> ## Documentation Index
> Fetch the complete documentation index at: https://docs.nimbusbci.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Research / PEFT

> Opt-in BaLoRA, LoRA/FiLM trunk arms, and research-only adaptation behind nimbus-bci[train].

<Warning>
  Everything on this page is an **opt-in research surface**. No certified accuracy claims. Production default remains frozen trunk + Bayesian head (`for_deployment`).
</Warning>

Install:

```bash theme={null}
pip install nimbus-bci[train]   # torch >= 2.14
```

## BaLoRA + composed uncertainty

Bayesian LoRA on the trunk with composed trunk+head BALD sampling:

```python theme={null}
from nimbus_bci import Personalizer, Recipe, MCSamplingConfig

# Auto-wraps a raw torch trunk (rank=4 default)
p = Personalizer.for_balora(torch_model, ["left", "right"], rank=4)
p.fit(X_cal, y_cal)
# Escalate trunk adaptation when your protocol calls for it:
# p.adapt_trunk(...)  # see API / AdaptArm registry

# Or Recipe.BALORA_COMPOSED / MCSamplingConfig.balora() presets:
# MCSamplingConfig.balora(n_trunk=8, n_head=32)
# MCSamplingConfig.balora_fast() / .balora_standard()
```

`mc_samples=(n_trunk, n_head)` marginalises over the BaLoRA α-posterior and the Bayesian head.

## Trunk-arm PEFT

| Tool                         | Role                                                        |
| ---------------------------- | ----------------------------------------------------------- |
| `wrap_lora`                  | LoRA on Linear + Conv2d                                     |
| FiLM rung                    | Feature-wise linear modulation arm                          |
| `AdaptArm` registry          | Named arms with measured cost estimates                     |
| `Personalizer.for_lora(...)` | Convenience factory when the trunk already has / needs LoRA |

```python theme={null}
from nimbus_bci.middleware.adapters.lora import wrap_lora
from nimbus_bci import Personalizer, available_arms

enc = wrap_lora(torch_model, ...)
p = Personalizer.for_research(enc, ["left", "right"])
print(available_arms())  # cost estimates for trainer arms
```

Trainer arms accept hyperparameter overrides. Prefer measuring cost on your hardware before comparing to head-only `partial_fit`.

## Other research-only mechanisms

| API                   | Role                                                             |
| --------------------- | ---------------------------------------------------------------- |
| `adapt_pseudo`        | Gated self-training on an unlabeled stream                       |
| `fit_tent`            | Entropy minimization over trunk norm affine params               |
| `T3APrototypeChannel` | Optimization-free recalibration on verified labels               |
| `adapt_errp`          | Soft-negative conjugate update from an external ErrP probability |
| `fit_population`      | Warm-start from pooled LOSO posteriors + prior-predictive gate   |

These ship for experimentation — confirmation-drift and protocol risks apply.

## Next read

<Columns cols={2}>
  <Card title="Evidence" icon="chart-line" href="/personalizer/evidence">
    When head-only beats FT on cost
  </Card>

  <Card title="Transforms" icon="sliders" href="/personalizer/transforms">
    Embedding-space alignment
  </Card>

  <Card title="Migration to 0.6" icon="arrow-right" href="/python-sdk/migration-0.6">
    Breaking changes and adoption map
  </Card>

  <Card title="API Reference" icon="book" href="/python-sdk/api-reference">
    Personalizer methods and configs
  </Card>
</Columns>