schema.py

"""Pydantic schemas for soup.yaml config — single source of truth."""

import re
from typing import Any, Dict, List, Literal, Optional, Union

from pydantic import BaseModel, Field, field_validator, model_validator

# Buffer bounds live with the streaming planner so the schema bound and the
# runtime validator's message can never disagree (layer_stream has no torch).
from soup_cli.utils.layer_stream import (
    DEFAULT_STREAM_BUFFERS,
    MAX_STREAM_BUFFERS,
    MIN_STREAM_BUFFERS,
)
from soup_cli.utils.layer_stream import (
    ROLLOUT_STREAM_TASKS as _STREAM_ROLLOUT_TASKS,
)
from soup_cli.utils.layer_stream import (
    SUPPORTED_STREAM_TASKS as _STREAM_SUPPORTED_TASKS,
)

# Noise-floor bounds live with the ship verdict so the schema bound and the
# `--noise-floor` CLI validator can never disagree (ship_verdict has no torch,
# same reasoning as stream_buffers importing its bounds from layer_stream).
from soup_cli.utils.ship_verdict import (
    MAX_NOISE_FLOOR_RUNS,
    MIN_NOISE_FLOOR_RUNS,
)

# v0.39.0 Part C — per-pattern LoRA rank/alpha bounds
_MAX_LORA_RANK_PATTERN_KEYS = 256
_MAX_LORA_RANK_PATTERN_VALUE = 1024
_MAX_LORA_TARGET_PARAMETERS = 256
_MAX_LORA_TARGET_PARAMETER_LEN = 512

# v0.71.23 #266 — Spectrum targeted-training unfrozen-parameter caps
_MAX_UNFROZEN_PARAMETERS = 50_000
_MAX_UNFROZEN_PATTERN_LEN = 512
# Reject nested-unbounded-quantifier regexes — e.g. ``(x+)+y`` / ``(a*)*`` —
# which catastrophically backtrack (ReDoS) when re.search'd against parameter
# names in apply_unfrozen_parameters. soup.yaml is shareable config, so the
# pattern *class* is rejected at parse time, not just compile failures.
_UNFROZEN_REDOS_RE = re.compile(r"\([^)]*[+*][^)]*\)\s*[+*]")

# v0.71.34 #267 / #307 — tasks whose transformers trainer wires LisaCallback.
# LISA is full-FT of a rotating set of decoder layers, so a task only belongs
# here once its trainer skips PEFT, keeps the model trainable, and calls
# ``attach_lisa_callback``. Adding a task to this tuple without that wiring
# would accept a config the trainer silently ignores.
_LISA_SUPPORTED_TASKS = ("sft", "pretrain")


class LoraConfig(BaseModel):
    # #340 — `r: 0` is the first-class full-fine-tuning switch: no
    # adapter is applied and the base weights train directly. It is the
    # spelling `trainer/classifier.py` has read as "no adapter" since
    # v0.71.12 (#146) and the one `commands/card.py::_is_adapter` already
    # resolves to "dense model". Before #340 a rank of 0 reached peft and
    # died with "`r` should be a positive integer value", so nothing that
    # worked before changes meaning. `ge=0` closes the pre-existing hole
    # where a NEGATIVE rank parsed and failed the same way, deep in peft.
    r: int = Field(
        default=64,
        ge=0,
        description=(
            "LoRA rank. 0 = full fine-tuning: no adapter, every base "
            "parameter trains (sft / embedding + transformers + text + "
            "quantization='none' only)."
        ),
    )
    alpha: int = Field(default=16, description="LoRA alpha")
    dropout: float = Field(default=0.05, description="LoRA dropout")
    target_modules: Union[str, List[str]] = Field(
        default="auto",
        description="Target modules for LoRA. 'auto' = let peft decide.",
    )
    target_parameters: Optional[Union[Literal["auto"], List[str]]] = Field(
        default=None,
        description=(
            "Raw 2-D/3-D nn.Parameter tensors to adapt with PEFT LoRA. "
            "Use 'auto' to select architecture-specific parameter targets "
            "(currently Qwen4-Exp routed experts), a list of parameter-name "
            "suffixes for explicit control, or omit to disable. Requires "
            "dropout=0 and is wired for transformers SFT/pretrain."
        ),
    )
    use_dora: bool = Field(
        default=False,
        description="Enable DoRA (Weight-Decomposed Low-Rank Adaptation)",
    )
    use_rslora: bool = Field(
        default=False,
        description="Enable rank-stabilized LoRA scaling (better for high ranks)",
    )
    use_vera: bool = Field(
        default=False,
        description=(
            "Enable VeRA (Vector-based Random Matrix Adaptation). "
            "Shared random matrices — much smaller memory than LoRA. "
            "Mutually exclusive with use_dora and use_olora."
        ),
    )
    use_olora: bool = Field(
        default=False,
        description=(
            "Enable OLoRA (Orthogonal LoRA init via QR decomposition). "
            "Passes init_lora_weights='olora' to peft. "
            "Mutually exclusive with use_dora and use_vera. "
            "Equivalent to init_strategy='olora'."
        ),
    )
    rank_pattern: Optional[Dict[str, int]] = Field(
        default=None,
        description=(
            "Per-target-module-pattern LoRA rank override. Maps module name "
            "patterns (e.g. 'q_proj', 'experts.*.w1') to integer rank values. "
            "Useful for MoE configs where expert FFNs need lower rank than attn. "
            "Incompatible with use_vera (VeRA shares one rank across modules)."
        ),
    )
    alpha_pattern: Optional[Dict[str, int]] = Field(
        default=None,
        description=(
            "Per-target-module-pattern LoRA alpha override. Maps module name "
            "patterns to integer alpha values. Pairs with rank_pattern. "
            "Incompatible with use_vera."
        ),
    )
    init_strategy: Literal["random", "pissa", "olora", "loftq"] = Field(
        default="random",
        description=(
            "LoRA init strategy. 'random' (default) is standard Kaiming init. "
            "'pissa' (PiSSA) initializes A/B from the SVD of the base weight — "
            "faster early convergence but adds an SVD pass on the first epoch. "
            "'olora' is equivalent to use_olora=True (orthogonal QR init). "
            "'loftq' (v0.41.0) initialises A/B + a low-bit base together, "
            "useful with QLoRA. Cannot be combined with use_dora or use_vera."
        ),
    )
    # v0.41.0 Part C — LoftQ tuning knobs (used only when init_strategy='loftq').
    loftq_iter: int = Field(
        default=1, ge=1, le=10,
        description=(
            "LoftQ iteration count (1-10). Higher = better quant-aware init "
            "at the cost of one-time setup latency. Used only when "
            "init_strategy='loftq'."
        ),
    )
    loftq_bits: Literal[2, 4, 8] = Field(
        default=4,
        description=(
            "LoftQ target bitwidth — must be one of {2, 4, 8}. Used only "
            "when init_strategy='loftq'."
        ),
    )

    @model_validator(mode="after")
    def _validate_peft_exclusivity(self) -> "LoraConfig":
        enabled = [
            name for name, value in (
                ("use_dora", self.use_dora),
                ("use_vera", self.use_vera),
                ("use_olora", self.use_olora),
            )
            if value
        ]
        if len(enabled) > 1:
            raise ValueError(
                f"PEFT methods are mutually exclusive, got multiple enabled: "
                f"{', '.join(enabled)}. Pick at most one of use_dora, "
                f"use_vera, use_olora."
            )
        return self

    @model_validator(mode="before")
    @classmethod
    def _backcompat_align_olora(cls, values):
        """Back-compat: pre-validation, align init_strategy='olora' when only use_olora was set."""
        if not isinstance(values, dict):
            return values
        # Copy to avoid mutating the caller's dict (matches v0.33.0 #47
        # CrossDocCollator immutability fix).
        if values.get("use_olora") and "init_strategy" not in values:
            values = dict(values)
            values["init_strategy"] = "olora"
        return values

    @model_validator(mode="after")
    def _validate_init_strategy(self) -> "LoraConfig":
        # use_olora=True must agree with init_strategy when both are explicit
        if self.use_olora and self.init_strategy != "olora":
            raise ValueError(
                f"use_olora=True conflicts with init_strategy={self.init_strategy!r}. "
                f"Either set init_strategy='olora' (or omit it), or set use_olora=False."
            )
        # init_strategy='pissa' is incompatible with DoRA / VeRA
        if self.init_strategy == "pissa" and (self.use_dora or self.use_vera):
            other = "use_dora" if self.use_dora else "use_vera"
            raise ValueError(
                f"init_strategy='pissa' is incompatible with {other}=True. "
                f"PiSSA initializes the LoRA pair via SVD; combine with plain LoRA "
                f"(or rsLoRA) only."
            )
        # v0.41.0 Part C — init_strategy='loftq' is incompatible with DoRA / VeRA
        if self.init_strategy == "loftq" and (self.use_dora or self.use_vera):
            other = "use_dora" if self.use_dora else "use_vera"
            raise ValueError(
                f"init_strategy='loftq' is incompatible with {other}=True. "
                f"LoftQ jointly initialises A/B with quantised base weights; "
                f"combine with plain LoRA only."
            )
        return self

