docs: v0.71.34 adapter algebra + LISA (version bump + CHANGELOG + docs)

CHANGELOG.md

@@ -12,6 +12,27 @@ reproducing 70+ versions of notes.
 
 ## [Unreleased]
 
+## [0.71.34] - 2026-07-15
+
+### Added
+- **`soup adapters arithmetic` — task-vector algebra over LoRA adapters (add / scale / negate).**
+  Apply task arithmetic (arXiv:2212.04089) to LoRA deltas via an expression such as
+  `"coder + 0.5*math - toxic"`, mapping names to adapter dirs with repeatable
+  `--adapter name=path`. Produces one merged adapter you can serve or merge.
+  - Signed, un-normalized element-wise combine over same-rank adapters; the effective
+    delta `ΔW = B @ A` scales **linearly** with each coefficient (negation flips the
+    delta, `0.5·` halves it) via a √|c| factor split — not the `c²` a naive sum gives.
+    Mixed-rank inputs are refused with a clear "harmonize rank" message.
+  - Reuses the backdoor-scan gate (refuses a FAIL-scanned input unless `--allow-unscanned`)
+    and a same-base-model check (`--allow-cross-base` to override). Hand-written expression
+    parser (no `eval`), cwd-contained/symlink-rejecting paths, exit 0 = ok / 1 = refusal.
+- **LISA — Layerwise Importance Sampled AdamW (arXiv:2403.17919).** Full-fine-tuning
+  quality at LoRA-like memory: every N steps LISA freezes all decoder layers except a
+  small random set (embeddings + head always trainable). Enable with
+  `training.lisa_enabled: true` (+ `lisa_num_layers`, `lisa_interval_steps`) on a
+  `task: sft`, transformers, text, `quantization: none` run; mutually exclusive with
+  LoRA features and the other freeze mechanisms. Live on a 4 GB GPU for small models.
+
 ## [0.71.33] - 2026-07-13
 
 ### Added

CONTRIBUTING.md

@@ -120,7 +120,7 @@ src/soup_cli/
   templates/          - 17 built-in soup.yaml templates (YAML + manifest.json) with load_template loader (v0.39.0, +bco v0.40.0)
   ui/                 - Web UI (FastAPI + HTML/JS SPA)
 
-tests/                - Test suite (311 files, 15806 tests)
+tests/                - Test suite (312 files, 15906 tests)
 examples/             - Real-world config examples and datasets
 ```
 

README.md

@@ -49,47 +49,40 @@ infrastructure instead of improving models. Soup fixes that.
 
 ## What's New
 
-**v0.71.33 — `soup draft`: know whether speculative decoding is actually worth it.** Everyone tells you to bolt a draft model onto your server for a free speedup. Nobody tells you to *measure* it first. Now you can.
+**v0.71.34 — adapter algebra + LISA.** Compose fine-tunes like vectors, and train big-model quality on a small-model memory budget.
 
-- **`soup draft measure`.** Reports a draft's **acceptance rate** — the fraction of your target's
-  own greedy tokens the draft would have proposed correctly — plus **real plain-vs-assisted
-  tok/s**. Exit 0 / 2 (below `--min-acceptance`) / 1, so CI can gate on it.
-- **`soup draft distill`.** Distils your tuned target into a tiny draft base (logit KD over the
-  existing `task: distill` trainer) and emits a **dense** model, loadable straight as an
-  `assistant_model`.
-- **Auto-wired into serving.** Drafts land in a local registry that `soup serve --auto-spec`
-  consults *before* the built-in pairing table — so a draft you trained yourself just gets used.
-- **What the measurement actually told us (honestly).** On `SmolLM2-360M-Instruct` ←
-  `SmolLM2-135M-Instruct`: the stock draft already scored **69.3%**, and distilling it changed
-  nothing (69.7% at 2 epochs, 69.3% at 10). Assisted decoding was a **net slowdown** (0.55–0.64×).
-  A small same-family draft is already at its ceiling. **That negative result is the feature
-  working** — it's the number you want *before* you ship speculative decoding, not after.
-  Whether distillation pays off on a larger or genuinely diverged pair is unproven on a 4 GB box.
+- **`soup adapters arithmetic "coder + 0.5*math - toxic"`.** Task-vector algebra over LoRA
+  adapters (arXiv:2212.04089) — **add, scale, and NEGATE** trained behaviours into one merged
+  adapter. The math is done right: a LoRA's effective delta `ΔW = B·A` scales *linearly* with
+  each coefficient (subtracting an adapter actually removes its behaviour), not quadratically.
+  Same-base + backdoor-scan gated; mixed ranks refused with a clear message.
+- **LISA — layerwise importance sampling (`training.lisa_enabled`).** Full-fine-tuning quality
+  at LoRA-like memory (arXiv:2403.17919): every N steps LISA re-activates a small random set of
+  decoder layers (embeddings + head always on) and freezes the rest. Live on a 4 GB GPU.
+- **Verified on real models (RTX 3050).** LISA trains SmolLM2-135M end-to-end; `2·a` produces an
+  adapter whose ΔW is *exactly* 2× the original's — the merged adapter loads and serves.
+
+```bash
+soup adapters arithmetic "coder - toxic" \
+  --adapter coder=./coder-lora --adapter toxic=./toxic-lora -o ./cleaned
+
+soup train --config sft.yaml   # with training.lisa_enabled: true, lisa_num_layers: 2
+```
+
+<details>
+<summary>Previous release — v0.71.33, <code>soup draft</code> (measure speculative decoding)</summary>
+
+`soup draft measure` reports a draft model's **acceptance rate** + real plain-vs-assisted tok/s
+(exit 0/2/1 for CI); `soup draft distill` distils your target into a dense tiny draft, auto-wired
+into `soup serve --auto-spec`. The honest result on a small same-family pair: distillation didn't
+move acceptance (69.3% → 69.3%) and assisted decoding was a net slowdown — which is exactly the
+number you want *before* shipping speculative decoding.
 
