Strip narrative README; keep minimal stub

README.md

@@ -1,143 +1,5 @@
 ---
-language: en
 license: mit
-library_name: pytorch
-tags:
-  - sparse-autoencoder
-  - temporal-sae
-  - crosscoder
-  - interpretability
-  - temporal-crosscoder
-pipeline_tag: feature-extraction
 ---
 
-# Temporal Crosscoder checkpoints (Phase 5 + Phase 5.7)
-
-Trained-checkpoint artifact for the [temp_xc](https://github.com/chainik1125/temp_xc)
-research repository. These are **Sparse Autoencoder / Crosscoder**
-checkpoints trained on `google/gemma-2-2b-it` layer 13 residual stream
-activations over 24 000 FineWeb sequences.
-
-See `summary.md` and the autoresearch plan in the code repo for the
-full story. This HF repo only hosts the binary `.pt` checkpoints;
-all training / probing / plotting code lives in the github repo.
-
-## Quick start
-
-Download all ckpts:
-
-```bash
-pip install huggingface_hub
-huggingface-cli download han1823123123/txcdr --local-dir ./ckpts
-```
-
-Or a single file:
-
-```bash
-huggingface-cli download han1823123123/txcdr \
-  ckpts/txcdr_contrastive_t5__seed42.pt \
-  --local-dir ./ckpts
-```
-
-## Loading a checkpoint
-
-Each `.pt` is saved as a dict with keys
-`{"state_dict", "arch", "meta", "state_dict_dtype"}`. The
-`state_dict` is in **fp16** to halve disk cost; the loader in
-`run_probing.py` casts it back to fp32 when building the model. Example
-(from `experiments/phase5_downstream_utility/probing/run_probing.py`):
-
-```python
-import torch
-from src.architectures.crosscoder import TemporalCrosscoder
-
-state = torch.load("ckpts/txcdr_t5__seed42.pt",
-                   map_location="cuda", weights_only=False)
-meta = state["meta"]
-T = meta["T"]                # e.g. 5
-k_eff = meta["k_win"] or (meta["k_pos"] * T)
-model = TemporalCrosscoder(d_in=2304, d_sae=18432, T=T, k=k_eff).cuda()
-cast = {k: v.float() if v.dtype == torch.float16 else v
-        for k, v in state["state_dict"].items()}
-model.load_state_dict(cast)
-model.eval()
-```
-
-For other architectures, the corresponding class lives under
-`src/architectures/` in the github repo. The `arch` field in each
-ckpt's state tells you which class to instantiate; the `meta` dict
-carries the constructor arguments.
-
-## Contents
-
-### Phase 5 canonical benchmark (seed 42, 25 archs)
-
-Trained on the full 6 000-sequence GPU-preloaded subset, plateau-stop
-at <2 %/1k-step loss drop, Adam lr=3e-4, batch=1024, max 25 000 steps.
-Probed on 36 binary tasks at `last_position` and `mean_pool`
-aggregations — see `docs/han/research_logs/phase5_downstream_utility/summary.md`
-in the github repo for the leaderboard.
-
-| family | arch(s) |
-|---|---|
-| Token SAE | `topk_sae` |
-| Layer crosscoder | `mlc`, `mlc_contrastive` |
-| Temporal crosscoder (T-sweep) | `txcdr_t{2,3,5,8,10,15,20}` |
-| Stacked per-position | `stacked_t{5,20}` |
-| Matryoshka (position-nested) | `matryoshka_t5` |
-| Weight-sharing ablation | `txcdr_shared_dec_t5`, `txcdr_shared_enc_t5`, `txcdr_tied_t5`, `txcdr_pos_t5`, `txcdr_causal_t5` |
-| Sparse-structure variant | `txcdr_block_sparse_t5` |
-| Decoder rank variant | `txcdr_lowrank_dec_t5`, `txcdr_rank_k_dec_t5` |
-| Time-contrastive (Ye et al. 2025) | `temporal_contrastive` (single-token) |
-| Time × Layer (novel) | `time_layer_crosscoder_t5` (d_sae=8192) |
-| TFA | `tfa_small`, `tfa_pos_small` (d_sae=4096, seq_len=32) |
-
-### Phase 5.7 autoresearch archs (seed 42)
-
-Novel architectures explored after the 25-arch benchmark. See
-`docs/han/research_logs/phase5_downstream_utility/2026-04-21-phase5_7-architectures.md`
-for design details.
-
-**Tier-1 / Tier-2 candidates:**
-
-| arch | role | status |
-|---|---|---|
-| `txcdr_contrastive_t5` | A2: TXCDR + Matryoshka H/L + InfoNCE on adjacent windows | FINALIST |
-| `matryoshka_txcdr_contrastive_t5` | A3: position-nested Matryoshka + InfoNCE on scale-1 prefix | FINALIST |
-| `txcdr_rotational_t5` | A1: rank-K Lie-group decoder | DISCARD |
-| `txcdr_basis_expansion_t5` | A5: decoder as K=3 basis combination | DISCARD |
-| `mlc_temporal_t3` | A4: MLC with shared-across-time encoder | DISCARD |
-| `time_layer_contrastive_t5` | A10: time_layer_crosscoder + InfoNCE on (T,L)-mean prefix | AMBIGUOUS |
-| `txcdr_dynamics_t5` | A8: recurrent sparse latent with per-feature gate | DISCARD |
-
-**Part B α-sweep variants (seed 42):**
-
-| arch | α | role |
-|---|---|---|
-| `txcdr_contrastive_t5_alpha003` | 0.03 | A2 under-weight |
-| `txcdr_contrastive_t5_alpha100` | 1.00 | A2 paper-default |
-| `txcdr_contrastive_t5_k2x` | 0.10 | A2 at k_win=1000 |
-| `matryoshka_txcdr_contrastive_t5_alpha003` | 0.03 | A3 under-weight |
-| `matryoshka_txcdr_contrastive_t5_alpha100` | 1.00 | A3 paper-default |
-| `matryoshka_txcdr_contrastive_t5_k2x` | 0.10 | A3 at k_win=1000 |
-
-**T17 seed-variance checkpoints (in-progress fragment):**
-a few additional `__seed{1,2,3}` variants exist on some archs from an
-early seed-variance experiment that was scrapped mid-flight in favour
-of Phase 5.7 autoresearch. See the autoresearch plan for context.
-
-## Reproduction
-
-For a full from-scratch reproduction (tokenise FineWeb → build 5-layer
-activation cache → build probe cache → train 25 archs → probe →
-headline plots → T-sweep plot), see
-`docs/han/research_logs/phase5_downstream_utility/2026-04-21-reproduction-brief.md`
-in the github repo. ~120 GB disk + ~12-15 h compute on an A40-class GPU.
-
-## Citation
-
-No paper yet; work-in-progress for NeurIPS submission.
-
-## License
-
-MIT. See LICENSE in the github repo.
+See https://github.com/chainik1125/temp_xc