    @field_validator("rank_pattern", "alpha_pattern", mode="before")
    @classmethod
    def _validate_pattern_dict(cls, value) -> Optional[Dict[str, int]]:
        if value is None:
            return None
        if not isinstance(value, dict):
            raise ValueError("rank_pattern/alpha_pattern must be a dict[str, int]")
        if len(value) > _MAX_LORA_RANK_PATTERN_KEYS:
            raise ValueError(
                f"rank_pattern/alpha_pattern caps at {_MAX_LORA_RANK_PATTERN_KEYS} keys, "
                f"got {len(value)}"
            )
        cleaned: Dict[str, int] = {}
        for key, val in value.items():
            if not isinstance(key, str) or not key:
                raise ValueError(
                    "rank_pattern/alpha_pattern keys must be non-empty strings"
                )
            if "\x00" in key:
                raise ValueError("rank_pattern/alpha_pattern keys cannot contain null bytes")
            if isinstance(val, bool) or not isinstance(val, int):
                raise ValueError(
                    f"rank_pattern/alpha_pattern values must be int, "
                    f"got {type(val).__name__} for {key!r}"
                )
            if val <= 0 or val > _MAX_LORA_RANK_PATTERN_VALUE:
                raise ValueError(
                    f"rank_pattern/alpha_pattern values must be in (0, "
                    f"{_MAX_LORA_RANK_PATTERN_VALUE}], got {val} for {key!r}"
                )
            cleaned[key] = val
        return cleaned

    @field_validator("target_parameters", mode="before")
    @classmethod
    def _validate_target_parameters(cls, value):
        if value is None or value == "auto":
            return value
        if not isinstance(value, list):
            raise ValueError("target_parameters must be 'auto', a list[str], or null")
        if len(value) > _MAX_LORA_TARGET_PARAMETERS:
            raise ValueError(
                f"target_parameters caps at {_MAX_LORA_TARGET_PARAMETERS} entries, "
                f"got {len(value)}"
            )
        cleaned: List[str] = []
        seen = set()
        for index, entry in enumerate(value):
            if not isinstance(entry, str) or not entry.strip():
                raise ValueError(
                    f"target_parameters[{index}] must be a non-empty string"
                )
            target = entry.strip()
            if target == "auto":
                raise ValueError(
                    "target_parameters: use scalar 'auto', not ['auto']"
                )
            if "\x00" in target:
                raise ValueError("target_parameters entries cannot contain null bytes")
            if len(target) > _MAX_LORA_TARGET_PARAMETER_LEN:
                raise ValueError(
                    "target_parameters entries cap at "
                    f"{_MAX_LORA_TARGET_PARAMETER_LEN} characters"
                )
            if target not in seen:
                cleaned.append(target)
                seen.add(target)
        return cleaned

    @model_validator(mode="after")
    def _validate_target_parameter_compat(self) -> "LoraConfig":
        if not self.target_parameters:
            return self
        if self.dropout != 0:
            raise ValueError(
                "target_parameters requires lora.dropout=0 because PEFT cannot "
                "apply dropout correctly to raw nn.Parameter tensors"
            )
        if self.use_dora:
            raise ValueError(
                "target_parameters is incompatible with use_dora=True in PEFT"
            )
        if self.use_vera:
            raise ValueError(
                "target_parameters is a LoRA-only PEFT feature and is incompatible "
                "with use_vera=True"
            )
        if self.init_strategy != "random":
            raise ValueError(
                "target_parameters currently requires init_strategy='random'; "
                "PiSSA, OLoRA, and LoftQ are not validated for raw 3-D parameters"
            )
        return self

    @model_validator(mode="after")
    def _validate_pattern_vera_exclusivity(self) -> "LoraConfig":
        if self.use_vera and self.rank_pattern:
            raise ValueError(
                "rank_pattern is incompatible with use_vera=True (VeRA shares "
                "a single rank across all target modules). Disable use_vera or "
                "remove rank_pattern."
            )
        if self.use_vera and self.alpha_pattern:
            raise ValueError(
                "alpha_pattern is incompatible with use_vera=True. Disable "
                "use_vera or remove alpha_pattern."
            )
        return self


class DataConfig(BaseModel):
    train: Union[str, List[str]] = Field(
        ...,
        description=(
            "Path to training data or HF dataset name, or a list of >= 2 "
            "local file paths to combine via data.interleave. (#443)"
        ),
    )

    @field_validator("train", mode="before")
    @classmethod
    def _validate_train_shape(cls, v):
        if isinstance(v, str):
            return v
        if isinstance(v, list):
            if len(v) == 0:
                raise ValueError("data.train list must not be empty")
            if len(v) == 1:
                raise ValueError(
                    "data.train list must have >= 2 entries for "
                    "data.interleave — use a single string path for one "
                    "dataset"
                )
            for i, entry in enumerate(v):
                if not isinstance(entry, str) or not entry.strip():
                    raise ValueError(
                        f"data.train[{i}] must be a non-empty string"
                    )
            return v
        raise ValueError(
            "data.train must be a string or a list of strings "
            f"(got {type(v).__name__})"
        )
    format: Literal[
        "alpaca", "sharegpt", "chatml", "dpo", "kto", "llava", "sharegpt4v",
        "plaintext", "embedding", "audio", "tool-calling", "auto",
        # v0.42.0 — Data Pipeline Pro
        "prm", "pre_tokenized", "input_output", "video", "multimodal",
        # v0.62.0 Part A — RAFT (Retrieval-Augmented Fine-Tuning)
        "raft",
        # v0.71.32 — ASR (Whisper): rows are {"audio": path, "text": transcript}
        "asr",
    ] = Field(
        default="auto",
        description="Data format",
    )
    val_split: float = Field(default=0.1, ge=0.0, le=0.5, description="Validation split ratio")
    max_length: int = Field(
        default=2048, ge=64, le=1048576,
        description="Max sequence length in tokens",
    )
    image_dir: Optional[str] = Field(
        default=None,
        description="Base directory for resolving relative image paths in vision datasets",
    )
    audio_dir: Optional[str] = Field(
        default=None,
        description="Base directory for resolving relative audio paths in audio datasets",
    )
    train_on_responses_only: bool = Field(
        default=True,
        description=(
            "Mask non-assistant tokens with IGNORE_INDEX (-100). When True, "
            "only assistant content contributes to the SFT loss. Mirrors "
            "LlamaFactory + Axolotl default — replaces TRL's heuristic. (v0.36.0)"
        ),
    )
    train_on_messages_with_train_field: bool = Field(
        default=False,
        description=(
            "Per-message training mask via messages[i].train: bool. "
            "Mutually exclusive with train_on_responses_only. (v0.36.0)"
        ),
    )
    chat_template: Optional[str] = Field(
        default=None,
        description=(
            "Override the tokenizer chat template. Accepts a registered "
            "name (chatml, llama3, qwen2.5, mistral, gemma3, phi4, "
            "deepseek-r1) or a raw Jinja string. None = use the tokenizer's "
            "shipped template (errors loudly if absent). (v0.36.0)"
        ),
    )
    raft_shuffle_seed: Optional[int] = Field(
        default=None,
        ge=0,
        le=2_147_483_647,
        description=(
            "Seed for the RAFT golden/distractor document shuffle "
            "(data.format='raft'). Documents are always shuffled for "
            "distractor robustness; this knob fixes which reproducible "
            "permutation. None = seed 0. (v0.71.10 #199)"
        ),
    )

    @field_validator("raft_shuffle_seed", mode="before")
    @classmethod
    def _validate_raft_shuffle_seed(cls, v):
        # Bool is a subclass of int — reject before Pydantic coerces True->1
        # (project bool-as-int policy).
        if isinstance(v, bool):
            raise ValueError("raft_shuffle_seed must not be a bool")
        return v

    raft_epoch_shuffle: bool = Field(
        default=False,
        description=(
            "Re-permute RAFT golden/distractor documents EACH training epoch "
            "(data.format='raft'). When False (default) the document order is "
            "baked once at tokenisation time and fixed across epochs; when "
            "True the trainer re-composes + re-tokenises rows per epoch with "
            "an epoch salt so the model cannot memorise a fixed golden-doc "
            "slot. (v0.71.17 #253)"
        ),
    )

    # --- v0.71.36 Data Moat II: continual-learning rehearsal ---------------
    replay: Optional[str] = Field(
        default=None,
        description=(
            "Path to an OLD dataset to interleave into training as "
            "continual-learning rehearsal, so fine-tuning on a new task does "
            "not erase the previous one. Rows are mixed into train ONLY "
            "(never val, which stays pure new-task). sft / pretrain only; "
            "incompatible with packing / multipack. (v0.71.36)"
        ),
    )
    replay_ratio: float = Field(
        default=0.1,
        gt=0.0,
        le=0.5,
        description=(
            "Fraction of the FINAL mixed train set that is replay rows: "
            "n_replay = round(r/(1-r) * n_new). At 0.1 over 1000 new rows "
            "that is 111 replay rows -> 1111 total -> 10.0%. (v0.71.36)"
        ),
    )
    replay_seed: Optional[int] = Field(
        default=None,
        ge=0,
        le=2_147_483_647,
        description=(
            "Seed for the replay sample + interleave. None = seed 0. "
            "(v0.71.36)"
        ),
    )

