[client] initialize (running) [client] session/new (running) I’ll use the `gsd` skill here because this is a multi-part audit: first I’ll read the local implementation and references, then verify URLs/snapshots, then review the two harnesses and collapse it into the requested verdict tables. [tool] Read SKILL.md (running) input: {"call_id":"call_hMhlWCMXk8cPaOEh4mCYl3wE","process_id":"55868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gsd/SKILL.md"],"cwd":"/me... files: /home/wassname/.claude/skills/gsd/SKILL.md [tool] Read SKILL.md (completed) kind: read input: {"call_id":"call_hMhlWCMXk8cPaOEh4mCYl3wE","process_id":"55868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gsd/SKILL.md"],"cwd":"/me... files: /home/wassname/.claude/skills/gsd/SKILL.md output: --- name: gsd description: "Get Shit Done workflow for non-trivial tasks: spec -> implement -> test -> review -> wrap. Mandatory for anything with more than one step." --- # GSD (Get Shit Done) Mandatory for all non-trivial tasks (more than one step). Skip only for single-line fixes. ## Part 1: Core workflow ### 1.1 Spec-first planning ... (192 more lines) [plan] - [in_progress] Inventory source, refs, tests - [pending] Verify external links and snapshot drift - [pending] Audit each variant against refs - [pending] Assess smoke and Qwen probe validity - [pending] Assemble concise findings with line citations I’m starting with a source inventory: variant implementations, runtime attach/load code, local reference snapshots, and the two harnesses. The key thing I’m extracting is line-numbered evidence so the final review can stay specific rather than hand-wavy. [tool] List /media/wassname/SGIronWolf/projects5/2026/lora-lite/refs (running) input: {"call_id":"call_WTlnC2ndhh62jn4E6rDNB6nY","process_id":"48507","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","find docs/refs -maxdepth 2 -type f"],"cwd":"/media/wassname/SGIronWolf/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/refs [tool] List /media/wassname/SGIronWolf/projects5/2026/lora-lite/lora_lite (running) input: {"call_id":"call_wXC8wsjCbDji5rdKyPNA6LrT","process_id":"22303","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","rg --files src/lora_lite docs/refs tests scripts"],"cwd":"/media/wassna... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/lora_lite [tool] Run git status --short (running) input: {"call_id":"call_FFOxlyCNApreJiKkClbo9Hxl","process_id":"78605","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","git status --short"],"cwd":"/media/wassname/SGIronWolf/projects5/2026/l... [tool] Run git status --short (completed) kind: execute input: {"call_id":"call_FFOxlyCNApreJiKkClbo9Hxl","process_id":"78605","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","git status --short"],"cwd":"/media/wassname/SGIronWolf/projects5/2026/l... output: M pyproject.toml M src/lora_lite/__init__.py M src/lora_lite/config.py M src/lora_lite/variants/antipasto.py M src/lora_lite/variants/delora.py M src/lora_lite/variants/dora.py M src/lora_lite/variants/eva.py M src/lora_lite/variants/hra.py M src/lora_lite/variants/ia3.py M src/lora_lite/variants/lora.py M src/lora_lite/variants/pissa.py M uv.lock ?? docs/audit/REVIEW_PROMPT_VARIANTS_V4.md ?? docs/audit/variants_review_v4.md [tool] List /media/wassname/SGIronWolf/projects5/2026/lora-lite/refs (completed) kind: search input: {"call_id":"call_WTlnC2ndhh62jn4E6rDNB6nY","process_id":"48507","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","find docs/refs -maxdepth 2 -type f"],"cwd":"/media/wassname/SGIronWolf/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/refs output: find: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by find) docs/refs/peft_delora_layer.py docs/refs/peft_lora_layer.py docs/refs/peft_eva.py docs/refs/baukit_nethook.py docs/refs/orig_pissa_init.py docs/refs/peft_lora_dora.py docs/refs/peft_hra_layer.py docs/refs/antipasto3_svd_adapter.py docs/refs/orig_delora.py docs/refs/peft_lora_variants.py docs/refs/peft_ia3_layer.py docs/refs/orig_hra_layer.py docs/refs/peft_eva_finetuning.py [tool] List /media/wassname/SGIronWolf/projects5/2026/lora-lite/lora_lite (completed) kind: search input: {"call_id":"call_wXC8wsjCbDji5rdKyPNA6LrT","process_id":"22303","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","rg --files src/lora_lite docs/refs tests scripts"],"cwd":"/media/wassna... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/lora_lite output: tests/test_lora_lite.py tests/smoke.py tests/_artifacts/hra_adapter.pt tests/_artifacts/delora_smoke_adapter.pt tests/_artifacts/lora_unexpected.pt tests/_artifacts/ia3_smoke_adapter.pt tests/_artifacts/lora_smoke_adapter.pt tests/_artifacts/lora_missing.pt tests/_artifacts/dora_smoke_adapter.pt tests/_artifacts/eva_smoke_adapter.pt tests/_artifacts/hra_smoke_adapter.pt tests/_artifacts/pissa_smoke_adapter.pt tests/_artifacts/dora_adapter.pt tests/_artifacts/lora_good.pt tests/_artifacts/delora_adapter.pt tests/_artifacts/lora_adapter.pt tests/_artifacts/pissa_adapter.pt tests/_artifacts/antipasto_smoke_adapter.pt tests/_artifacts/ia3_adapter.pt scripts/qwen_train_probe.py src/lora_lite/__init__.py src/lora_lite/variant.py src/lora_lite/config.py src/lora_lite/adapter.py docs/refs/peft_eva_finetuning.py docs/refs/orig_hra_layer.py docs/refs/peft_ia3_layer.py docs/refs/peft_lora_variants.py ... (19 more lines) [tool] Read variant.py (running) input: {"call_id":"call_oHYOvEKikwSmKCy4dhjfCInI","process_id":"73582","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variant.py"],"cwd":"/media/wassname/SGIronWolf/pro... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py [tool] Read config.py (running) input: {"call_id":"call_6WRPNCijW4RyEvz1cHydfmnj","process_id":"3191","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/config.py"],"cwd":"/media/wassname/SGIronWolf/proje... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py [tool] Read adapter.py (running) input: {"call_id":"call_6B9nl0I4kqlNBxHAeqADPA71","process_id":"94068","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/adapter.py"],"cwd":"/media/wassname/SGIronWolf/pro... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py [tool] Read target.py (running) input: {"call_id":"call_KrDjqr1FquYfne814Qxkvot0","process_id":"55044","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/target.py"],"cwd":"/media/wassname/SGIronWolf/proj... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py [tool] Read variant.py (completed) kind: read input: {"call_id":"call_oHYOvEKikwSmKCy4dhjfCInI","process_id":"73582","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variant.py"],"cwd":"/media/wassname/SGIronWolf/pro... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py output: 1 """Variant protocol + registry. Variants own (x, layer.weight, layer.lora_*) -> y_new.""" 2 from dataclasses import dataclass 3 from typing import Callable, Protocol, Any 4 import torch 5 from torch import nn 6 7 from .config import LoraLiteConfig 8 9 10 @dataclass 11 class ParamSpec: 12 shape: tuple[int, ...] 