when it talks about both whales and penguins
variant.set_when(conditional, {
    pirate_feature[0]: 0.9
})

response = client.chat.completions.create(
    messages=[{"role": "user", "content": "Tell me about whales and penguins!"}],
    model=variant
)

print(response.choices[0].message["content"])

​
Creating Conditionals

​
Comparison Operators

You can create conditionals by comparing features or feature groups with numeric values or other features using standard comparison operators. This creates a Conditional object that can be used in steering behaviors.

# Compare feature to numeric value
condition = feature > 0.75

# Compare feature group to numeric value
condition = feature_group >= 0.5

# Compare features to each other
condition = feature1 < feature2

Supported operators:

  • == (equal)
  • != (not equal)
  • < (less than)
  • <= (less than or equal)
  • > (greater than)
  • >= (greater than or equal)

​
Logical Operators

Multiple conditions can be combined using logical operators to create a ConditionalGroup:

# AND operator
condition = (feature1 > 0.5) & (feature2 < 0.3)

# OR operator
condition = (feature1 > 0.5) | (feature2 > 0.5)

​
Using Conditionals

​
set_when()

Apply feature interventions when a condition is met.

Parameters:

​
condition
ConditionalGroup
required

The ConditionalGroup that triggers the intervention

​
values
Union[FeatureEdits, dict[Union[Feature, FeatureGroup], float]]
required

Feature edits to apply when condition is met

Returns: None

Example:

# Set pirate features when whale features are detected
variant.set_when(whale_feature > 0.75, {
    pirate_features[0]: 0.5
})

​
abort_when()

Abort inference when a condition is met by raising an InferenceAbortedException.

Parameters:

​
condition
ConditionalGroup
required

The ConditionalGroup that triggers the abort

Returns: None

Example:

# Abort if whale features are too strong
variant.abort_when(whale_feature > 0.75)

try:
    response = client.chat.completions.create(
        messages=[{"role": "user", "content": "Tell me about whales."}],
        model=variant
    )
except goodfire.exceptions.InferenceAbortedException:
    print("Generation aborted due to whale content")

​
handle_when()

Register a custom handler function to be called when a condition is met.

Parameters:

​
condition
ConditionalGroup
required

The ConditionalGroup that triggers the handler

​
handler
Callable[[InferenceContext], None]
required

Function that takes an InferenceContext and returns None

Returns: None

Example:

def custom_handler(context: InferenceContext):
    # Custom handling logic
    pass

variant.handle_when(whale_feature > 0.5, custom_handler)

​
AutoConditional

The AutoConditional utility helps automatically generate conditional statements based on natural language descriptions.

Parameters:

​
specification
str
required

Natural language description of the desired condition

​
client
Client
required

Goodfire client instance

​
model
Union[str, Variant]
required

Model to use for generating conditions

Returns:

​
conditional
ConditionalGroup

Generated ConditionalGroup

Example:

# Generate conditional based on description - this will create conditions for both whales and penguins being present
conditional= goodfire.conditional.AutoConditional(
    "when the model talks about whales and penguins",
    client=client,
    model=variant
)
# Get pirate  feature
pirate_feature = client.features.search(
    "talk like a pirate", model=variant, top_k=1
)
# Make the model talk like a pirate when it talks about both whales and penguins
variant.set_when(conditional, {
    pirate_feature[0]: 0.9
})

​
Best Practices

  • Use conditional interventions to create context-aware steering behaviors
  • Combine multiple conditions with logical operators for more precise control
  • Handle aborted inferences gracefully in your application
  • Test conditions thoroughly to ensure desired behavior
  • Consider using AutoConditional for quick prototyping

​
Classes

​
ConditionalGroup

A group of conditions combined with logical operators.

​
Conditional

A single conditional expression comparing features.

​
InferenceContext

Context object containing information about the current inference state.

FeaturesAuto Steer