    @field_validator("replay")
    @classmethod
    def _validate_replay_path(cls, v):
        if v is None:
            return None
        if not isinstance(v, str):
            raise ValueError("data.replay must be a string path")
        cleaned = v.strip()
        if not cleaned:
            raise ValueError("data.replay must be a non-empty path")
        if "\x00" in cleaned:
            raise ValueError("data.replay must not contain null bytes")
        if len(cleaned) > 4096:
            raise ValueError("data.replay path too long (max 4096 chars)")
        return cleaned

    @field_validator("replay_ratio", mode="before")
    @classmethod
    def _validate_replay_ratio(cls, v):
        # Bool is a subclass of int/float — reject before coercion.
        if isinstance(v, bool):
            raise ValueError("data.replay_ratio must not be a bool")
        return v

    @field_validator("replay_seed", mode="before")
    @classmethod
    def _validate_replay_seed(cls, v):
        if isinstance(v, bool):
            raise ValueError("data.replay_seed must not be a bool")
        return v

    # --- v0.42.0 Data Pipeline Pro -----------------------------------------
    video_dir: Optional[str] = Field(
        default=None,
        description=(
            "Base directory for resolving relative video paths in video "
            "datasets. Mirrors image_dir / audio_dir. (v0.42.0 Part A)"
        ),
    )
    tokenized_path: Optional[str] = Field(
        default=None,
        description=(
            "Path to a pre-tokenized cache produced by `soup data preprocess`. "
            "When set, the trainer skips the tokenize stage and reads tensors "
            "directly. Mirrors LF tokenized_path / Axolotl `empty` type. "
            "(v0.42.0 Part C)"
        ),
    )
    streaming: bool = Field(
        default=False,
        description=(
            "Pass-through to HF datasets `streaming=True`. Use for datasets "
            "that don't fit on disk. Pairs with `buffer_size`. (v0.42.0 Part B)"
        ),
    )
    buffer_size: Optional[int] = Field(
        default=None,
        description=(
            "Shuffle buffer size for streaming datasets. None = HF default. "
            "Bounds [1, 1_000_000]. (v0.42.0 Part B)"
        ),
    )
    shards: Optional[int] = Field(
        default=None,
        description=(
            "Number of shards for HF dataset splits (axolotl `shards`). "
            "Bounds [1, 1024]. (v0.42.0 Part B)"
        ),
    )
    interleave: Optional[Union[str, Dict]] = Field(
        default=None,
        description=(
            "Multi-dataset interleave strategy: 'concat' / 'under' / 'over' / "
            "{strategy: 'probs', probs: [...]}. (v0.42.0 Part D)"
        ),
    )
    mask_history: bool = Field(
        default=False,
        description=(
            "LF mask_history — mask all but the last assistant turn during "
            "loss computation. (v0.42.0 Part D)"
        ),
    )
    train_on_prompt: bool = Field(
        default=False,
        description=(
            "LF train_on_prompt — include the prompt tokens in the loss. "
            "Inverse of train_on_responses_only. (v0.42.0 Part D)"
        ),
    )
    eval_on_each_dataset: bool = Field(
        default=False,
        description=(
            "LF eval_on_each_dataset — when interleaving, run eval on every "
            "constituent dataset separately. (v0.42.0 Part D)"
        ),
    )
    split_thinking: bool = Field(
        default=False,
        description=(
            "Axolotl split_thinking — separate `<think>` reasoning blocks "
            "from the final answer for fine-grained masking. Qwen3-style. "
            "(v0.42.0 Part D)"
        ),
    )
    image_min_pixels: Optional[int] = Field(
        default=None,
        description="Per-image min pixel count for vision data. (v0.42.0 Part D)",
    )
    image_max_pixels: Optional[int] = Field(
        default=None,
        description="Per-image max pixel count for vision data. (v0.42.0 Part D)",
    )
    image_resize_algorithm: Optional[
        Literal["nearest", "bilinear", "bicubic", "lanczos"]
    ] = Field(
        default=None,
        description="Pillow resize algorithm for image preprocessing. (v0.42.0 Part D)",
    )
    video_fps: Optional[float] = Field(
        default=None,
        description=(
            "Target frames-per-second for video preprocessing. (v0.42.0 Part D)"
        ),
    )
    video_maxlen: Optional[int] = Field(
        default=None,
        description=(
            "Max number of frames per video clip. Bounds (0, 4096]. "
            "(v0.42.0 Part D)"
        ),
    )
    add_new_tokens: Optional[List[str]] = Field(
        default=None,
        description=(
            "Add these tokens to the tokenizer vocab + resize embeddings. "
            "Cap 10_000 entries; per-token <= 256 chars; no duplicates. "
            "(v0.42.0 Part E)"
        ),
    )
    new_special_tokens: Optional[List[str]] = Field(
        default=None,
        description=(
            "Like add_new_tokens but registered as additional_special_tokens "
            "so they are not split by the tokenizer. (v0.42.0 Part E)"
        ),
    )
    resize_vocab: bool = Field(
        default=False,
        description=(
            "Resize the model's input/output embedding matrix when "
            "add_new_tokens / new_special_tokens grew the vocab. "
            "(v0.42.0 Part E)"
        ),
    )
    extend_conversation: bool = Field(
        default=False,
        description=(
            "Unsloth-style conversation extension — extend the last assistant "
            "turn with N more tokens for 'continue' prompts. (v0.42.0 Part E)"
        ),
    )
    skip_prepare_dataset: bool = Field(
        default=False,
        description=(
            "Axolotl skip_prepare_dataset — escape hatch when the input is "
            "already in the trainer's expected schema. (v0.42.0 Part E)"
        ),
    )
    remove_unused_columns: bool = Field(
        default=True,
        description=(
            "HF Trainer remove_unused_columns. Set False when feeding "
            "extra cols to a custom collator. (v0.42.0 Part E)"
        ),
    )
    prompt_strategy: Optional[str] = Field(
        default=None,
        description=(
            "Axolotl-style 'module.path:function_name' Python transform. "
            "Schema-only in v0.42.0 — runtime invocation lands in v0.42.1. "
            "(v0.42.0 Part E)"
        ),
    )
    # ---- v0.61.0 Part A — Unlearning data sources --------------------------
    forget_set: Optional[str] = Field(
        default=None,
        description=(
            "Path or HF dataset name for the forget set (rows to unlearn). "
            "Required when task='unlearn'. Null-byte rejected, capped at "
            "4096 chars. Containment is deferred to the trainer-side loader "
            "so HF dataset IDs (e.g. ``locuslab/TOFU``) still pass schema. "
            "(v0.61.0 Part A)"
        ),
    )
    retain_set: Optional[str] = Field(
        default=None,
        description=(
            "Path or HF dataset name for the retain set (rows whose "
            "performance must be preserved). Optional but recommended — "
            "NPO/SimNPO/RMU all degrade without one. Same validation as "
            "forget_set. (v0.61.0 Part A)"
        ),
    )

    @field_validator("forget_set", "retain_set")
    @classmethod
    def _validate_unlearn_dataset_path(cls, value: Optional[str]) -> Optional[str]:
        """v0.61.0 Part A — shape-only validation for forget/retain refs.

        Accepts None, an HF dataset id (e.g. ``locuslab/TOFU``), or a
        local relative path. Null-byte rejected, oversize rejected.
        Containment check is deliberately deferred to the trainer-side
        loader so legitimate HF dataset IDs (which look like file paths
        with a slash) still pass schema-load — mirrors v0.40.5
        ``reward_model`` policy.
        """
        if value is None:
            return None
        if not isinstance(value, str):
            raise ValueError("forget_set / retain_set must be a string")
        if not value:
            return None
        if "\x00" in value:
            raise ValueError(
                "forget_set / retain_set must not contain null bytes"
            )
        if len(value) > 4096:
            raise ValueError(
                "forget_set / retain_set must be <= 4096 chars"
            )
        return value

    @field_validator("video_dir", "tokenized_path")
    @classmethod
    def _validate_v042_optional_path(cls, value: Optional[str]) -> Optional[str]:
        if value is None:
            return None
        if not isinstance(value, str):
            raise ValueError("path must be a string")
        if not value:
            return None
        if "\x00" in value:
            raise ValueError("path must not contain null bytes")
        if len(value) > 4096:
            raise ValueError("path must be <= 4096 chars")
        # Schema-level containment via shared `is_under_cwd` (os.path.realpath
        # + commonpath). Rejects arbitrary system paths at config load so a
        # crafted soup.yaml fails fast instead of at first filesystem read.
        from soup_cli.utils.paths import is_under_cwd

        if not is_under_cwd(value):
            raise ValueError(
                "path must stay under the current working directory "
                "(absolute paths outside cwd are rejected at config load)."
            )
        return value

    @field_validator("buffer_size")
    @classmethod
    def _validate_buffer_size_v042(cls, value: Optional[int]) -> Optional[int]:
        from soup_cli.utils.data_pipeline import validate_buffer_size

        return validate_buffer_size(value)

    @field_validator("shards")
    @classmethod
    def _validate_shards_v042(cls, value: Optional[int]) -> Optional[int]:
        from soup_cli.utils.data_pipeline import validate_shards

        return validate_shards(value)

    @field_validator("image_min_pixels", "image_max_pixels")
    @classmethod
    def _validate_image_pixels_v042(cls, value, info):
        from soup_cli.utils.data_pipeline import validate_image_pixels

        return validate_image_pixels(info.field_name, value)