13 init: str | Callable[[torch.Tensor], None] = "zeros" # 'zeros'|'kaiming'|'ones'|callable(t) 14 trainable: bool = True 15 as_buffer: bool = False # if True, register_buffer instead of register_parameter 16 17 def _empty(self, dtype: torch.dtype, device) -> torch.Tensor: 18 t = torch.empty(self.shape, dtype=dtype, device=device) 19 if callable(self.init): 20 self.init(t) 21 elif self.init == "zeros": 22 t.zero_() 23 elif self.init == "ones": 24 t.fill_(1.0) 25 elif self.init == "kaiming": 26 # match nn.Linear default: kaiming_uniform_(a=sqrt(5)) 27 nn.init.kaiming_uniform_(t, a=5 ** 0.5) if t.ndim >= 2 else t.normal_(0, 0.02) 28 else: ... (39 more lines) [tool] Read config.py (completed) kind: read input: {"call_id":"call_6WRPNCijW4RyEvz1cHydfmnj","process_id":"3191","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/config.py"],"cwd":"/media/wassname/SGIronWolf/proje... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py output: 1 from dataclasses import dataclass, field, asdict 2 from typing import Any 3 import torch 4 5 6 @dataclass 7 class LoraLiteConfig: 8 variant: str = "lora" 9 r: int = 8 10 alpha: float | int = 16.0 11 dtype: torch.dtype = torch.bfloat16 12 13 # targeting 14 target_roles: tuple[str, ...] = ("reader", "writer") 15 target_names: tuple[str, ...] = () 16 exclude_names: tuple[str, ...] = ("lm_head", "embed_tokens") 17 layers: tuple[int, ...] | None = None 18 19 # variant-specific bag (e.g. lambda0 for DeLoRA) 20 variant_kwargs: dict[str, Any] = field(default_factory=dict) 21 22 def to_dict(self) -> dict: 23 d = asdict(self) 24 d["dtype"] = str(self.dtype).removeprefix("torch.") 25 return d 26 27 @classmethod 28 def from_dict(cls, d: dict) -> "LoraLiteConfig": ... (9 more lines) [tool] Read adapter.py (completed) kind: read input: {"call_id":"call_6B9nl0I4kqlNBxHAeqADPA71","process_id":"94068","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/adapter.py"],"cwd":"/media/wassname/SGIronWolf/pro... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py output: 1 """attach / detach / save / load. The whole runtime.""" 2 from __future__ import annotations 3 import torch 4 from torch import nn 5 from torch.utils.hooks import RemovableHandle 6 7 from .config import LoraLiteConfig 8 from .variant import REGISTRY 9 from .target import find_targets 10 11 12 _ATTACHED_ATTR = "_lora_lite_attached" 13 14 15 def _hook(layer, args, y): 16 (x,) = args 17 cfg: LoraLiteConfig = layer._lora_cfg 18 x_cast = x.to(cfg.dtype) 19 out = layer._lora_variant.forward(layer, x_cast, y) 20 return out.to(y.dtype) 21 22 23 def _pre_hook(layer, args): 24 (x,) = args 25 cfg: LoraLiteConfig = layer._lora_cfg 26 x_cast = x.to(cfg.dtype) 27 x_new = layer._lora_variant.forward_input(layer, x_cast) 28 return (x_new.to(x.dtype),) ... (126 more lines) [tool] Read target.py (completed) kind: read input: {"call_id":"call_KrDjqr1FquYfne814Qxkvot0","process_id":"55044","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/target.py"],"cwd":"/media/wassname/SGIronWolf/proj... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py output: 1 """Find linear-like targets by shape (reader/writer/inner) + name regex. 2 3 Structural matching is deliberate: bnb Linear4bit/8bitLt are not nn.Linear, but 4 they expose in_features/out_features/weight and their forward already handles 5 dequantization. 6 """ 7 import re 8 from torch import nn 9 10 11 def is_linear_like(m: nn.Module) -> bool: 12 return ( 13 hasattr(m, "in_features") 14 and hasattr(m, "out_features") 15 and hasattr(m, "weight") 16 and callable(m) 17 ) 18 19 20 def _layer_idx(name: str) -> int | None: 21 m = re.search(r"\.layers?\.(\d+)\.", name) 22 return int(m.group(1)) if m else None 23 24 25 def _classify(m: nn.Module, d_model: int, name: str) -> str: 26 di, do = m.in_features, m.out_features 27 if di == d_model and do != d_model: 28 return "reader" ... (31 more lines) [tool] Read lora.py (running) input: {"call_id":"call_Jh1Na1STB1xKq0EwNgOl2tMI","process_id":"97888","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/lora.py"],"cwd":"/media/wassname/SGIronWo... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py [tool] Read pissa.py (running) input: {"call_id":"call_ki80zmAxjTfxGnQFtc3gmXQU","process_id":"19824","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/pissa.py"],"cwd":"/media/wassname/SGIronW... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py [tool] Read delora.py (running) input: {"call_id":"call_pBUz8jzUuqsbkFPhBMFCfJgi","process_id":"82529","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/delora.py"],"cwd":"/media/wassname/SGIron... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/delora.py [tool] Read ia3.py (running) input: {"call_id":"call_yHMhcitdVwaR7eK4so94DuIX","process_id":"27826","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/ia3.py"],"cwd":"/media/wassname/SGIronWol... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/ia3.py [tool] Read lora.py (completed) kind: read input: {"call_id":"call_Jh1Na1STB1xKq0EwNgOl2tMI","process_id":"97888","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/lora.py"],"cwd":"/media/wassname/SGIronWo... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py output: 1 """Vanilla LoRA. Hu et al. 2021 https://arxiv.org/abs/2106.09685 2 3 h = W x + (alpha/r) B A x 4 5 Identity at t=0 from B=0. Faithful to the paper. 6 7 Reference implementations (for review/cross-check): 8 - peft Linear.update_layer + lora_A/B init, forward: 9 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py 10 (see docs/refs/peft_lora_layer.py for offline copy) 11 """ 12 from einops import einsum 13 from jaxtyping import Float 14 from torch import nn, Tensor as T 15 import torch 16 17 from ..variant import register, ParamSpec 18 19 20 @register 21 class LoRA: 22 name = "lora" 23 24 @staticmethod 25 def param_specs(d_in, d_out, cfg): 26 return { 27 "lora_A": ParamSpec((cfg.r, d_in), init="kaiming", trainable=True), 28 "lora_B": ParamSpec((d_out, cfg.r), init="zeros", trainable=True), ... (18 more lines) [tool] Read pissa.py (completed) kind: read input: {"call_id":"call_ki80zmAxjTfxGnQFtc3gmXQU","process_id":"19824","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/pissa.py"],"cwd":"/media/wassname/SGIronW... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py output: 1 """PiSSA: top-r SVD of W into A,B; replace W with W_res = W - B@A. 2 3 Meng et al. 2024 https://arxiv.org/abs/2404.02948 4 W_eff(t=0) = W_res + B@A = W (numerically; bf16 round-trip not bit-exact). 5 6 DEVIATION FROM PAPER (documented): 7 - Paper sets adapter scale = 1 (no alpha/r factor); we keep LoRA's alpha/r 8 pipeline so callers must pass alpha=r to get paper-faithful identity. 9 - Saved adapter does NOT include W_res (would double checkpoint size). Instead 10 `adapter.save` records a fingerprint of the post-init base weights and 11 `adapter.load` re-runs PiSSA init then verifies the fingerprint matches 12 -- so loading onto a different base weight raises loudly instead of 13 silently producing wrong outputs. 