Configuration Gallery

Overview

Aspect	Details
Purpose	Quick pointers to common presets and overlays.
Audience	Users looking for ready-to-use configurations.
Note	Presets are repo assets, not shipped in wheels.
Source	`configs/presets/` and `configs/overlays/`.

Pointers to common presets in this repository you can start from. Presets are repo assets (not shipped in wheels). Use flag‑only invarlock evaluate when installing from PyPI, or clone this repo to reference these files.

Note: Adapter‑based flows such as invarlock evaluate and invarlock run with HF models require extras like invarlock[hf] or invarlock[adapters]. The core install (pip install invarlock) remains torch‑free.

Presets (Runnable)

Causal LM (decoder-only)

Preset	Use Case	Model Type	Dataset
`configs/presets/causal_lm/wikitext2_512.yaml`	Standard evaluation	Decoder-only causal	WikiText-2

When to use: Primary preset for causal language models. 512-token sequences provide good coverage while keeping runtime reasonable.

invarlock evaluate --baseline gpt2 --subject /path/to/edited \
  --preset configs/presets/causal_lm/wikitext2_512.yaml --profile ci

Masked LM (BERT, RoBERTa, etc.)

Preset	Use Case	Model Type	Dataset
`configs/presets/masked_lm/wikitext2_128.yaml`	Standard MLM evaluation	BERT/RoBERTa	WikiText-2
`configs/presets/masked_lm/synthetic_128.yaml`	Offline testing	BERT/RoBERTa	Synthetic

When to use: MLM presets for BERT-family models. Use synthetic preset when network access is unavailable or for CI smoke tests.

invarlock evaluate --baseline bert-base-uncased --subject /path/to/edited \
  --preset configs/presets/masked_lm/wikitext2_128.yaml --profile ci

Seq2Seq (T5, etc.)

Preset	Use Case	Model Type	Dataset
`configs/presets/seq2seq/synth_64.yaml`	Quick seq2seq tests	T5	Synthetic

When to use: Encoder-decoder models. Synthetic data keeps runs offline and fast for smoke testing.

Edit Overlays (Demo RTN Quantization)

These overlays apply the built-in quant_rtn edit for demonstration. For production, use Compare & evaluate (BYOE) with your own pre-edited checkpoint instead.

Overlay	Scope	Use Case
`configs/overlays/edits/quant_rtn/8bit_attn.yaml`	Attention layers only	Conservative quantization demo
`configs/overlays/edits/quant_rtn/8bit_full.yaml`	All linear layers	Full model quantization demo
`configs/overlays/edits/quant_rtn/tiny_demo.yaml`	Minimal layers	Quick smoke test

Example (demo edit):

invarlock evaluate --baseline gpt2 --subject gpt2 \
  --preset configs/presets/causal_lm/wikitext2_512.yaml \
  --edit-config configs/overlays/edits/quant_rtn/8bit_attn.yaml \
  --profile ci

Profiles

Profiles control window counts and bootstrap depth:

Profile	Windows	Bootstrap	Use Case
`ci`	200/200	1200	Standard CI evaluation
`release`	400/400	3200	Production releases
`ci_cpu`	120/120	1200	CPU-only environments

Tips

Use --profile ci|release|ci_cpu to apply runtime window counts and bootstrapping defaults.
Keep seq_len = stride for deterministic non‑overlapping windows.
Combine presets with edit overlays using multiple -c flags or --edit-config.
For custom data, see Bring Your Own Data.

Configuration Schema — All config options
CLI Reference — Command flags and profiles
Compare & evaluate (BYOE) — Production workflow
Dataset Providers — Available data sources