    @field_validator("video_fps")
    @classmethod
    def _validate_video_fps_v042(cls, value: Optional[float]) -> Optional[float]:
        from soup_cli.utils.data_pipeline import validate_video_fps

        return validate_video_fps(value)

    @field_validator("video_maxlen")
    @classmethod
    def _validate_video_maxlen_v042(cls, value: Optional[int]) -> Optional[int]:
        from soup_cli.utils.data_pipeline import validate_video_maxlen

        return validate_video_maxlen(value)

    @field_validator("add_new_tokens", "new_special_tokens")
    @classmethod
    def _validate_new_tokens_v042(
        cls, value: Optional[List[str]]
    ) -> Optional[List[str]]:
        from soup_cli.utils.data_pipeline import validate_new_tokens

        return validate_new_tokens(value)

    @field_validator("prompt_strategy")
    @classmethod
    def _validate_prompt_strategy_v042(cls, value: Optional[str]) -> Optional[str]:
        from soup_cli.utils.data_pipeline import validate_prompt_strategy

        return validate_prompt_strategy(value)

    @field_validator("interleave")
    @classmethod
    def _validate_interleave_v042(cls, value):
        # Shape validation only, independent of `train`. We accept None /
        # str / dict here and reject obvious type errors so a YAML like
        # ``data.interleave: 99`` fails loudly at config load. The full
        # parse (which needs num_datasets = len(data.train)) now happens
        # in SoupConfig._validate_interleave_compat below — num_datasets is
        # a parse-time constant since #443 widened data.train to accept a
        # list.
        if value is None:
            return None
        if isinstance(value, str):
            from soup_cli.utils.data_pipeline import INTERLEAVE_STRATEGIES

            if value not in INTERLEAVE_STRATEGIES:
                raise ValueError(
                    f"interleave must be one of {sorted(INTERLEAVE_STRATEGIES)} "
                    f"or a dict — got {value!r}"
                )
            if value == "probs":
                # Probs requires the dict form so the per-dataset weights are
                # supplied — bare "probs" is meaningless.
                raise ValueError(
                    "interleave='probs' requires a 'probs' list — use "
                    "{strategy: probs, probs: [...]} dict form."
                )
            return value
        if isinstance(value, dict):
            if "strategy" not in value:
                raise ValueError(
                    "interleave dict form must include 'strategy' key"
                )
            return value
        raise ValueError(
            f"interleave must be None, a string, or a dict (got "
            f"{type(value).__name__})"
        )

    @field_validator("chat_template")
    @classmethod
    def _validate_chat_template(cls, value: Optional[str]) -> Optional[str]:
        if value is None:
            return None
        if not isinstance(value, str):
            raise ValueError("chat_template must be a string")
        if not value:
            return None
        if "\x00" in value:
            raise ValueError("chat_template must not contain null bytes")
        if len(value) > 65536:
            raise ValueError("chat_template must be <= 64KB")
        # Block Jinja directives that touch the filesystem or load arbitrary
        # modules. Only control-flow + variable interpolation are allowed
        # for raw chat-template strings (v0.36.0 security review fix).
        lower = value.lower()
        for tag in ("{%- include", "{% include", "{%- import", "{% import",
                    "{%- from", "{% from", "{%- macro", "{% macro",
                    "{%- extends", "{% extends"):
            if tag in lower:
                directive = tag.split(None, 1)[-1]
                raise ValueError(
                    f"chat_template may not use Jinja '{directive}' directive — "
                    f"only control-flow and variable interpolation are allowed."
                )
        return value

    @model_validator(mode="after")
    def _validate_loss_mask_exclusivity(self) -> "DataConfig":
        if self.train_on_responses_only and self.train_on_messages_with_train_field:
            raise ValueError(
                "train_on_responses_only and train_on_messages_with_train_field "
                "are mutually exclusive. Disable one. The per-message 'train' "
                "field is opt-in for fine-grained per-message control."
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_train_on_prompt(self) -> "DataConfig":
        # train_on_prompt is the inverse semantics of train_on_responses_only —
        # both True is contradictory. Match v0.36.0 loss-mask exclusivity policy.
        if self.train_on_prompt and self.train_on_responses_only:
            raise ValueError(
                "train_on_prompt and train_on_responses_only are mutually "
                "exclusive — train_on_prompt opts INTO prompt-token loss, "
                "train_on_responses_only opts OUT. Pick one."
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_image_pixel_range(self) -> "DataConfig":
        if (
            self.image_min_pixels is not None
            and self.image_max_pixels is not None
            and self.image_min_pixels > self.image_max_pixels
        ):
            raise ValueError(
                "image_min_pixels must be <= image_max_pixels"
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_streaming_buffer(self) -> "DataConfig":
        # buffer_size only meaningful when streaming=True — surface the
        # mismatch loudly (mirrors v0.32.0 spike-recovery / loss-watchdog
        # cross-validator policy).
        if self.buffer_size is not None and not self.streaming:
            raise ValueError(
                "buffer_size requires streaming=True (HF datasets only "
                "supports a shuffle buffer in streaming mode)."
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_video_fields(self) -> "DataConfig":
        # Video-only fields must not be set when format != 'video' to avoid
        # silent no-ops (Axolotl-mode footgun this validator prevents).
        video_fields = (
            ("video_fps", self.video_fps),
            ("video_maxlen", self.video_maxlen),
            ("video_dir", self.video_dir),
        )
        any_set = any(v is not None for _, v in video_fields)
        if any_set and self.format not in ("video", "multimodal", "auto"):
            names = [n for n, v in video_fields if v is not None]
            raise ValueError(
                f"video-related fields {names} require format in "
                "{video, multimodal, auto} (got "
                f"{self.format!r})."
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_resize_vocab_requires_tokens(self) -> "DataConfig":
        if self.resize_vocab and not (
            self.add_new_tokens or self.new_special_tokens
        ):
            raise ValueError(
                "resize_vocab=True requires add_new_tokens or "
                "new_special_tokens to be non-empty — otherwise the resize is "
                "a no-op."
            )
        return self

    @model_validator(mode="after")
    def _validate_v042_pre_tokenized_path(self) -> "DataConfig":
        # tokenized_path is meaningful regardless of format (Axolotl `empty`
        # type expects the cache to be the source of truth). But the
        # pre_tokenized format implies the path must be set.
        if self.format == "pre_tokenized" and not self.tokenized_path:
            raise ValueError(
                "format='pre_tokenized' requires data.tokenized_path to point "
                "at a cache directory produced by `soup data preprocess`."
            )
        return self


class AdviseConfig(BaseModel):
    """Pre-flight decision config (v0.54.0 — schema-only).

    Surfaces the `soup advise` knobs through the central config schema so a
    `soup.yaml` can carry persistent advise settings (e.g. a frozen goal
    string + history-log path override). Live consumption is owned by
    ``soup_cli/commands/advise.py``; this field is informational on
    ``SoupConfig`` only.
    """

    goal: Optional[str] = Field(
        default=None,
        max_length=4096,
        description=(
            "Default goal string for `soup advise`. Sharpens task "
            "classification when set."
        ),
    )
    probe: bool = Field(
        default=False,
        description=(
            "Run the 10-minute ROI probe by default when `soup advise` is "
            "invoked through this config. Heuristic stubs in v0.54.0."
        ),
    )
    record: bool = Field(
        default=False,
        description=(
            "Append every verdict from this config to "
            "~/.soup/advise_history.jsonl with accepted=True."
        ),
    )

    @field_validator("goal")
    @classmethod
    def _goal_no_null_byte(cls, value: Optional[str]) -> Optional[str]:
        if value is None:
            return value
        if not isinstance(value, str):
            raise TypeError("advise.goal must be a string")
        if "\x00" in value:
            raise ValueError("advise.goal must not contain null bytes")
        return value


class EvalGateConfig(BaseModel):
    """Eval-Gated Training config (v0.26.0 Part B).

    Runs a declarative eval suite at epoch boundaries and halts training
    if any task regresses below ``regression_threshold`` vs the baseline.
    """

    enabled: bool = Field(
        default=False,
        description="Turn the eval gate on",
    )
    suite: Optional[str] = Field(
        default=None,
        description="Path to eval-suite YAML (evals/gate.yaml)",
    )
    every_n_epochs: int = Field(
        default=1, ge=1, le=100,
        description="Run gate every N epochs (1-100)",
    )
    regression_threshold: float = Field(
        default=0.05, ge=0.0, le=1.0,
        description="Max absolute drop vs baseline before regression fires",
    )
    baseline: Optional[str] = Field(
        default=None,
        description="registry://<id> | 'previous' | file path - scores to compare against",
    )
    on_regression: Literal["stop", "warn", "continue"] = Field(
        default="stop",
        description="Action on regression: stop training | warn only | continue",
    )

    @model_validator(mode="after")
    def _require_suite_when_enabled(self) -> "EvalGateConfig":
        if self.enabled and not self.suite:
            raise ValueError(
                "eval_gate.suite is required when eval_gate.enabled=true"
            )
        return self


class TrainingConfig(BaseModel):
    epochs: int = Field(default=3, ge=1, description="Number of training epochs")
    lr: float = Field(default=2e-5, gt=0, description="Learning rate")
    batch_size: Union[int, Literal["auto"]] = Field(
        default="auto",
        description="Batch size. 'auto' = find max that fits in memory.",
    )

    @field_validator("batch_size")
    @classmethod
    def _validate_batch_size_positive(cls, v: Any) -> Any:
        """Reject 0 and negatives, which parsed happily before v0.73.1.