14 15 Reference implementations (for review/cross-check): 16 - PiSSA original (NeurIPS'24 spotlight) init script (SVD on dequant W): 17 https://github.com/MuLabPKU/PiSSA/blob/main/utils/init_pissa.py 18 (offline: docs/refs/orig_pissa_init.py) 19 - peft PiSSA flavor (init_lora_weights='pissa') in: 20 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py 21 (offline: docs/refs/peft_lora_layer.py, see pissa_init / loftq_init paths) 22 """ 23 import torch 24 from einops import einsum 25 from jaxtyping import Float 26 from torch import nn, Tensor as T 27 28 from ..variant import register, ParamSpec ... (48 more lines) [tool] Read delora.py (completed) kind: read input: {"call_id":"call_pBUz8jzUuqsbkFPhBMFCfJgi","process_id":"82529","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/delora.py"],"cwd":"/media/wassname/SGIron... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/delora.py output: 1 """DeLoRA: per-input-channel weight-norm scaling, per-rank A/B normalization. 2 3 Bini et al. 2025 (ICLR'25) https://arxiv.org/abs/2503.18225 4 5 Paper Eq. 8: W' = W + (lambda * ||W||_F / r) B Xi A 6 where Xi_{i,i} = 1 / (||b_i|| ||a_i||) makes each rank-1 component unit-norm. 7 8 Implementation follows the peft upstream (which the DeLoRA authors maintain), 9 which differs from the paper notation in two ways that are equivalent at the 10 forward level but matter for gradients/numerics: 11 1. ||W|| is captured PER INPUT CHANNEL (shape (d_in,)), not as a scalar 12 Frobenius norm. Used to scale `x` element-wise on the input dim. 13 See docs/refs/peft_delora_layer.py:150 (init) and :250 (forward). 14 2. Per-rank normalization applied via division (1/||A_i||*||B^j||) inside 15 the diagonal scaling, instead of as F.normalize on A,B themselves. 16 This keeps the gradient flowing through the un-normalized parameters. 17 18 Identity at t=0: lambda0=0 -> delta is exactly zero (bit-identity). 19 20 KNOWN GRADIENT ISSUE (flagged by external review 2026-04-26): 21 With lambda0=0 the *forward* is identity but `A,B` get zero gradient on step 0 22 (delta is proportional to lambda). Only `lora_lambda` moves first step. 23 The paper's true initialization (frozen-copy trick, Eq. 9) achieves both 24 identity AND non-zero A/B gradients; we do NOT implement it here. 25 26 Reference implementations: 27 - DeLoRA paper authors (ExplainableML/DeLoRA) -- their fork of peft: 28 https://github.com/ExplainableML/DeLoRA/blob/main/peft/src/peft/tuners/delora.py ... (61 more lines) [tool] Read ia3.py (completed) kind: read input: {"call_id":"call_yHMhcitdVwaR7eK4so94DuIX","process_id":"27826","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/ia3.py"],"cwd":"/media/wassname/SGIronWol... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/ia3.py output: 1 """IA3-style elementwise gating. Liu et al. 2022 https://arxiv.org/abs/2205.05638 2 3 Two registered variants, matching the paper's two regimes: 4 5 * `ia3` -- OUTPUT-side gating, parameter shape (d_out,). 6 y_new = y * g. Use for attention projections (k_proj, v_proj). 7 8 * `ia3_ff` -- INPUT-side gating, parameter shape (d_in,). 9 y_new = base_layer(x * g). Use for FFN-down layers (down_proj, 10 fc2). Equivalent to the paper's "gate the FFN intermediate (post- 11 activation)" position because down_proj's input IS that 12 intermediate hidden state. 13 14 In both cases g is initialized to 1 -> identity at t=0. 15 16 To match the paper exactly on a Llama/Qwen-style block requires TWO attach 17 passes (one per variant), since each variant uses one hook type: 18 19 cfg_attn = LoraLiteConfig(variant="ia3", target_names=(r"\\.k_proj$", r"\\.v_proj$")) 20 cfg_ffn = LoraLiteConfig(variant="ia3_ff", target_names=(r"\\.down_proj$",)) 21 22 Reference implementation: 23 - peft IA3 layer (is_feedforward toggles input-vs-output gating, see 24 docs/refs/peft_ia3_layer.py:177-188 forward and :214 update_layer): 25 https://github.com/huggingface/peft/blob/main/src/peft/tuners/ia3/layer.py 26 """ 27 import torch 28 from jaxtyping import Float ... (44 more lines) [tool] Read hra.py (running) input: {"call_id":"call_5dApBuiwrxILvOHq0UsAWrR6","process_id":"43282","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/hra.py"],"cwd":"/media/wassname/SGIronWol... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/hra.py [tool] Read dora.py (running) input: {"call_id":"call_bY1P6NZAFCUJK0gMBKDoSogD","process_id":"88868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/dora.py"],"cwd":"/media/wassname/SGIronWo... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/dora.py [tool] Read antipasto.py (running) input: {"call_id":"call_yooJHvjyZ7ccdKbRP1LnXWRo","process_id":"90297","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/antipasto.py"],"cwd":"/media/wassname/SGI... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/antipasto.py [tool] Read eva.py (running) input: {"call_id":"call_mHqxO4d6Ysrff17whtDJcMmv","process_id":"5302","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/eva.py"],"cwd":"/media/wassname/SGIronWolf... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/eva.py [tool] Read hra.py (completed) kind: read input: {"call_id":"call_5dApBuiwrxILvOHq0UsAWrR6","process_id":"43282","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/hra.py"],"cwd":"/media/wassname/SGIronWol... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/hra.py output: 1 """HRA: Householder Reflection Adaptation. Yuan et al. 2024 https://arxiv.org/abs/2405.17484 2 3 Paper formulation (Sec. 3): adapt each frozen weight as 4 5 W' = W R, R = prod_{i=1..r} H_i, H_i = I - 2 u_i u_i^T / ||u_i||^2 6 7 so the layer output becomes y' = W' x = W (R x). R is in INPUT space (d_in x d_in). 8 9 We implement this via a `forward_input` pre-hook that returns `R x`, then the 10 frozen base layer (including bnb 4/8-bit Linear) computes `W (R x)` itself. 11 12 Identity at t=0 (PEFT-style symmetric init, requires even r): 13 Rows are kaiming-init in pairs: U[0]=U[1], U[2]=U[3], ... Adjacent pairs of 14 Householder reflections with identical vectors cancel exactly 15 (H_i H_i = I), so R = I at init -> y' = y to bit-precision. 16 After the first gradient step the paired rows diverge and the chain becomes a 17 general orthogonal matrix; gradient flows into U from step 0 (no dead-grad). 18 Odd r is rejected (matches peft warning behaviour). 19 20 OMITTED: paper also adds an orthogonality regularizer (Eq. 6 / Sec. 3.3), 21 a loss-side term. Add it in your training loop if you want regularized HRA. 22 23 Reference implementations (for review/cross-check): 24 - HRA paper authors (DaShenZi721/HRA), llama variant of OFT layer with HRA: 25 https://github.com/DaShenZi721/HRA/blob/master/llama/peft/oft/layer_GS_HRA.py 26 (offline: docs/refs/orig_hra_layer.py) 27 - peft HRA layer, reset_hra_parameters (lines 100-108): 28 https://github.com/huggingface/peft/blob/main/src/peft/tuners/hra/layer.py ... (54 more lines) [tool] Read dora.py (completed) kind: read input: {"call_id":"call_bY1P6NZAFCUJK0gMBKDoSogD","process_id":"88868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/dora.py"],"cwd":"/media/wassname/SGIronWo... