Extracting steering vectors

Two complementary routes turn captured hidden states into interventions.

Analysis-based: easysteer.steer

Computes a semantic direction from contrastive hidden states — no training loop. Available extractors: DiffMean, PCA, LAT, linear probe, and SAE feature vectors.

from easysteer.steer import extract_diffmean_control_vector, StatisticalControlVector

control_vector = extract_diffmean_control_vector(
    all_hidden_states=all_hidden_states,  # nested [samples][layer][token]
    positive_indices=[0, 1, 2, 3],
    negative_indices=[4, 5, 6, 7],
    token_pos=-1,       # which token's activation to use
    normalize=True,
)

control_vector.export_gguf("vectors/diffmean.gguf")
# ... later
control_vector = StatisticalControlVector.import_gguf("vectors/diffmean.gguf")

The exported GGUF file is what VectorSpec(source=...) consumes at inference time.

Sibling functions follow the same shape: extract_pca_control_vector, extract_lat_control_vector, extract_linear_probe_control_vector, and the generic extract_statistical_control_vector. SAE helpers (search_sae_features, get_sae_feature_explanation, extract_sae_decoder_vector) locate and export interpretable SAE decoder directions. See the API reference.

Learning-based: easysteer.reft

Reimplements pyreft: trains a parameterized intervention (e.g. BiasIntervention, LoReFT) on a frozen HuggingFace model with a standard transformers trainer, then saves the learned representation for inference.

import easysteer.reft as reft

reft_config = reft.ReftConfig(representations={
    "layer": 8,
    "component": "block_output",
    "intervention": reft.BiasIntervention(embed_dim=model.config.hidden_size),
})
reft_model = reft.get_reft_model(model, reft_config)
# ... build a data module, run reft.ReftTrainer, then reft_model.save(...)

The complete training walkthrough (data module, trainer, saving) is in ReFT training; see the LoReFT replication for a complete train-then-steer notebook.