SAELens
English
sparse-autoencoder
SAE
interpretability
deception-detection
mechanistic-interpretability
neuronpedia
behavioral-sampling
phi
Instructions to use Solshine/deception-saes-phi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SAELens
How to use Solshine/deception-saes-phi-2 with SAELens:
# pip install sae-lens from sae_lens import SAE sae, cfg_dict, sparsity = SAE.from_pretrained( release = "RELEASE_ID", # e.g., "gpt2-small-res-jb". See other options in https://github.com/jbloomAus/SAELens/blob/main/sae_lens/pretrained_saes.yaml sae_id = "SAE_ID", # e.g., "blocks.8.hook_resid_pre". Won't always be a hook point ) - Notebooks
- Google Colab
- Kaggle
File size: 843 Bytes
80cca10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | {
"architecture": "topk",
"d_in": 2560,
"d_sae": 10240,
"dtype": "float32",
"device": "cpu",
"model_name": "microsoft/phi-2",
"hook_name": "model.layers.16",
"hook_layer": 16,
"hook_head_index": null,
"activation_fn_str": "topk",
"activation_fn_kwargs": {},
"apply_b_dec_to_input": false,
"finetuning_scaling_factor": false,
"sae_lens_training_version": "deception-behavioral-v1",
"prepend_bos": false,
"dataset_path": "Solshine/deception-behavioral-multimodel",
"dataset_trust_remote_code": false,
"context_size": null,
"normalize_activations": "none",
"training_condition": "mixed",
"training_notes": "Deception behavioral SAE \u2014 same-prompt behavioral sampling. Model: microsoft/phi-2, Layer 16, topk. See https://github.com/SolshineCode/deception-nanochat-sae-research"
} |