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/dora.py output: 1 """DoRA: weight-decomposed LoRA. Liu et al. 2024 https://arxiv.org/abs/2402.09353 2 3 W' = m * V / ||V||_c where V = W + (alpha/r) B A (||.||_c = per-output-row L2 norm) 4 5 At t=0: B=0 -> V=W -> y_new = (m_init / ||W||_c) (Wx + 0) = Wx when m_init = ||W||_c. 6 7 Limitation: requires materializing the dense weight to compute ||V||_c. v1 supports 8 plain nn.Linear only; bnb 4/8-bit layers raise loudly. 9 10 DEVIATION (numerical): 11 - We differentiate through ||V||_c every forward. The paper's sec. 4.3 suggests 12 a 'cost-saving' variant that detaches ||V|| in backward (gradient only flows 13 through V); we do NOT do that. Real impact: slower step, slightly different 14 gradient direction. Faithful to the eq.5 forward, not the optimized one. 15 16 Reference implementations (for review/cross-check): 17 - peft DoRA (separate file under lora/): 18 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/dora.py 19 (offline: docs/refs/peft_lora_dora.py) 20 """ 21 import torch 22 from einops import einsum 23 from jaxtyping import Float 24 from torch import nn, Tensor as T 25 26 from ..variant import register, ParamSpec 27 28 ... (46 more lines) [tool] Read antipasto.py (completed) kind: read input: {"call_id":"call_yooJHvjyZ7ccdKbRP1LnXWRo","process_id":"90297","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/antipasto.py"],"cwd":"/media/wassname/SGI... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/antipasto.py output: 1 """AntiPaSTO: SVD steering with learnable singular-value deltas + block-diagonal Cayley rotation. 2 3 Lite port of wassname's AntiPaSTO3 SVD adapter (research code, not an 4 upstream peft variant). Reference: 5 https://github.com/wassname/antipasto3 (offline: docs/refs/antipasto3_svd_adapter.py) 6 7 Decomposition (PyTorch nn.Linear convention, weight (d_out, d_in)): 8 9 W = U diag(S) Vh + W_res (top-r SVD; W_res = W - U_r diag(S_r) Vh_r) 10 11 We freeze U, S, Vh, W_res and learn: 12 - delta_s : (r,) -- additive delta to singular values 13 - rot_T : (n_blocks, bs(bs-1)/2) -- upper-triangle of skew matrix per block 14 15 Forward (matches base layer convention exactly at t=0): 16 17 R = block_diag(Cayley(skew(rot_T))) # (r, r) effective 18 Vh_rot = R @ Vh # rotates input basis 19 S_eff = S + delta_s # learnable spectrum 20 delta_y = ((x @ Vh_rot.T) * S_eff) @ U.T # rank-r path 21 base_y = x @ W_res.T # frozen residual 22 y_total = base_y + delta_y # == original output at t=0 23 24 At init: rot_T = 0 -> R = I -> Vh_rot = Vh, delta_s = 0 -> S_eff = S, so 25 delta_y reconstructs the truncated SVD term and y_total == x @ W^T to numerical 26 precision (fp32 SVD round-tripped to cfg.dtype). 27 28 WHICH BASIS IS ROTATED: ... (121 more lines) [tool] Read eva.py (completed) kind: read input: {"call_id":"call_mHqxO4d6Ysrff17whtDJcMmv","process_id":"5302","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba src/lora_lite/variants/eva.py"],"cwd":"/media/wassname/SGIronWolf... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/eva.py output: 1 """EVA: Explained-Variance Adaptation. Paischer et al. 2024. 2 3 Paper: https://arxiv.org/abs/2410.07170 (also referred to as ICLR'25 EVA). 4 5 Idea: instead of random A and zero B (LoRA) or SVD of W (PiSSA), initialize 6 `lora_A` to the top-r right singular vectors of the LAYER INPUT distribution 7 on a small calibration set. Forward = `y + scale * (B @ A @ x)` exactly like 8 LoRA; with `lora_B = 0` the adapter is identity at t=0. Only B trains 9 afterwards (A frozen). The result: each rank slot points along a direction 10 that actually carries information at this layer. 11 12 This is a stripped-down EVA; we do NOT implement: 13 - rank redistribution across layers via explained-variance ratios 14 (peft EVA computes an explained_variance_ratio per layer then redistributes 15 the global rank budget; we use a uniform `cfg.r` per layer). 16 - Incremental PCA over many micro-batches (we run one full SVD on the 17 pooled calibration activations per layer). 18 - Equal-input deduplication (peft hashes inputs to share SVD across QKV). 19 20 API stress-test: this variant requires data-driven init, so it implements 21 `group_init(model, targets, cfg, calibration_data)` to drive a single forward 22 pass on `calibration_data` with hooks that capture each target's input. 23 24 Identity at t=0: `lora_B = 0` -> delta = 0 -> y unchanged. 25 26 References: 27 - peft EVA (full impl, with IncrementalPCA + redistribution): 28 https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/eva.py ... (98 more lines) The runtime freezes every existing model parameter before installing adapter params, and hook dispatch casts adapter inputs to `cfg.dtype` then casts back to the base output dtype. That cast path is going to matter for every dtype verdict, especially bf16 identity claims. [tool] Read smoke.py (running) input: {"call_id":"call_Vt6FggawETJefJCZf3IhuKcg","process_id":"10277","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba tests/smoke.py"],"cwd":"/media/wassname/SGIronWolf/projects5/202... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/tests/smoke.py [tool] Read qwen_train_probe.py (running) input: {"call_id":"call_I0OJJCuss3rKB0LfwlTtCiIX","process_id":"80757","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba scripts/qwen_train_probe.py"],"cwd":"/media/wassname/SGIronWolf/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/scripts/qwen_train_probe.py [tool] Read smoke.py (completed) kind: read input: {"call_id":"call_Vt6FggawETJefJCZf3IhuKcg","process_id":"10277","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba tests/smoke.py"],"cwd":"/media/wassname/SGIronWolf/projects5/202... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/tests/smoke.py output: 1 """Smoke test: current variants on a tiny synthetic transformer-like model. 2 3 Verifies: 4 1. Identity at t=0 (delta ~ 0, output close to base). 5 2. Save/load round-trip preserves outputs. 6 3. A few SGD steps reduce a random loss (gradients flow). 7 8 Run: 9 cd lora-lite 10 python -m pip install -e . 