        `Union[int, Literal["auto"]]` carried no lower bound, so `batch_size: -4`
        loaded and then meant whatever each trainer's arithmetic happened to do
        with it — including the VRAM pre-flight, which multiplies by it.
        """
        if isinstance(v, int) and not isinstance(v, bool) and v < 1:
            raise ValueError(f"training.batch_size must be >= 1 or 'auto'; got {v}")
        return v
    auto_batch_size_strategy: Literal["auto", "static", "probe"] = Field(
        default="auto",
        description=(
            "How to pick the auto batch size: 'static' (fast formula), "
            "'probe' (real OOM try/halve loop), 'auto' (probe on CUDA, "
            "static on CPU). Default 'auto' (v0.36.0)."
        ),
    )
    gradient_accumulation_steps: int = Field(default=4, ge=1)
    # #341 — the general training seed. Before this there was none:
    # `TrainingArguments` took HF's defaults on every run, so two runs of the
    # same config differed only by GPU nondeterminism and "run it again with a
    # different seed" was impossible. Both default to None rather than to 42
    # so the trainer can distinguish "unset" from "explicitly 42" — the
    # multipack FFD sampler's historical seed of 0 has to survive an unset
    # seed, while `TrainingArguments.seed` keeps HF's 42.
    #
    # Upper bound: `transformers.set_seed` feeds the value to
    # `numpy.random.seed`, which rejects anything outside [0, 2**32-1]. Bound
    # it here so a too-large seed is a config error, not a numpy traceback at
    # step 0.
    seed: Optional[int] = Field(
        default=None,
        ge=0,
        le=2**32 - 1,
        description=(
            "Training seed (weight init of new params, data order, dropout). "
            "Reaches TrainingArguments.seed and the multipack sampler. "
            "Unset = HF's default of 42."
        ),
    )
    data_seed: Optional[int] = Field(
        default=None,
        ge=0,
        le=2**32 - 1,
        description=(
            "Separate seed for data sampling only (TrainingArguments."
            "data_seed). Set it alongside `seed` to vary data order while "
            "holding initialisation fixed. Unset = follow `seed`."
        ),
    )

    @field_validator("seed", "data_seed", mode="before")
    @classmethod
    def _validate_seed_ints(cls, v: Any) -> Any:
        """#341 — reject bool-as-int (`bool` subclasses `int`, so `seed: true`
        would silently become seed 1). Mirrors the v0.71.34 LISA policy."""
        if isinstance(v, bool):
            raise ValueError(
                "training.seed / training.data_seed must be int, not bool"
            )
        return v

    warmup_ratio: float = Field(default=0.03, ge=0.0, le=0.5)
    weight_decay: float = Field(default=0.01, ge=0.0)
    max_grad_norm: float = Field(default=1.0, gt=0)
    lora: LoraConfig = Field(default_factory=LoraConfig)
    quantization: Literal[
        "4bit",
        "8bit",
        "none",
        "gptq",
        "awq",
        "hqq:1bit",
        "hqq:2bit",
        "hqq:3bit",
        "hqq:4bit",
        "hqq:5bit",
        "hqq:6bit",
        "hqq:8bit",
        "aqlm",
        "eetq",
        "mxfp4",
        "fp8",
        # v0.52.0 Part D — BitNet 1.58-bit (axolotl + onebitllms).
        "bitnet_1.58",
    ] = Field(
        default="4bit",
        description=(
            "Quantization (v0.38.0 — Quant Menu): "
            "4bit (BNB QLoRA), 8bit (BNB), none, "
            "gptq / awq (load pre-quantized checkpoint, train LoRA on top), "
            "hqq:Nbit (HQQ 1-8 bit, N in {1..6, 8}), "
            "aqlm (extreme 2-bit), eetq (8-bit fast), "
            "mxfp4 (BNB 4-bit MXFP4 quant_type), "
            "fp8 (load FP8 checkpoint with dequantize-on-load), "
            "bitnet_1.58 (BitNet ternary, v0.52.0 schema-only)."
        ),
    )
    gptq_disable_exllama: bool = Field(
        default=True,
        description=(
            "v0.38.0 — disable exllama backend for GPTQ. PEFT requires triton "
            "backend; exllama silently breaks adapter training."
        ),
    )
    bnb_4bit_quant_storage: Optional[
        Literal["uint8", "float16", "bfloat16", "float32"]
    ] = Field(
        default=None,
        description=(
            "v0.38.0 Part G — BNB 4-bit storage dtype. FSDP+QLoRA resolves "
            "this automatically to the effective floating compute dtype; "
            "outside FSDP, None keeps BNB's legacy uint8 default."
        ),
    )
    quantization_aware: Union[bool, Literal["fp8"]] = Field(
        default=False,
        description=(
            "Quantization-Aware Training. False=off, True=int8 QAT (torchao), "
            "'fp8'=FP8 training on H100/B100 (v0.28.0)."
        ),
    )
    fp8_recipe: Literal["tensorwise", "rowwise", "rowwise_with_gw_hp"] = Field(
        default="tensorwise",
        description=(
            "FP8 scaling recipe (only used when quantization_aware='fp8'). "
            "'tensorwise' (fastest, default), 'rowwise' (more accurate, CUTLASS rowwise), "
            "'rowwise_with_gw_hp' (most accurate, grad_weight in high precision). (v0.28.1)."
        ),
    )
    optimizer: str = Field(
        default="adamw_torch",
        description=(
            "Optimizer name. v0.41.0 expands the allowlist to cover BAdam, "
            "APOLLO, Adam-mini, lomo/adalomo, grokadamw, schedule_free, "
            "muon/dion/came_pytorch, and TorchAO ao_adamw_{fp8,4bit,8bit}. "
            "See soup_cli.utils.optimizer_zoo.SUPPORTED_OPTIMIZERS for the "
            "full list."
        ),
    )
    # v0.41.0 Part B — per-module-pattern LR override.
    lr_groups: Optional[List[Dict[str, Union[str, float]]]] = Field(
        default=None,
        description=(
            "Per-module LR override. List of {pattern, lr} entries (or a "
            "{pattern: lr} dict). First match wins; remaining params fall "
            "through to the base lr. Capped at 32 entries. (v0.41.0)"
        ),
    )
    # v0.41.0 Part C — LLaMA Pro block expansion.
    expand_layers: Optional[int] = Field(
        default=None, ge=1, le=64,
        description=(
            "LLaMA Pro: append N zero-init transformer blocks and freeze "
            "the original ones. Schema lands in v0.41.0 — full live wiring "
            "deferred to v0.41.1."
        ),
    )
    freeze_trainable_layers: Optional[int] = Field(
        default=None,
        description=(
            "LLaMA Pro: signed int. Positive = train only top-N decoder "
            "layers; negative = train only bottom-N. Magnitude capped at "
            "1000. (v0.41.0)"
        ),
    )
    # v0.41.0 Part C schema / v0.71.12 #84 live — Mixture-of-Depths routing.
    use_mod: bool = Field(
        default=False,
        description=(
            "Enable Mixture-of-Depths selective-token routing (arXiv:2404.02258). "
            "Live in v0.71.12 #84 for SFT + Pretrain on Llama / Qwen / Mistral; "
            "each decoder layer gets a router that passes only the top-k tokens "
            "(k = floor(seq_len * mod_capacity_factor)) through the block."
        ),
    )
    mod_capacity_factor: float = Field(
        default=0.125, gt=0.0, le=1.0,
        description=(
            "Fraction of tokens routed through each block when use_mod=True. "
            "Bounded (0, 1]; default 0.125 (per the MoD paper). (v0.71.12 #84)"
        ),
    )
    # v0.41.0 Part C — Friendly aliases for `quantization` (LF / Axolotl users).
    load_in_8bit: Optional[bool] = Field(
        default=None,
        description=(
            "Friendly alias for quantization='8bit' / 'none'. When True, "
            "rewrites quantization to '8bit' if currently 'none'/'4bit'. "
            "Conflicts with load_in_16bit. (v0.41.0)"
        ),
    )
    load_in_16bit: Optional[bool] = Field(
        default=None,
        description=(
            "Friendly alias: when True, sets quantization='none' (full bf16/"
            "fp16 LoRA). Conflicts with load_in_8bit. (v0.41.0)"
        ),
    )
    scheduler: str = Field(default="cosine", description="LR scheduler type")
    save_steps: int = Field(default=100, description="Save checkpoint every N steps")
    logging_steps: int = Field(default=10, description="Log metrics every N steps")
    # DPO-specific
    dpo_beta: float = Field(
        default=0.1, gt=0, description="DPO beta — KL penalty coefficient"
    )
    # KTO-specific
    kto_beta: float = Field(
        default=0.1, gt=0, description="KTO beta — KL penalty coefficient"
    )
    # ORPO-specific
    orpo_beta: float = Field(
        default=0.1, gt=0, description="ORPO beta — odds ratio weight"
    )
    # SimPO-specific
    simpo_gamma: float = Field(
        default=0.5, ge=0, description="SimPO gamma — reward margin term"
    )
    cpo_alpha: float = Field(
        default=1.0, gt=0, description="CPO/SimPO alpha — NLL loss weight"
    )
    # IPO-specific (uses DPO trainer with loss_type='ipo')
    ipo_tau: float = Field(
        default=0.1, gt=0, description="IPO tau — regularization strength"
    )
    # BCO-specific (Binary Classifier Optimization, v0.40.0 Part A)
    bco_beta: float = Field(
        default=0.1, gt=0, description="BCO beta — KL penalty coefficient"
    )
    # Unified preference loss dispatcher (v0.40.0 Part B).
    # Set when task='preference'. Legacy task strings ('dpo', 'simpo', ...)
    # remain first-class and are unaffected.
    preference_loss: Optional[Literal["dpo", "simpo", "orpo", "ipo", "bco"]] = Field(
        default=None,
        description=(
            "Preference loss for task='preference'. One of: dpo, simpo, orpo, "
            "ipo, bco. Mutually exclusive with task in {dpo, simpo, orpo, ipo, bco}."
        ),
    )
    # KL-controlled DPO variants (v0.40.0 Part C).
    dpo_beta_schedule: Optional[Literal["linear", "cosine", "exponential"]] = Field(
        default=None,
        description=(
            "Anneal DPO β over training. None = constant β (default). Requires "
            "dpo_beta_end. DPO-family tasks only (dpo, ipo, preference+dpo)."
        ),
    )
    dpo_beta_end: Optional[float] = Field(
        default=None,
        gt=0,
        description=(
            "Target β at the end of training when dpo_beta_schedule is set. "
            "Must be > 0. The starting β is dpo_beta."
        ),
    )
    dpo_ref_regen_epochs: Optional[int] = Field(
        default=None,
        ge=1,
        le=1000,
        description=(
            "Replace the frozen ref model with the current student every N "
            "epochs. None = never regen (default). DPO-family tasks only."
        ),
    )
    # Multi-objective preference loss (v0.40.0 Part D).
    preference_loss_weights: Optional[dict[str, float]] = Field(
        default=None,
        description=(
            "Weighted blend of preference losses, e.g. {'dpo': 0.7, 'bco': 0.3}. "
            "Each weight ∈ (0, 1]; weights must sum to 1.0 (±1e-6). All keys "
            "must be members of {dpo, simpo, orpo, ipo, bco}. Requires "
            "task='preference'; mutually exclusive with preference_loss (the "
            "scalar form). Capped at 5 components (the supported set)."
        ),
    )
    # GRPO-specific
    grpo_beta: float = Field(
        default=0.1, gt=0, description="GRPO beta — KL penalty coefficient"
    )
    num_generations: int = Field(
        default=4, ge=2, description="Number of generations per prompt for GRPO"
    )
    reward_fn: Optional[str] = Field(
        default="accuracy",
        description=(
            "Reward function: 'accuracy', 'format', 'verifiable', a path to a "
            "custom .py file, or a comma-separated ensemble of the above "
            "(e.g. 'accuracy,format') — the comma form is GRPO-only (v0.71.40)."
        ),
    )
    @field_validator("reward_fn", mode="before")
    @classmethod
    def _validate_reward_fn_field(cls, value: Any) -> Optional[str]:
        """v0.71.40 #311 — shape-only validation (containment enforced at load).

