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| Field | Value |
|---|---|
| Analyzed model | Qwen/Qwen3.5-2B-Base |
| Analyzed model revision | b1485b2fa6dfa1287294f269f5fb618e03d52d7c |
| Model kind | base |
| Activation site | resid_post |
| Layer indexing | transformer_blocks_zero_based |
| Available layers | 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 |
| Hidden size | 2048 |
| Input row normalization | none |
| ICALens package version | 0.3.6 |
| Fitting dataset | NeelNanda/pile-10k |
| Dataset revision | 127bfedcd5047750df5ccf3a12979a47bfa0bafa |
| Dataset split | train |
| Fitting token scope | all |
| Candidate tokens | 5256549 |
| Fitting tokens | 1000000 |
icalens.json; model-card metadata is not used when loading the lens.1from icalens import ICALens
2
3lens = ICALens.from_pretrained("REPOSITORY_ID")
4result = lens.analyze("She deposited the check at the bank.", layer=0)
5
6print(result.tokens)
7print(result.scores) # signed standard ICA scores
8print(result.energy) # per-token squared-score fractionsscores = lens.transform(activations, layer=0)icalens.json.score² / sum(all component scores²).| Layer | Components | Fitting tokens | FastICA iterations |
|---|---|---|---|
| 0 | 2048 | 1000000 | 50 |
| 1 | 2048 | 1000000 | 50 |
| 2 | 2048 | 1000000 | 50 |
| 3 | 2048 | 1000000 | 50 |
| 4 | 2048 | 1000000 | 50 |
| 5 | 2048 | 1000000 | 50 |
| 6 | 2048 | 1000000 | 50 |
| 7 | 2048 | 1000000 | 50 |
| 8 | 2048 | 1000000 | 50 |
| 9 | 2048 | 1000000 | 50 |
| 10 | 2048 | 1000000 | 50 |
| 11 | 2048 | 1000000 | 50 |
| 12 | 2048 | 1000000 | 50 |
| 13 | 2048 | 1000000 | 50 |
| 14 | 2048 | 1000000 | 50 |
| 15 | 2048 | 1000000 | 50 |
| 16 | 2048 | 1000000 | 50 |
| 17 | 2048 | 1000000 | 50 |
| 18 | 2048 | 1000000 | 50 |
| 19 | 2048 | 1000000 | 50 |
| 20 | 2048 | 1000000 | 50 |
| 21 | 2048 | 1000000 | 50 |
| 22 | 2048 | 1000000 | 50 |
| 23 | 2048 | 1000000 | 50 |
1{
2 "activation_dataset": {
3 "dtype": "bfloat16",
4 "format": "icalens.activations",
5 "format_version": 1,
6 "manifest_sha256": "608f025afdcdc64f83d5135c732ea55d9acbe212205b9cb0612c4a7935513402"
7 },
8 "candidate_tokens": 5256549,
9 "context_length": 1024,
10 "dataset": {
11 "repo_id": "NeelNanda/pile-10k",
12 "revision": "127bfedcd5047750df5ccf3a12979a47bfa0bafa",
13 "split": "train"
14 },
15 "document_framing": {
16 "evidence_note": "Qwen documents are separated by <|endoftext|> inside packed pretraining sequences.",
17 "evidence_url": "https://qwen.readthedocs.io/en/latest/getting_started/concepts.html#control-tokens",
18 "included_in_fitting_samples": false,
19 "policy_source": "project:model_framing.json",
20 "registry_model_entry": "Qwen/Qwen3.5-2B-Base",
21 "registry_schema_version": 1,
22 "registry_sha256": "c6782bd5d550ed35b5e5b1e63c922907444bf4f40c10fb3f7f8f86858a4093d1",
23 "requested": "auto",
24 "strategy": "prepend-eos",
25 "token": "<|endoftext|>",
26 "token_id": 248044
27 },
28 "fitting_tokens": 1000000,
29 "sampling_seed": 0,
30 "text_field": "text",
31 "token_scope": "all"
32}1@article{liu2026icalens,
2 title={ICA Lens: Interpreting Language Models Without Training Another Dictionary},
3 author={Liu, Sida and Han, Feijiang},
4 journal={arXiv preprint arXiv:2606.11722},
5 year={2026}
6}