11 python tests/smoke.py 12 13 BLUF format: 14 SHOULD: max|y_adapter - y_base| < tol_init for all variants. ELSE init or hook bug. 15 SHOULD: loss decreases > 5% over 20 SGD steps for all variants. ELSE grad/wiring bug. 16 """ 17 from __future__ import annotations 18 import argparse 19 import os, sys, math 20 from pathlib import Path 21 import torch 22 from torch import nn 23 24 # allow running as `python tests/smoke.py` without install 25 sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src")) 26 27 import lora_lite as ll # noqa: E402 28 ... (366 more lines) [tool] Read qwen_train_probe.py (completed) kind: read input: {"call_id":"call_I0OJJCuss3rKB0LfwlTtCiIX","process_id":"80757","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba scripts/qwen_train_probe.py"],"cwd":"/media/wassname/SGIronWolf/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/scripts/qwen_train_probe.py output: 1 from __future__ import annotations 2 3 import argparse 4 import gc 5 import math 6 from pathlib import Path 7 8 import torch 9 from tabulate import tabulate 10 from transformers import AutoModelForCausalLM, AutoTokenizer 11 12 import lora_lite as ll 13 14 15 PROMPT = "LoRA-lite probe: Paris is the capital of France. The answer is" 16 EXPECTED_TARGETS = { 17 "model.layers.0.self_attn.q_proj", 18 "model.layers.0.self_attn.v_proj", 19 } 20 21 22 def cfg_for_variant(variant: str, dtype: torch.dtype, r: int, alpha: float) -> ll.LoraLiteConfig: 23 return ll.LoraLiteConfig( 24 variant=variant, 25 r=r, 26 alpha=r if variant == "pissa" else alpha, 27 dtype=dtype, 28 target_roles=(), ... (180 more lines) [tool] Search pissa|PiSSA|reset_lora|kaiming|lora_A|lora_B|scaling|DoRA|Dora|eva|IA3|ia3|hra|HRA|delora|DeLoRA|lambda|weight_norm|svd|Singular|Householder|lora_magnitude_vector|magnitude in peft_lora_layer.py (running) input: {"call_id":"call_iAPLKeevRKI2FhcpcZM4ZWfl","process_id":"72662","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","rg -n \"pissa|PiSSA|reset_lora|kaiming|lora_A|lora_B|scaling|DoRA|Dora|... [tool] Run wc -l docs/refs/*.py (running) input: {"call_id":"call_bdEgZthDlRra2IlaA2Igs8Oa","process_id":"80034","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","wc -l docs/refs/*.py"],"cwd":"/media/wassname/SGIronWolf/projects5/2026... [tool] Search pissa|PiSSA|reset_lora|kaiming|lora_A|lora_B|scaling|DoRA|Dora|eva|IA3|ia3|hra|HRA|delora|DeLoRA|lambda|weight_norm|svd|Singular|Householder|lora_magnitude_vector|magnitude in peft_lora_layer.py (completed) kind: search input: {"call_id":"call_iAPLKeevRKI2FhcpcZM4ZWfl","process_id":"72662","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","rg -n \"pissa|PiSSA|reset_lora|kaiming|lora_A|lora_B|scaling|DoRA|Dora|... output: docs/refs/peft_lora_dora.py:28:"""Whether to enable DoRA caching, which makes it faster at inference but requires more memory""" docs/refs/peft_lora_dora.py:32: """Caching decorator for DoRA docs/refs/peft_lora_dora.py:34: Caching is only enabled if ENABLE_DORA_CACHING is set to True (default: False), when in eval mode, and when the docs/refs/peft_lora_dora.py:62:class DoraLinearLayer(nn.Module): docs/refs/peft_lora_dora.py:69: # cache intermediate values, e.g. weight norm of DoRA docs/refs/peft_lora_dora.py:86: def get_weight_norm(self, weight, lora_weight, scaling, adapter_name: Optional[str] = None) -> torch.Tensor: docs/refs/peft_lora_dora.py:89: weight = weight + scaling * lora_weight docs/refs/peft_lora_dora.py:90: weight_norm = torch.linalg.norm(weight, dim=1).to(weight.dtype) docs/refs/peft_lora_dora.py:91: return weight_norm docs/refs/peft_lora_dora.py:94: def get_lora_weight(self, lora_A, lora_B, adapter_name: Optional[str] = None): docs/refs/peft_lora_dora.py:95: # Don't use `lora_weight = lora_B.weight @ lora_A.weight` because this causes errors with FSDP. Instead, docs/refs/peft_lora_dora.py:97: x_eye = torch.eye(lora_A.weight.shape[1], device=lora_A.weight.device, dtype=lora_A.weight.dtype) docs/refs/peft_lora_dora.py:98: lora_weight = lora_B(lora_A(x_eye)).T docs/refs/peft_lora_dora.py:101: def update_layer(self, *, base_layer, lora_A, lora_B, scaling, place_on_cpu=False) -> None: docs/refs/peft_lora_dora.py:103: dtype_is_fp16 = lora_A.dtype == torch.float16 docs/refs/peft_lora_dora.py:105: lora_A = lora_A.float() docs/refs/peft_lora_dora.py:106: lora_B = lora_B.float() docs/refs/peft_lora_dora.py:116: r = lora_A.shape[0] docs/refs/peft_lora_dora.py:117: lora_weight = torch.mm(lora_B.view([-1, r]), lora_A.view([r, -1])) docs/refs/peft_lora_dora.py:120: lora_weight = lora_B @ lora_A docs/refs/peft_lora_dora.py:124... [tool] Run wc -l docs/refs/*.py (completed) kind: execute input: {"call_id":"call_bdEgZthDlRra2IlaA2Igs8Oa","process_id":"80034","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","wc -l docs/refs/*.py"],"cwd":"/media/wassname/SGIronWolf/projects5/2026... output: 375 docs/refs/antipasto3_svd_adapter.py 471 docs/refs/baukit_nethook.py 446 docs/refs/orig_delora.py 420 docs/refs/orig_hra_layer.py 59 docs/refs/orig_pissa_init.py 274 docs/refs/peft_delora_layer.py 96 docs/refs/peft_eva_finetuning.py 739 docs/refs/peft_eva.py 462 docs/refs/peft_hra_layer.py 336 docs/refs/peft_ia3_layer.py 287 docs/refs/peft_lora_dora.py 2510 docs/refs/peft_lora_layer.py 923 docs/refs/peft_lora_variants.py 7398 total [tool] Read peft_lora_layer.py (running) input: {"call_id":"call_6NarexWBfuoCsQBduKpM1wqf","process_id":"78324","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '190,276p' docs/refs/peft_lora_layer.py | nl -ba -v190"],"cwd":"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py [tool] Read peft_lora_layer.py (running) input: {"call_id":"call_wzNKIz9kVoezvm1lbPlep4sO","process_id":"81918","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '360,392p' docs/refs/peft_lora_layer.py | nl -ba -v360"],"cwd":"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py [tool] Read peft_delora_layer.py (running) input: {"call_id":"call_MexmC1hD4dzGon2Fl6m2LTOb","process_id":"2287","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '128,154p' docs/refs/peft_delora_layer.py | nl -ba -v128"],"cwd":... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py [tool] Read peft_delora_layer.py (running) input: {"call_id":"call_FtNtpBysfs7h0C5Qc4QBDv4I","process_id":"38456","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '243,260p' docs/refs/peft_delora_layer.py | nl -ba -v243"],"cwd"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py [tool] Read peft_lora_layer.py (completed) kind: read input: {"call_id":"call_6NarexWBfuoCsQBduKpM1wqf","process_id":"78324","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '190,276p' docs/refs/peft_lora_layer.py | nl -ba -v190"],"cwd":"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 190 lora_dropout_layer = nn.Dropout(p=lora_dropout) 191 else: 192 lora_dropout_layer = nn.Identity() 193 194 self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer})) 195 196 # Actual trainable parameters 197 self.lora_A[adapter_name] = nn.Linear(self.in_features, r, bias=False) 198 self.lora_B[adapter_name] = nn.Linear(r, self.out_features, bias=lora_bias) 199 200 # Tying adapters is only implemented for Linear layers 201 # where the source is the embedding layer. 202 # Currently, this is the most prevelant way of tying layers (weight tying) 203 if tied_adapter: 204 lora_A_params = tied_adapter["lora_A"] 205 lora_B_params = tied_adapter["lora_B"] 206 207 self.lora_A[adapter_name].weight = torch.nn.Parameter(lora_A_params) 208 self.lora_B[adapter_name].weight = torch.nn.Parameter(lora_B_params) 209 210 self.lora_bias[adapter_name] = lora_bias 211 212 if use_rslora: 213 self.scaling[adapter_name] = lora_alpha / math.sqrt(r) 214 else: 215 self.scaling[adapter_name] = lora_alpha / r 216 ... (60 more lines) [tool] Read peft_lora_layer.py (completed) kind: read input: {"call_id":"call_wzNKIz9kVoezvm1lbPlep4sO","process_id":"81918","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '360,392p' docs/refs/peft_lora_layer.py | nl -ba -v360"],"cwd":"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 360 def pissa_init(self, adapter_name, init_lora_weights): 361 weight = self.get_base_layer().weight 362 dtype = weight.dtype 363 if dtype not in [torch.float32, torch.float16, torch.bfloat16]: 364 raise TypeError( 365 "Please initialize PiSSA under float32, float16, or bfloat16. " 366 "Subsequently, re-quantize the residual model to help minimize quantization errors." 