---
license: mit
tags: [amoe, adapter, mixture-of-experts, sentence-similarity, captionbert, aleph]
base_model: AbstractPhil/captionbert-8192-v2
library_name: amoe-lora
---

# captionbert-8192-v2 :: AMOE 3-anchor collective

Three [amoe-lora](https://github.com/AbstractEyes/amoe-lora) aleph anchors on the
**frozen** `captionbert-8192-v2` trunk, under a trained dispatch, greedily
selected from a **six**-candidate pool. The trunk never moves; only the adapters
and the routing keys train.

This is the shipped collective and the source of the model's headline number:
**.6077 -> .7287** mean Spearman over eight sentence-similarity tasks.

| anchor | trained on | in the ship? |
|---|---|---|
| `equiv` | all-nli triplets | **yes** |
| `simplify` | simple-wiki + altlex + sentence-compression | **yes** |
| `paraphrase` | paraphrase pairs | **yes** |
| `lexical` | lexical-overlap pairs | no -- rejected by the sweep |
| `topical` | topical pairs | no -- tied, not taken (see below) |
| `random` | random pairing | no -- **it is the capacity control** |

All six anchors ship in this folder so the selection can be reproduced or
contested. `equiv` and `simplify` are byte-identical to the ones in `amoe/moe/`
(sha256 `0029ad69…`, `8e419f69…`) -- the collective reused them rather than
retraining.

## Results -- 8 tasks, Spearman rho

The single-anchor columns are **masked** reads: the collective with the other
two arms disabled. Masking never renormalizes the amplitude budget, so a masked
single is *not* the same as that anchor trained and run alone -- it delivers
strictly less. Compare masked-to-masked; do not read these as solo scores.

| task | OFF | `equiv` masked | `simplify` masked | `paraphrase` masked | **COLLECTIVE** |
|---|---|---|---|---|---|
| STS-B   | .5747 | .7208 | .6022 | .6127 | **.7684** |
| SICK-R  | .6526 | .7269 | .6580 | .6585 | **.7391** |
| STS12   | .5051 | .6275 | .5359 | .5154 | **.6682** |
| STS13   | .5995 | .6988 | .6304 | .6338 | **.7557** |
| STS14   | .5452 | .6423 | .5747 | .5774 | **.6921** |
| STS15   | .7136 | .7783 | .7391 | .7328 | **.8055** |
| STS16   | .6776 | .7107 | .6945 | .7332 | **.7626** |
| BIOSSES | .5933 | .5845 | .6016 | .6051 | **.6382** |
| **mean** | **.6077** | .6862 | .6295 | .6336 | **.7287** |
| mean erank | 30.56 | 36.70 | 34.36 | 34.02 | **45.00** |

**The collective beats its best masked member by +.0425** (.7287 vs `equiv`'s
.6862) and beats it on **8 of 8** tasks.

`equiv` masked is *worse than the bare trunk* on BIOSSES (.5845 vs .5933) --
biomedical similarity is outside every anchor's training domain -- yet the
collective still lands **+.0450** there. Recovering on the one task where the
strongest member regresses is the honest read on this dispatch.

Effective rank rises with the score (30.6 -> 45.0). The anchors add *directions*,
not merely a rescaling: this trunk's unsupervised consensus target used only
~28.7 of 768 available directions, and supervision is what buys new ones.

## The greedy sweep -- what was tried, what was rejected

8 subsets, tau 0.1, seed 0, ranked by 8-task mean:

| subset | mean |
|---|---|
| equiv + simplify + topical | .7290 |
| **equiv + simplify + paraphrase**  <- SHIPPED | **.7287** |
| equiv + simplify | .7233 |
| equiv + simplify + paraphrase + random | .7224 |
| equiv + simplify + topical | .7223 |
| equiv + simplify + paraphrase + lexical | .7174 |
| equiv + simplify + random | .7157 |
| equiv + simplify + lexical | .7146 |

Two things worth stating plainly rather than burying:

1. **The `+topical` variant scored .7290, marginally above the shipped .7287.**
   That is a tie inside single-seed noise, and the smaller subset was taken. It
   is not a measured win for the shipped configuration.
2. **`random` is a capacity control, and it is the informative row.** Adding a
   randomly-paired anchor *costs* .0076 against the same-size real subset
   (.7157 vs .7233) and .0063 against the shipped one (.7224 vs .7287). Extra
   adapter capacity by itself does not buy score here -- the arms are earning
   it.