        Accepts a comma-separated spec ("accuracy,format"); rejects null bytes,
        oversize, and empty comma segments so a stray comma fails loud rather
        than silently dropping a reward.
        """
        if value is None:
            return None
        if isinstance(value, bool) or not isinstance(value, str):
            raise ValueError(
                f"reward_fn must be a string, got {type(value).__name__}"
            )
        if "\x00" in value:
            raise ValueError("reward_fn must not contain null bytes")
        if len(value) > 512:
            raise ValueError("reward_fn must be <= 512 chars")
        if not value.strip():
            raise ValueError("reward_fn must not be blank")
        if any(not seg.strip() for seg in value.split(",")):
            raise ValueError(
                "reward_fn has an empty comma segment — remove the stray comma"
            )
        return value

    # RLVR — verifiable reward domain (Part C of v0.25.0)
    verifiable_domain: Optional[Literal["math", "code", "json_schema"]] = Field(
        default=None,
        description=(
            "RLVR verifiable reward domain: math | code | json_schema. "
            "Required when reward_fn='verifiable'."
        ),
    )
    # v0.71.30 — PRM-guided GRPO: use a trained Soup PRM as the per-step
    # reward inside GRPO. ``prm_reward`` names the PRM directory (or HF id);
    # ``prm_aggregate`` folds the per-step scalars into one reward.
    prm_reward: Optional[str] = Field(
        default=None,
        description=(
            "Path (or HF id) to a Soup-trained PRM (task='prm') used as the "
            "GRPO per-step reward (v0.71.30). When set, the PRM replaces "
            "reward_fn. Requires task='grpo', backend='transformers', "
            "modality='text'."
        ),
    )
    prm_aggregate: Literal["min", "prod", "last"] = Field(
        default="min",
        description=(
            "How PRM per-step scores fold into one reward: min (weakest-link, "
            "default) | prod | last. Only meaningful when prm_reward is set. "
            "NOTE: 'prod' assumes per-step scores are bounded in ~[0,1] (a "
            "probability of step-correctness). Soup's PRM head is trained with "
            "unconstrained MSE regression, so 'prod' can blow up / flip sign on "
            "unbounded labels — prefer the default 'min' unless your PRM labels "
            "are calibrated to [0,1]."
        ),
    )

    @field_validator("prm_reward", mode="before")
    @classmethod
    def _validate_prm_reward_field(cls, value: Any) -> Optional[str]:
        """v0.71.30 — shape-only validation (containment enforced at load)."""
        if value is None:
            return None
        if isinstance(value, bool) or not isinstance(value, str):
            raise ValueError(
                f"prm_reward must be a string path/id, got {type(value).__name__}"
            )
        if not value:
            raise ValueError("prm_reward must not be an empty string")
        if "\x00" in value:
            raise ValueError("prm_reward must not contain null bytes")
        if len(value) > 512:
            raise ValueError("prm_reward must be <= 512 chars")
        return value

    # v0.71.31 — Online DPO (task='online_dpo'): on-policy generation judged by
    # a pairwise judge (online_dpo_judge URL) OR a reward_model. beta reuses
    # dpo_beta; loss_type + max_new_tokens map to OnlineDPOConfig.
    online_dpo_judge: Optional[str] = Field(
        default=None,
        description=(
            "Judge URL (ollama://model | https://... | http://localhost) for "
            "task='online_dpo'. Mutually exclusive with reward_model."
        ),
    )
    online_dpo_loss_type: Literal["sigmoid", "ipo"] = Field(
        default="sigmoid",
        description="Online DPO loss type (maps to OnlineDPOConfig.loss_type).",
    )
    online_dpo_max_new_tokens: int = Field(
        default=64,
        ge=1,
        le=4096,
        description="Max new tokens generated per online-DPO step.",
    )

    @field_validator("online_dpo_judge", mode="before")
    @classmethod
    def _validate_online_dpo_judge_field(cls, value: Any) -> Optional[str]:
        """v0.71.31 — shape-only validation (SSRF enforced at trainer setup)."""
        if value is None:
            return None
        if isinstance(value, bool) or not isinstance(value, str):
            raise ValueError(
                f"online_dpo_judge must be a string URL, got {type(value).__name__}"
            )
        if not value.strip():
            raise ValueError("online_dpo_judge must be a non-empty string")
        if "\x00" in value:
            raise ValueError("online_dpo_judge must not contain null bytes")
        if len(value) > 512:
            raise ValueError("online_dpo_judge must be <= 512 chars")
        return value

    # v0.71.32 — ASR (Whisper) fine-tuning knobs.
    asr_language: Optional[str] = Field(
        default=None,
        description=(
            "Target language for task='asr' (Whisper), e.g. 'en' or 'spanish'. "
            "Sets the forced decoder prompt; None uses the model default / "
            "language detection."
        ),
    )
    asr_task: Literal["transcribe", "translate"] = Field(
        default="transcribe",
        description=(
            "Whisper decoding objective for task='asr': 'transcribe' (same "
            "language) or 'translate' (to English)."
        ),
    )
    asr_lora: bool = Field(
        default=False,
        description=(
            "Opt-in LoRA for task='asr' (adapts q/v attention projections). "
            "Default False = full fine-tune (tiny Whisper fits a 4 GB GPU). "
            "Mirrors classifier_lora — the standard training.lora block still "
            "supplies r/alpha/dropout when enabled."
        ),
    )

    @field_validator("asr_language", mode="before")
    @classmethod
    def _validate_asr_language_field(cls, value: Any) -> Optional[str]:
        """v0.71.32 — shape-only validation of the ASR language code."""
        if value is None:
            return None
        if isinstance(value, bool) or not isinstance(value, str):
            raise ValueError(
                f"asr_language must be a string, got {type(value).__name__}"
            )
        if not value.strip():
            raise ValueError("asr_language must be a non-empty string")
        if "\x00" in value:
            raise ValueError("asr_language must not contain null bytes")
        if len(value) > 32:
            raise ValueError("asr_language must be <= 32 chars")
        return value