367 ) 368 weight = transpose(weight.to(torch.float32), self.fan_in_fan_out) 369 if init_lora_weights == "pissa": 370 # USV^T = W <-> VSU^T = W^T, where W^T = weight.data in R^{out_channel, in_channel}, 371 V, S, Uh = torch.linalg.svd(weight.data, full_matrices=False) 372 Vr = V[:, : self.r[adapter_name]] 373 Sr = S[: self.r[adapter_name]] 374 Sr /= self.scaling[adapter_name] 375 Uhr = Uh[: self.r[adapter_name]] 376 elif len(init_lora_weights.split("_niter_")) == 2: 377 Vr, Sr, Ur = svd_lowrank( 378 weight.data, self.r[adapter_name], niter=int(init_lora_weights.split("_niter_")[-1]) 379 ) 380 Sr /= self.scaling[adapter_name] 381 Uhr = Ur.t() 382 else: 383 raise ValueError( 384 f"init_lora_weights should be 'pissa' or 'pissa_niter_[number of iters]', got {init_lora_weights} instead." 385 ) 386 ... (6 more lines) [tool] Read peft_delora_layer.py (completed) kind: read input: {"call_id":"call_MexmC1hD4dzGon2Fl6m2LTOb","process_id":"2287","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '128,154p' docs/refs/peft_delora_layer.py | nl -ba -v128"],"cwd":... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 128 def reset_delora_parameters( 129 self, 130 adapter_name: str, 131 init_weights: bool = True, 132 delora_lambda: float = 15.0, 133 ) -> None: 134 if adapter_name not in self.delora_A.keys(): 135 return 136 137 if init_weights is True: 138 nn.init.kaiming_uniform_(self.delora_A[adapter_name], a=math.sqrt(5)) 139 nn.init.zeros_(self.delora_B[adapter_name]) 140 else: 141 nn.init.kaiming_uniform_(self.delora_A[adapter_name], a=math.sqrt(5)) 142 nn.init.kaiming_uniform_(self.delora_B[adapter_name], a=math.sqrt(5)) 143 144 self.delora_lambda[adapter_name].data.fill_(float(delora_lambda)) 145 146 # capture a fixed norm for this adapter to use for future delta computations 147 with torch.no_grad(): 148 w = self.get_base_layer().weight 149 if w.device.type != "meta": 150 w_norm = torch.norm(w.data, dim=0).detach() 151 else: 152 # For meta tensors, we can't compute the norm, so use a default value 153 w_norm = torch.ones(w.shape[1], device=w.device) 154 self.delora_w_norm[adapter_name] = w_norm [tool] Read peft_delora_layer.py (completed) kind: read input: {"call_id":"call_FtNtpBysfs7h0C5Qc4QBDv4I","process_id":"38456","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '243,260p' docs/refs/peft_delora_layer.py | nl -ba -v243"],"cwd"... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_delora_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 243 if adapter not in self.delora_A: 244 continue 245 246 x_d = self.delora_dropout[adapter](x) 247 248 # Decomposed delta calculation 249 # 1. (x * w_norm) @ A.T 250 h = nn.functional.linear(x_d * self.delora_w_norm[adapter], self.delora_A[adapter]) 251 252 # 2. h @ diag 253 An = torch.clamp(self.delora_A[adapter].norm(dim=1), min=1e-4) 254 Bn = torch.clamp(self.delora_B[adapter].norm(dim=0), min=1e-4) 255 scaling = (self.delora_lambda[adapter] / self.r[adapter]) / (An * Bn) 256 257 h = h * scaling 258 259 # 3. h @ B.T 260 h = nn.functional.linear(h, self.delora_B[adapter]) [tool] Read peft_ia3_layer.py (running) input: {"call_id":"call_YHAJ6VMgq9xcD7wqoh5TnPRh","process_id":"99386","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '56,75p' docs/refs/peft_ia3_layer.py | nl -ba -v56"],"cwd":"/med... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py [tool] Read peft_lora_dora.py (running) input: {"call_id":"call_u9VdPsmOvCaxZc1LPZrvu97d","process_id":"42148","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '86,165p' docs/refs/peft_lora_dora.py | nl -ba -v86"],"cwd":"/me... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_dora.py [tool] Read peft_ia3_layer.py (running) input: {"call_id":"call_81sbHRsSNlWnas3IM8yKCuaQ","process_id":"52784","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '168,188p' docs/refs/peft_ia3_layer.py | nl -ba -v168"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py [tool] Read peft_hra_layer.py (running) input: {"call_id":"call_B0sU5DYQP4aIu7GgcTTaNzy8","process_id":"58196","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '100,108p' docs/refs/peft_hra_layer.py | nl -ba -v100"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py [tool] Read peft_ia3_layer.py (completed) kind: read input: {"call_id":"call_YHAJ6VMgq9xcD7wqoh5TnPRh","process_id":"99386","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '56,75p' docs/refs/peft_ia3_layer.py | nl -ba -v56"],"cwd":"/med... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 56 def update_layer(self, adapter_name: str, config: IA3Config, **kwargs): 57 init_ia3_weights = config.init_ia3_weights 58 inference_mode = config.inference_mode 59 60 # This code works for linear layers, override for other layer types 61 # Actual trainable parameters 62 if self.is_feedforward: 63 weight = torch.randn((1, self.in_features)) 64 else: 65 weight = torch.randn((self.out_features, 1)) 66 self.ia3_l[adapter_name] = nn.Parameter(weight) 67 if init_ia3_weights: 68 self.reset_ia3_parameters(adapter_name) 69 self._move_adapter_to_device_of_base_layer(adapter_name) 70 self.set_adapter(self.active_adapters, inference_mode=inference_mode) 71 72 def reset_ia3_parameters(self, adapter_name): 73 if adapter_name in self.ia3_l.keys(): 74 # initialize learned vector with torch.ones 75 nn.init.constant_(self.ia3_l[adapter_name], 1.0) [tool] Read peft_ia3_layer.py (completed) kind: read input: {"call_id":"call_81sbHRsSNlWnas3IM8yKCuaQ","process_id":"52784","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '168,188p' docs/refs/peft_ia3_layer.py | nl -ba -v168"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_ia3_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 168 result = self.base_layer(x, *args, **kwargs) 169 else: 170 ia3_scaling = 1 171 for active_adapter in self.active_adapters: 172 if active_adapter not in self.ia3_l.keys(): 173 continue 174 dtype = self.ia3_l[active_adapter].dtype 175 ia3_scaling *= self.ia3_l[active_adapter].flatten() 176 177 if self.is_feedforward: 178 x = x.to(dtype) 179 # TODO: weight.dtype can be != self.ia3_l[self.active_adapters].dtype 180 # e.g. bf16 vs fp32. Is that okay? 181 interm = (x * ia3_scaling).to(previous_dtype) 182 result = self.base_layer(interm, *args, **kwargs) 183 else: 184 result = self.base_layer(x, *args, **kwargs) 185 result_dtype = result.dtype 186 result = (result * ia3_scaling).to(result_dtype) 187 188 return result [tool] Read peft_lora_dora.py (completed) kind: read input: {"call_id":"call_u9VdPsmOvCaxZc1LPZrvu97d","process_id":"42148","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '86,165p' docs/refs/peft_lora_dora.py | nl -ba -v86"],"cwd":"/me... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_lora_dora.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 86 def get_weight_norm(self, weight, lora_weight, scaling, adapter_name: Optional[str] = None) -> torch.Tensor: 87 # calculate L2 norm of weight matrix, column-wise 88 weight = transpose(weight, self.fan_in_fan_out) 89 weight = weight + scaling * lora_weight 90 weight_norm = torch.linalg.norm(weight, dim=1).to(weight.dtype) 91 return weight_norm 92 93 @cache_decorator("lora-weight") 94 def get_lora_weight(self, lora_A, lora_B, adapter_name: Optional[str] = None): 95 # Don't use `lora_weight = lora_B.weight @ lora_A.weight` because this causes errors with FSDP. Instead, 96 # calculate the same but using forward. 97 x_eye = torch.eye(lora_A.weight.shape[1], device=lora_A.weight.device, dtype=lora_A.weight.dtype) 98 lora_weight = lora_B(lora_A(x_eye)).T 99 return lora_weight 100 101 def update_layer(self, *, base_layer, lora_A, lora_B, scaling, place_on_cpu=False) -> None: 102 # temporarily convert fp16 to fp32, as fp16 can cause trouble on CPU with PyTorch < 2.2 103 dtype_is_fp16 = lora_A.dtype == torch.float16 104 if dtype_is_fp16: 105 lora_A = lora_A.float() 106 lora_B = lora_B.float() 107 108 with gather_params_ctx(base_layer.parameters()): 109 if base_layer.__class__.__name__ == "Linear4bit": 110 # We have to create a copy of the base layer, otherwise, FSDP will throw an error. 8bit does not work 111 # yet because Int8Params cannot be correctly deep-copied (attributes vanish) 112 base_layer = deepcopy(base_layer) ... (53 more lines) [tool] Read peft_hra_layer.py (completed) kind: read input: {"call_id":"call_B0sU5DYQP4aIu7GgcTTaNzy8","process_id":"58196","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '100,108p' docs/refs/peft_hra_layer.py | nl -ba -v100"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 100 def reset_hra_parameters(self, adapter_name: str): 101 if self.hra_r[adapter_name] % 2 != 0: 102 warnings.warn("The symmetric initialization can NOT be performed when r is odd!") 103 nn.init.kaiming_uniform_(self.hra_u[adapter_name], a=math.sqrt(5)) 104 else: 105 shape = self.hra_u[adapter_name].shape 106 half_u = torch.zeros(shape[0], shape[1] // 2) 107 nn.init.kaiming_uniform_(half_u, a=math.sqrt(5)) 108 self.hra_u[adapter_name] = nn.Parameter(torch.repeat_interleave(half_u, 2, dim=1)) [tool] Read peft_eva.py (running) input: {"call_id":"call_690oIE1mGi9p5KwBfYRgFCdI","process_id":"47440","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '466,533p' docs/refs/peft_eva.py | nl -ba -v466"],"cwd":"/media/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py [tool] Read peft_hra_layer.py (running) input: {"call_id":"call_UI8XKTEBvQpPGk7VSsCZvLFm","process_id":"20694","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '209,236p' docs/refs/peft_hra_layer.py | nl -ba -v209"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py [tool] Read antipasto3_svd_adapter.py (running) input: {"call_id":"call_JCuFK2l4lqHJxxajl0MKxKM4","process_id":"80090","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '35,65p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v35"],"cw... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py [tool] Read antipasto3_svd_adapter.py (running) input: {"call_id":"call_Hc7YfPlC2HNtnJxmPJFafOs4","process_id":"81194","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '117,158p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v117"],... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py [tool] Read peft_hra_layer.py (completed) kind: read input: {"call_id":"call_UI8XKTEBvQpPGk7VSsCZvLFm","process_id":"20694","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '209,236p' docs/refs/peft_hra_layer.py | nl -ba -v209"],"cwd":"/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_hra_layer.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 209 rank = self.hra_r[adapter_name] 210 apply_GS = self.hra_apply_GS[adapter_name] 211 opt_u = self.hra_u[adapter_name] 212 shape = opt_u.shape 213 214 if apply_GS: 215 weight = [(opt_u[:, 0] / opt_u[:, 0].norm()).view(-1, 1)] 216 for i in range(1, rank): 217 ui = opt_u[:, i].view(-1, 1) 218 for j in range(i): 219 ui = ui - (weight[j].t() @ ui) * weight[j] 220 weight.append((ui / ui.norm()).view(-1, 1)) 221 weight = torch.cat(weight, dim=1) 222 weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) - 2 * weight @ weight.t() 223 224 else: 225 opt_u = opt_u / opt_u.norm(dim=0) 226 weight = torch.eye(shape[0], device=opt_u.device, dtype=opt_u.dtype) 227 if reverse: 228 indices = range(rank - 1, -1, -1) 229 else: 230 indices = range(rank) 231 232 for i in indices: 233 ui = opt_u[:, i].view(-1, 1) 234 weight = weight - 2 * weight @ ui @ ui.t() 235 ... (1 more lines) [tool] Read peft_eva.py (completed) kind: read input: {"call_id":"call_690oIE1mGi9p5KwBfYRgFCdI","process_id":"47440","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '466,533p' docs/refs/peft_eva.py | nl -ba -v466"],"cwd":"/media/... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 466 eva_state_dict = {} 467 for name, rank in rank_dist.items(): 468 hook = hooks[layer_hook_map[name]][0] 469 if not torch.all(hook.converged[:rank]): 470 raise ValueError( 471 f"Layer {name} has not converged but was assigned rank {rank}. " 472 "Please report this issue at https://github.com/huggingface/peft/issues" 473 ) 474 u = hook.svd.components_[:rank] 475 if peft_config.eva_config.whiten: 476 u /= hook.svd.singular_values_[:rank].sqrt().reshape(-1, 1) 477 eva_state_dict[name] = u 478 479 # restore model state 480 model.train(training) 481 482 # move tensors to device 483 if device is not None: 484 eva_state_dict = {k: v.to(device) for k, v in eva_state_dict.items()} 485 486 return eva_state_dict 487 488 489 def _load_eva_state_dict( 490 model: torch.nn.Module, 491 eva_state_dict: dict, 492 adapter_name: str, ... (41 more lines) [tool] Read antipasto3_svd_adapter.py (completed) kind: read input: {"call_id":"call_JCuFK2l4lqHJxxajl0MKxKM4","process_id":"80090","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '35,65p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v35"],"cw... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 35 class DeltaSParam(SVDParam): 36 """Trainable delta_s scaling parameters (full LR).""" 37 pass 38 39 40 class RotationParam(SVDParam): 41 """Block-diagonal rotation parameters (lower LR via rotation_lr_scale config).""" 42 pass 43 44 45 class SVDFrozen(nnx.Variable): 46 """Frozen SVD component. Not differentiated.""" 47 pass 48 49 50 # -- SVD Steering Linear (replaces nnx.Linear) ------------------------------ 51 52 class SVDSteeringLinear(nnx.Module): 53 """SVD steering adapter replacing a linear layer. 54 55 y = ((x @ U_rot) * S_scaled) @ Vh + x @ W_res 56 57 where U_rot, S_scaled depend on alpha (the steering coefficient). 58 Frozen: U, S, Vh, W_res. Learnable: delta_s, rotation_params. 