## Why the dispatch works here

Dispatched amplitude is `(w_k/z) * sigmoid(gate_k) * consume_k(x)`, where
`w_k/z = sinh(u_k) / SUM_j cosh(u_j)` over ALL anchors (the damping law).
That approaches ~1.0 only when one anchor engages and the others **abstain**
(`u ~ 0`); if several fire together it collapses and the mixture delivers a
fraction of what any single member does alone.

Measured on this collective: mean `|w/z|` moved from
**.237 / .229 / .263 (blend)** before alignment to
**.587 / .138 / .142 (specialize)** after the keys-only alignment phase, with no
starvation strikes. `equiv` won the amplitude budget and the other two learned
to abstain. That flip is why the mixture beats its best member instead of
damping itself below it.

Per-block routing spread is **0.348** -- routing is not uniform with depth;
`equiv` dominates most blocks, but not all.

## Load

The model resolves its own arms. This is the supported path:

```python
from transformers import AutoModel
model = AutoModel.from_pretrained("AbstractPhil/captionbert-8192-v2",
                                  trust_remote_code=True)
model.attach_amoe()                      # this collective, by default
emb = model.encode(["a cat on a windowsill"])
with model.amoe_off():                   # the unsupervised baseline
    base = model.encode(["a cat on a windowsill"])
model.detach_amoe()                      # bit-exact restore, asserted
```

Explicitly, via amoe-lora:

```python
import amoe
h = amoe.attach(trunk, ["amoe/collective/equiv.anchor.pt",
                        "amoe/collective/simplify.anchor.pt",
                        "amoe/collective/paraphrase.anchor.pt"],
                dispatch="amoe/collective/captionbert-v2-collective.dispatch.pt",
                binding=CaptionBertV2Binding(d=512))   # from modeling_captionbert.py
base = h.detach()        # bit-exact or raises
```

The trunk these anchors expect is **`checkpoints/best_model.pt`**, which is what
the repo-root `model.safetensors` carries (verified tensor-for-tensor, max
absolute delta 0.0). Note that the sibling `-B` repo ships `final_model.pt` at
its root instead -- each repo's arms match its own root, but the two roots are
selected by different criteria.

**Anchors are trunk-bound.** These are `captionbert-8192-v2` anchors. Measured
2026-08-02, they lose 31% of their gain on the `-B` trunk (.7287 -> .6863), and
re-aligning the routing keys alone recovers only 29% of that. Retrained natively
on `-B` they reach .7294. Do not port these to another trunk -- retrain them
(~18 min).

All anchors disabled reproduces the bare trunk **bit-exact** (asserted at build
time). Masking never renormalizes -- that is the damping law, not an oversight.

Anchor `.pt` key layout is `{block}.{param}`. (`blocks.{site}.{param}` in the
amoe README is the *safetensors* layout, a different serializer.)

## Training

Anchors: MNRL, in-batch + hard negatives where the source provides them, 1,500
steps at batch 256, pure Adam wd=0 (`amoe.laws.make_optimizer`), fp32 / TF32 off
(`amoe.laws.pin_precision`). Trunk frozen throughout.

1,500 steps and not 4,000: the first solo run's STS-B **peaked at step 1,000**
and then fell .0178 while SICK-R kept climbing -- 56,825 distinct anchors behind
200,000 draws (space/draws .284).

Dispatch: 800 steps, routing keys only, anchors frozen, starvation safeguard
armed. tau 0.1, seed 0.

SICK-R and BIOSSES are never trained on and are the honest transfer reads.
Full configuration in `config.json`; every number above is in `metrics.json`.