 ```bash
 soup draft measure --target ./my-tuned-model --draft HuggingFaceTB/SmolLM2-135M-Instruct \
   --prompts prod-prompts.jsonl        # -> acceptance %, real tok/s, ship-or-not
 ```
 
-<details>
-<summary>Previous release — v0.71.32, ASR fine-tuning (Whisper)</summary>
-
-Fine-tune Whisper on your accent or domain, locally: `task='asr'` (`AsrTrainerWrapper` over HF
-`Seq2SeqTrainer` + `WhisperProcessor`), `soup infer --task asr` with per-row + corpus **WER/CER**,
-pure-python metrics in `soup_cli.utils.asr_metrics`, and 4 new recipes (catalog 138 → 142).
-whisper-tiny (39M) / base (74M) train on a 4 GB GPU.
-
-```yaml
-base: openai/whisper-tiny
-task: asr
-data:
-  format: asr            # rows: {"audio": "clip.wav", "text": "hello world"}
-  audio_dir: ./data/audio
-training:
-  asr_language: en
-  asr_lora: true         # optional; default = full fine-tune
-```
-
 </details>
 
 Full history: [CHANGELOG.md](CHANGELOG.md) &middot; [GitHub Releases](https://github.com/MakazhanAlpamys/Soup/releases).

docs/adapters-and-governance.md

@@ -270,6 +270,12 @@ soup adapters merge ./run-v17 ./run-v18 ./run-v19 -o ./merged --strategy ties \
 soup adapters merge ./run-v17 ./run-v18 -o ./merged --strategy dare \
   --density 0.5 --seed 42
 
+# Task-vector arithmetic (v0.71.34) — add / scale / NEGATE trained behaviours
+# into one adapter (arXiv:2212.04089). Names map to dirs via --adapter name=path.
+soup adapters arithmetic "coder + 0.5*math - toxic" \
+  --adapter coder=./coder-lora --adapter math=./math-lora \
+  --adapter toxic=./toxic-lora -o ./blended
+
 # Leave-one-out ablation plan against a 4-hour wall-clock budget
 soup adapters blame ./run-v18 --dataset train.jsonl --layer q_proj.7 \
   --budget 4h --shards 10 --plan-only
@@ -314,6 +320,8 @@ soup adapters branches
 
 **v0.71.4:** the merge verdict is now LIVE — `soup adapters merge … --canary suite.json` lifts the `MergeReport.verdict` `UNKNOWN` stub. A pre-scored `{"baseline_scores","candidate_scores"}` suite classifies the blend OK / MINOR / MAJOR with no model load; a `{"tasks":[...]}` suite uses an injectable scorer. `--strict-verdict` exits 2 on MAJOR. The backdoor-scan and license-conflict gates now run for **every** strategy, including `cmaes`. `soup adapters branch <name> --from-registry <id>` / `--attach-to-registry <id>` link training-env snapshots into the Registry lineage DAG (shown as a `branches` node in `soup history`).
 
+**v0.71.34 — task-vector arithmetic.** `soup adapters arithmetic "<expr>"` applies task arithmetic (arXiv:2212.04089) to LoRA deltas: **add** (blend two skills), **scale** (`2*coder`), and — the differentiator — **negate** (`- toxic` removes a behaviour). Names in the expression map to adapter dirs via repeatable `--adapter name=path`. The math is done at the effective-delta level: a LoRA contributes `ΔW = B·A`, so the coefficient is applied so that `ΔW` scales **linearly** (subtracting an adapter actually negates its delta — a naïve element-wise sum would scale by `c²`, making negation a no-op). Adapters must share the base model (`--allow-cross-base` to override) and rank (mixed ranks are refused, not silently approximated). Each input passes the v0.71.2 backdoor-scan gate (`--allow-unscanned` to skip). Output is a single loadable adapter; exit 0 = ok, 1 = refusal.
+
 
 ## Soup Cans (Shareable Recipes)
 

docs/commands.md

@@ -27,6 +27,7 @@ soup merge --adapter ./output                 Merge LoRA with base model
 soup merge-sharded-fsdp-weights ./shards -o merged.safetensors  Consolidate FSDP shards into one safetensors (v0.71.14; --plan-only previews)
 soup delinearize-llama4 ./src --target ./out [--num-experts N] [--plan-only]  Live Llama-4 fused-expert reshape [E*din,dout] -> [E,din,dout] + sidecar copy (v0.71.21)