59 """ 60 61 def __init__( ... (4 more lines) [tool] Read antipasto3_svd_adapter.py (completed) kind: read input: {"call_id":"call_Hc7YfPlC2HNtnJxmPJFafOs4","process_id":"81194","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '117,158p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v117"],... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 117 U = self.svd_U.value 118 S = self.svd_S.value 119 Vh = self.svd_Vh.value 120 W_res = self.svd_W_res.value 121 params = self.rotation_params.value # (n_blocks, n_triu) 122 bs = self.block_size 123 n_blocks = params.shape[0] 124 125 # Reconstruct skew-symmetric from upper-triangle params (like OFT/PSOFT). 126 # 0.5 factor matches BOFT convention: cancels the 2x gradient from A - A^T. 127 A = jnp.zeros((n_blocks, bs, bs), dtype=jnp.float32) 128 A = A.at[:, self._triu_rows, self._triu_cols].set(params.astype(jnp.float32)) 129 A = 0.5 * (A - jnp.swapaxes(A, -1, -2)) 130 131 # Angle clamping (element-wise tanh, bounds bidirectional symmetry error) 132 a_limit = 2 * math.tan(self.max_angle / 2) 133 A = a_limit * jnp.tanh(A / a_limit) 134 135 # Cayley transform in float32: R = (I - X)^{-1}(I + X) 136 eye = jnp.eye(bs, dtype=jnp.float32) 137 X = alpha * A / 2 138 R_blocks = jnp.linalg.solve( 139 eye[None] - X, 140 eye[None] + X, 141 ) 142 143 # Apply rotation to U (input singular vectors) ... (15 more lines) [tool] Read antipasto3_svd_adapter.py (running) input: {"call_id":"call_VyW5j2uOyRzOTWVgvcecQYHo","process_id":"51183","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '158,205p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v158"],... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py [tool] Read antipasto3_svd_adapter.py (completed) kind: read input: {"call_id":"call_VyW5j2uOyRzOTWVgvcecQYHo","process_id":"51183","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '158,205p' docs/refs/antipasto3_svd_adapter.py | nl -ba -v158"],... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py output: sed: /tmp/.mount_vscodiOHg5sd/lib/x86_64-linux-gnu/libselinux.so.1: no version information available (required by sed) 158 Vh_rot = Vh 159 160 S_scaled = S + alpha * self.delta_s.value if self.use_delta_s else S 161 162 dt = x.dtype 163 out = (x @ U_rot.astype(dt)) * S_scaled.astype(dt) 164 out = out @ Vh_rot.astype(dt) 165 out = out + x @ W_res.astype(dt) 166 return out 167 168 169 def create_svd_adapter( 170 kernel: jax.Array, 171 r: int, 172 rotation_block_size: int, 173 max_rotation_angle: float, 174 rngs: nnx.Rngs, 175 selected_indices: jax.Array | None = None, 176 rotate_U: bool = True, 177 rotate_V: bool = False, 178 use_delta_s: bool = True, 179 ) -> SVDSteeringLinear: 180 """Create SVD adapter from a kernel matrix (in_dim, out_dim). 181 182 If selected_indices is provided, uses those SVD dimensions. 183 Otherwise uses top-r by singular value. 184 """ ... (21 more lines) [tool] Searching the Web (running) [tool] Opening: https://arxiv.org/abs/2106.09685 (completed) kind: fetch input: https://arxiv.org/abs/2106.09685 [tool] Searching the Web (running) [tool] Opening: https://arxiv.org/abs/2503.18225 (completed) kind: fetch input: https://arxiv.org/abs/2503.18225 [tool] Searching the Web (running) [tool] Opening: https://github.com/huggingface/peft/blob/main/src/peft/tuners/ia3/layer.py (completed) kind: fetch input: https://github.com/huggingface/peft/blob/main/src/peft/tuners/ia3/layer.py [tool] Searching the Web (running) [tool] Opening: https://github.com/DaShenZi721/HRA/blob/master/llama/peft/oft/layer_GS_HRA.py (completed) kind: fetch input: https://github.com/DaShenZi721/HRA/blob/master/llama/peft/oft/layer_GS_HRA.py [tool] Searching the Web (running) [tool] Opening: https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning.py (completed) kind: fetch input: https://github.com/huggingface/peft/blob/main/examples/eva_finetuning/eva_finetuning.py [tool] Searching the Web (running) [tool] Opening: https://raw.githubusercontent.com/huggingface/peft/main/src/peft/tuners/hra/layer.py (completed) kind: fetch input: https://raw.githubusercontent.com/huggingface/peft/main/src/peft/tuners/hra/layer.py [tool] Searching the Web (running) [plan] - [completed] Inventory source, refs, tests - [in_progress] Verify external links and snapshot drift - [pending] Audit each variant against refs - [pending] Assess smoke and Qwen probe validity - [pending] Assemble concise findings with line citations The external links are live for the paper and GitHub references I checked; AntiPaSTO is the standout because the docstring names only a GitHub research repo and no paper, which fails the reference standard in your prompt. I’m doing the math pass now, especially around PiSSA scaling, HRA orientation, and AntiPaSTO basis naming. [tool] Searching for: site:github.com/huggingface/peft src/peft/tuners/delora/layer.py PEFT DeLoRA layer.py, github huggingface peft delora layer.py (completed) kind: fetch input: site:github.com/huggingface/peft src/peft/tuners/delora/layer.py PEFT DeLoRA layer.py [tool] Read variants_review_v4.md (running) input: {"call_id":"call_jJh1eAt0PAr2or6I9kT26Nge","process_id":"63414","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba docs/audit/variants_review_v4.md"],"cwd":"/media/wassname/SGIron... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/audit/variants_review_v4.md [tool] Read REVIEW_PROMPT_VARIANTS_V4.md (running) input: {"call_id":"call_kWky1g3kEyZFT5QS99mtsU42","process_id":"76948","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba docs/audit/REVIEW_PROMPT_VARIANTS_V4.md"],"cwd":"/media/wassname... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/audit/REVIEW_PROMPT_VARIANTS_V4.md [tool] Read variants_review_v4.md (completed) kind: read input: {"call_id":"call_jJh1eAt0PAr2or6I9kT26Nge","process_id":"63414","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","nl -ba docs/audit/variants_review_v4.md"],"cwd":"/media/wassname/SGIron... files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/audit/variants_review_v4.md output: 1 [client] initialize (running) 2 3 [client] session/new (running) 4 I’ll use the `gsd` skill here because this is a multi-part audit: first I’ll read the local implementation and references, then verify URLs/snapshots, then review the two harnesses and collapse it into the requested verdict tables. 5 6 [tool] Read SKILL.md (running) 7 input: {"call_id":"call_hMhlWCMXk8cPaOEh4mCYl3wE","process_id":"55868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gsd/SKILL.md"],"cwd":"/me... 8 files: /home/wassname/.claude/skills/gsd/SKILL.md 9 10 [tool] Read SKILL.md (completed) 11 kind: read 12 input: {"call_id":"call_hMhlWCMXk8cPaOEh4mCYl3wE","process_id":"55868","turn_id":"019dc9b0-4472-7811-b19f-df7e11d4e3b9","command":["/usr/bin/zsh","-lc","sed -n '1,220p' /home/wassname/.claude/skills/gsd/SKILL.md"],"cwd":"/me... 13 files: /home/wassname/.claude/skills/gsd/SKILL.md 14 output: 15 --- 16 name: gsd 17 description: "Get Shit Done workflow for non-trivial tasks: spec -> implement -> test -> review -> wrap. Mandatory for anything with more than one step." 18 --- 19 20