    # v0.50.0 Part A — GRPO objective variants (unsloth + axolotl parity).
    # Schema-only in v0.50.0; live loss kernels wired in v0.50.1.
    grpo_variant: Optional[Literal[
        "standard", "gspo", "dapo", "dr_grpo", "bnpo", "two_sided", "rft"
    ]] = Field(
        default=None,
        description=(
            "GRPO objective variant: standard | gspo | dapo | dr_grpo | "
            "bnpo | two_sided | rft. Defaults to None (legacy GRPO). "
            "Requires task='grpo'. Live wiring for v0.50.0 additions is "
            "deferred to v0.50.1 (schema gate only)."
        ),
    )
    grpo_delta: Optional[float] = Field(
        default=None,
        gt=0.0,
        le=1.0,
        description=(
            "Symmetric clipping radius for grpo_variant='two_sided' (required) "
            "or grpo_variant='gspo' (optional sequence clipping radius, "
            "defaults to 0.2). Rejected for all other variants."
        ),
    )
    grpo_fp16: bool = Field(
        default=False,
        description=(
            "Force FP16 mixed precision for GRPO/RL. Soup currently has "
            "FP8 RL support; this flag is the explicit FP16 opt-in (unsloth "
            "parity). v0.50.0: schema-only; live mixed-precision routing "
            "deferred to v0.50.1."
        ),
    )
    # v0.50.0 Part B — Long-context + memory-efficient RL
    long_context_grpo: bool = Field(
        default=False,
        description=(
            "Enable long-context GRPO (unsloth: 380K B200 / 110K H100). "
            "Wires Tiled MLP from v0.56.0 Part A; schema-only in v0.50.0. "
            "Requires task='grpo' on a non-mlx backend and "
            "use_ring_attention=False (both rewrite attention)."
        ),
    )
    vllm_sleep_mode: bool = Field(
        default=False,
        description=(
            "Enable vLLM sleep/standby between rollouts (memory savings "
            "during the optimisation step). Requires backend in "
            "{transformers, unsloth}. Schema-only in v0.50.0; live wiring "
            "in v0.50.1."
        ),
    )
    # v0.50.0 Part C — Multi-turn agent rollout backend (live v0.71.21 #125)
    rollout_backend: Optional[Literal[
        "art", "ruler", "nemo_gym", "openenv"
    ]] = Field(
        default=None,
        description=(
            "Multi-turn agent rollout backend (unsloth / axolotl parity): "
            "art (OpenPipe ART) / ruler / nemo_gym / openenv. "
            "Requires task='grpo'. openenv runs live (v0.71.21 #125) via "
            "training.rollout_func; art/ruler/nemo_gym are lazy-import "
            "gated."
        ),
    )
    # v0.71.21 #125 — user-supplied OpenEnv rollout callable.
    rollout_func: Optional[str] = Field(
        default=None,
        description=(
            "OpenEnv rollout function as 'module.path:function_name' "
            "(v0.71.21 #125). Requires rollout_backend='openenv'. The "
            "callable receives the seed prompts list and returns rollout "
            "rows ({'prompt': str|messages, 'answer'?: str}) that replace "
            "the GRPO prompt dataset. Trusted-input policy: names "
            "operator-controlled code (mirrors data.prompt_strategy)."
        ),
    )

    @field_validator("rollout_func", mode="before")
    @classmethod
    def _validate_rollout_func_field(cls, value):
        """v0.71.21 #125 — module:fn shape validation at config load."""
        from soup_cli.utils.agent_rollout import validate_rollout_func

        return validate_rollout_func(value)
    # v0.50.0 Part D — GRPO stability / efficiency knobs (axolotl + unsloth).
    # All schema-only in v0.50.0; live trainer callbacks wired in v0.50.1.
    ref_model_ema_alpha: Optional[float] = Field(
        default=None,
        gt=0.0,
        le=1.0,
        description=(
            "Exponential moving average coefficient for ref-model sync "
            "(policy → reference). Must be in (0, 1]. None = disabled. "
            "Axolotl parity."
        ),
    )
    replay_buffer_size: Optional[int] = Field(
        default=None,
        ge=1,
        le=1_000_000,
        description=(
            "Bounded replay buffer size for GRPO rollouts. None = disabled. "
            "Axolotl parity."
        ),
    )
    async_grpo_prefetch: bool = Field(
        default=False,
        description=(
            "Overlap rollout + train via async prefetch (axolotl). "
            "Requires backend in {transformers, unsloth}."
        ),
    )
    tis_threshold: Optional[float] = Field(
        default=None,
        gt=0.0,
        le=100.0,
        description=(
            "Truncated importance sampling threshold (unsloth, axolotl). "
            "Must be in (0, 100]. None = disabled."
        ),
    )
    mask_truncated_completions: bool = Field(
        default=False,
        description=(
            "Mask out truncated completions when computing the policy "
            "gradient (paired with tis_threshold). Unsloth + axolotl parity."
        ),
    )
    defer_rerolling: bool = Field(
        default=False,
        description=(
            "Defer re-rolling identical prompts across optimisation steps "
            "(axolotl). Saves rollouts on repeat prompts."
        ),
    )
    skip_zero_advantage: bool = Field(
        default=False,
        description=(
            "Skip backward pass on samples whose advantage is exactly zero "
            "(axolotl). Avoids wasted compute on no-signal samples."
        ),
    )
    off_policy_mask_threshold: Optional[float] = Field(
        default=None,
        ge=0.0,
        le=1.0,
        description=(
            "Off-policy mask threshold for token/sequence gating (axolotl). "
            "Must be in [0, 1]. None = disabled."
        ),
    )
    # v0.50.0 Part E — Vision-RL opt-in flag
    vision_grpo: bool = Field(
        default=False,
        description=(
            "Enable Vision RL / VLM RL — extends GRPO/PPO to vision "
            "modality (Qwen2-VL / Pixtral / InternVL). Requires "
            "modality='vision', task in {grpo, ppo}, backend in "
            "{transformers, unsloth}. Schema-only in v0.50.0; live VLM-RL "
            "rollout wiring deferred to v0.50.1."
        ),
    )
    # v0.51.0 Part E — alternative model hubs (ModelScope / Modelers)
    hub: Literal["hf", "modelscope", "modelers"] = Field(
        default="hf",
        description=(
            "Model hub for downloads + pushes. 'hf' (default), 'modelscope' "
            "(China-hosted; mirrors most Llama/Qwen/etc.), 'modelers' "
            "(Openmind hub). Schema-only in v0.51.0; live downloader / "
            "uploader wiring deferred to v0.51.1."
        ),
    )