 soup spectrum scan --model <id|path> --top-percent 50 [--modules mlp,attn] [-o patch.yaml]  Spectrum SNR scan (no model load) -> training.unfrozen_parameters YAML patch (v0.71.23)
+soup train --config sft.yaml  # training.lisa_enabled: true [lisa_num_layers lisa_interval_steps]  LISA layerwise importance sampling — full-FT quality at LoRA-like memory (sft/transformers/text/quantization=none) (v0.71.34)
 soup export --model ./output --format gguf    Export to GGUF (Ollama)
 soup export --model ./output --deploy ollama  Export GGUF + auto-deploy to Ollama
 soup export --model ./output --format onnx    Export to ONNX
@@ -184,6 +185,7 @@ soup adapters sign <adapter> [--backend unsigned|ed25519] [--key <pem>|--generat
 soup adapters verify <adapter> [--strict] [--public-key <pem>]  Verify manifest + ed25519 signature
 soup adapters check-safetensors <adapter> [--strict]  Refuse pickle / PyTorch-classic weights
 soup adapters merge ... [--license <id>] [--license-override <reason>] [--allow-unscanned]  License + backdoor-scan gates (auto-detect license; scan FAIL refused)
+soup adapters arithmetic "coder + 0.5*math - toxic" --adapter coder=<p> --adapter math=<p> --adapter toxic=<p> -o <out> [--allow-unscanned --allow-cross-base]  Task-vector algebra over LoRA adapters (add/scale/negate; same-rank; scan + same-base gated) (v0.71.34)
 soup attest emit ... [--sign ed25519 --key <pem>] [-o att.json]  in-toto/SLSA-3 attestation (+ .sig sidecar)
 soup attest verify <statement> --signature <sig> [--public-key <pem>]  Verify ed25519 attestation signature
 soup airgap-bundle --model <m> --output <out.tar> [--repro-receipt <r.json>]  Signed tarball for data-diode transfer (embeds repro-receipt)

docs/peft-and-efficiency.md

@@ -380,6 +380,25 @@ training:
 
 Works with and without LoRA. When used with LoRA, LoRA is applied only to unfrozen layers.
 
+## LISA — Layerwise Importance Sampling (v0.71.34)
+
+LISA (Layerwise Importance Sampled AdamW, [arXiv:2403.17919](https://arxiv.org/abs/2403.17919)) targets full-fine-tuning quality at LoRA-like memory. Instead of picking layers once (that's Spectrum's static `unfrozen_parameters`), LISA re-samples a small random set of decoder layers **every N steps** and freezes the rest; the input embeddings, the LM head, and the final norm stay trainable throughout.
+
+```yaml
+task: sft
+backend: transformers
+modality: text
+training:
+  quantization: none      # LISA is full-FT of the active layers
+  lisa_enabled: true
+  lisa_num_layers: 2       # decoder layers active per interval (clamped to model depth)
+  lisa_interval_steps: 20  # re-sample cadence, in global steps
+```
+
+Because only a handful of layers train at any moment (and their optimizer state is cleared when they're re-frozen), peak optimizer memory is roughly `embeddings + head + lisa_num_layers` — far below a full fine-tune, while every layer still gets updated over the course of training. LISA is `sft` + `transformers` + `text` + `quantization: none` only, and is mutually exclusive with LoRA features, `freeze_layers`/`freeze_ratio`, and Spectrum's `unfrozen_parameters` (each independently decides what trains).
+
+Implementation note: the model is left fully trainable at trainer-setup time so HF's optimizer (built before the first callback fires) contains every decoder parameter; the LISA callback then toggles `requires_grad` per interval — frozen parameters produce no gradient and the optimizer skips them.
+
 
 ## Loss Watchdog
 

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "hatchling.build"
 
 [project]
 name = "soup-cli"
-version = "0.71.33"
+version = "0.71.34"
 description = "Fine-tune and post-train LLMs in one command. No SSH, no config hell."
 readme = "README.md"
 license = "Apache-2.0"

src/soup_cli/__init__.py

@@ -1,3 +1,3 @@
 """Soup CLI — Fine-tune and post-train LLMs in one command."""
 
-__version__ = "0.71.33"
+__version__ = "0.71.34"