    # ---- v0.52.0 — Modality II (schema-only; live wiring in v0.52.1) ----
    # Part A — TTS
    tts_family: Optional[Literal[
        "orpheus", "sesame_csm", "llasa", "spark", "oute"
    ]] = Field(
        default=None,
        description=(
            "TTS model family — required when task='tts'. One of: orpheus, "
            "sesame_csm, llasa, spark, oute. Schema-only in v0.52.0; live "
            "trainer wrapper deferred to v0.52.1."
        ),
    )
    tts_emotion: Optional[str] = Field(
        default=None,
        description=(
            "Optional emotion tag for emotion-conditioned families "
            "(Orpheus / Oute). Allowlisted per-family. (v0.52.0)"
        ),
    )
    # Part B — classifier / reranker / cross_encoder
    num_labels: Optional[int] = Field(
        default=None, ge=1, le=1024,
        description=(
            "Number of output labels for task in (classifier, reranker, "
            "cross_encoder). Required when task is one of those. (v0.52.0)"
        ),
    )
    classifier_kind: Optional[Literal["single_label", "multi_label"]] = Field(
        default=None,
        description=(
            "Sequence-classification head kind: single_label (default for "
            "task='classifier') or multi_label. (v0.52.0)"
        ),
    )
    label_names: Optional[List[str]] = Field(
        default=None,
        description=(
            "Optional human-readable label names. Length must match "
            "num_labels. Capped at 1024 entries. (v0.52.0)"
        ),
    )
    # MoLE per-token adapter routing (v0.67.0 schema / v0.71.12 #222 live)
    mole_task_adapters: Optional[List[str]] = Field(
        default=None,
        description=(
            "Paths (HF ids or local dirs) to the N pre-trained task LoRA "
            "adapters routed over by the MoLE gate. Required when "
            "task='moe_lora_routing'; 2-64 entries, deduplicated. The base "
            "model + every task adapter stay frozen — only the gate trains. "
            "(v0.71.12 #222)"
        ),
    )
    mole_top_k: Optional[int] = Field(
        default=None,
        description=(
            "Number of task adapters each token is routed to (sparse top-k "
            "dispatch). Defaults to num_task_adapters (dense) when unset. "
            "1 <= top_k <= len(mole_task_adapters). (v0.71.12 #222)"
        ),
    )
    mole_temperature: Optional[float] = Field(
        default=None,
        description=(
            "Softmax temperature for the MoLE router (default 1.0). "
            "(1e-6, 100.0]. (v0.71.12 #222)"
        ),
    )
    # Part C — knowledge distillation
    teacher_model: Optional[str] = Field(
        default=None,
        description=(
            "Teacher model HF id or local path — required when task='distill'. "
            "Null-byte rejected, capped at 512 chars. Schema-only in v0.52.0; "
            "live distill trainer deferred to v0.52.1."
        ),
    )
    distill_divergence: Optional[Literal[
        "forward_kl", "reverse_kl", "js"
    ]] = Field(
        default=None,
        description=(
            "Divergence used for distillation loss. 'kl' is an alias for "
            "'forward_kl' (canonical form). (v0.52.0)"
        ),
    )
    distill_temperature: Optional[float] = Field(
        default=None,
        description=(
            "Softmax temperature applied to teacher and student logits "
            "before the divergence. Bounded [0.05, 100.0]. (v0.52.0)"
        ),
    )
    distill_mode: Literal["token", "sequence"] = Field(
        default="token",
        description=(
            "Distillation mode. 'token' (default, v0.53.2) = column-aligned "
            "logit KL — requires a shared tokenizer (or set uld_strategy for "
            "the cross-tokenizer logit path). 'sequence' (v0.71.12) = "
            "sequence-level KD: the teacher GENERATES a completion per prompt "
            "and the student does plain CE on the re-tokenised output, which "
            "works across ANY tokenizer pair (e.g. Llama-3 student / Qwen-2 "
            "teacher)."
        ),
    )
    # v0.71.12 #146 — opt-in LoRA / PEFT path for classifier-family tasks.
    classifier_lora: bool = Field(
        default=False,
        description=(
            "Wrap the sequence-classification head with LoRA (task_type="
            "'SEQ_CLS') instead of full fine-tuning. Opt-in (default False "
            "preserves the v0.53.2 full-finetune behaviour). Reuses the "
            "training.lora block (r / alpha / dropout / target_modules). "
            "Only honored for task in (classifier, reranker, cross_encoder). "
            "(v0.71.12 #146)"
        ),
    )
    # Part E — EBFT + GDPO
    ebft_variant: Optional[Literal["structured", "strided"]] = Field(
        default=None,
        description=(
            "Energy-Based FT variant. SFT-task-only; live loss kernel "
            "deferred to v0.52.1. (v0.52.0)"
        ),
    )
    ebft_temperature: Optional[float] = Field(
        default=None,
        description=(
            "Sampling temperature for EBFT energy proxy. Bounded "
            "[1e-4, 100.0]. (v0.52.0)"
        ),
    )
    gdpo_variant: Optional[Literal[
        "standard", "length_normalized", "margin"
    ]] = Field(
        default=None,
        description=(
            "Generalized DPO variant. DPO-family-task-only; live loss kernel "
            "deferred to v0.52.1. (v0.52.0)"
        ),
    )
    # Part F — MoE expert quantization + router-only training
    moe_expert_quant: Optional[Literal["nf4", "int8_rowwise"]] = Field(
        default=None,
        description=(
            "Per-expert quantization for fused-MoE Linear blocks. "
            "Requires moe_lora=true. (v0.52.0)"
        ),
    )
    train_router_only: bool = Field(
        default=False,
        description=(
            "Freeze every expert + train only the gating router (unsloth "
            "MoE recipe). Requires moe_lora=true. (v0.52.0)"
        ),
    )
    # Part G — gpt-oss reasoning effort + EOT control
    reasoning_effort: Optional[Literal["low", "medium", "high"]] = Field(
        default=None,
        description=(
            "gpt-oss train-time reasoning effort level. Routes through a "
            "prompt prefix at training time; live formatter wiring deferred "
            "to v0.52.1. (v0.52.0)"
        ),
    )
    train_on_eot: bool = Field(
        default=False,
        description=(
            "Include explicit EOT / EOS control tokens in the SFT loss "
            "(axolotl ``train_on_eot``). Default False matches HF Trainer "
            "convention. (v0.52.0)"
        ),
    )
    # ---- v0.53.0 Quant Menu II — UD GGUFs + KV cache + NVFP4 ---------------
    # Part C — KV cache types (serve-side hint, captured here for round-trip).
    kv_cache_type: Optional[Literal["q8_0", "bf16", "f16", "fp8"]] = Field(
        default=None,
        description=(
            "KV-cache element type for inference (q8_0 / bf16 / f16 / fp8). "
            "Schema-only in v0.53.0; live wiring deferred to v0.53.1."
        ),
    )
    # Part D — Train-time advanced precision.
    fp8_attention: bool = Field(
        default=False,
        description=(
            "Extend the v0.28.0 FP8 menu to FP8 attention "
            "(axolotl-parity flag). Requires quantization_aware='fp8'. "
            "Schema-only in v0.53.0; live wiring deferred to v0.53.1."
        ),
    )
    nvfp4: bool = Field(
        default=False,
        description=(
            "Blackwell-only NVFP4 training (unsloth + axolotl). "
            "Schema-only in v0.53.0; live wiring deferred to v0.53.1."
        ),
    )
    unsloth_bnb_4bit: bool = Field(
        default=False,
        description=(
            "Promote Unsloth Dynamic 4-bit from 'inferable' to a native flag. "
            "Requires backend='unsloth' and quantization='4bit'. (v0.53.0)"
        ),
    )
    # Part E — LF / Axolotl parity.
    bnb_4bit_use_double_quant: Optional[bool] = Field(
        # #321 — tri-state so an unset flag round-trips cleanly. Every 4-bit load
        # path has always double-quantized, so `None` (unset) means "use the
        # shipped default", which each 4-bit consumer resolves to True. A literal
        # `default=True` here would make `model_dump()` emit the key on EVERY
        # config, so re-validating a dumped non-4bit config (train.py replay,
        # sweep round-trips, the 21 bundled recipes) would trip the footgun below.
        # With `None` the footgun fires only on an explicit `true`. Only
        # meaningful when quantization='4bit'.
        default=None,
        description=(
            "Apply BNB 4-bit double-quantization (LF / Axolotl parity). "
            "Unset means the shipped default (on, to match every 4-bit load "
            "path); set false to disable it. Only meaningful when "
            "quantization='4bit'. (v0.53.0)"
        ),
    )

    @property
    def double_quant_on(self) -> bool:
        """Resolve the tri-state ``bnb_4bit_use_double_quant`` for the 4-bit load
        paths: unset (``None``) uses the shipped default (double-quantize), so
        every consumer that builds a ``BitsAndBytesConfig`` reads a real bool and
        stays in agreement (#321)."""
        return self.bnb_4bit_use_double_quant is not False

    llm_int8: bool = Field(
        default=False,
        description=(
            "Explicit 8-bit LLM.int8 alias for quantization='8bit'. "
            "When True, requires quantization='8bit'. (v0.53.0)"
        ),
    )
    quantize_ref_model: bool = Field(
        default=False,
        description=(
            "Apply the same Quant Menu config to the reference model "
            "(DPO/IPO/SimPO/ORPO/BCO ref model) — extends v0.40.5. (v0.53.0)"
        ),
    )
    quantize_reward_model: bool = Field(
        default=False,
        description=(
            "Apply the same Quant Menu config to the reward model "
            "(PPO/reward_model task) — extends v0.40.5. (v0.53.0)"
        ),
    )

    # ---- v0.70.0 Part F — Echo-trap detector -----------------------------
    echo_trap_enabled: bool = Field(
        default=False,
        description=(
            "Enable RAGEN-style echo-trap detection during multi-turn "
            "agent RL. Requires task in {'grpo', 'ppo'} on a non-mlx "
            "backend. Schema-only in v0.70.0; live callback in v0.70.1."
        ),
    )
    echo_trap_threshold: float = Field(
        default=0.6,
        ge=0.0,
        le=1.0,
        description=(
            "Threshold on the aggregate echo signal. Above this = TRAP. "
            "Bounded [0.0, 1.0]. (v0.70.0)"
        ),
    )
    echo_trap_halt: bool = Field(
        default=False,
        description=(
            "Auto-halt training on TRAP verdict. Requires "
            "echo_trap_enabled=True. (v0.70.0)"
        ),
    )
    echo_trap_tokenizer_aware: bool = Field(
        default=False,
        description=(
            "Use tokenizer-id n-grams for echo-trap scoring instead of "
            "whitespace tokens. More sensitive to subword repetition but "
            "bound to the active tokenizer vocabulary. Requires "
            "echo_trap_enabled=True. (v0.70.x)"
        ),
    )

    # ---- v0.70.0 Part D — Mid-epoch RL checkpoint ------------------------
    rl_checkpoint_save_every_steps: Optional[int] = Field(
        default=None,
        ge=1,
        le=10_000_000,
        description=(
            "Save an RL-aware mid-epoch checkpoint every N steps. None "
            "= use HF Trainer's per-epoch checkpoint only. Requires "
            "task in {'grpo', 'ppo'}. Schema-only in v0.70.0; live "
            "save_state / load_state in v0.70.1."
        ),
    )
    rl_checkpoint_keep_last: int = Field(
        default=3,
        ge=1,
        le=100,
        description=(
            "Number of recent RL checkpoints to retain. Older ones are "
            "pruned at write time. (v0.70.0)"
        ),
    )
    rl_checkpoint_include_optimizer: bool = Field(
        default=True,
        description=(
            "Include AdamW / Lion optimizer state in the mid-epoch RL "
            "checkpoint. (v0.70.0)"
        ),
    )
    rl_checkpoint_include_ref_model: bool = Field(
        default=False,
        description=(
            "Include the frozen reference model state in the RL "
            "checkpoint. Default False (ref model is reconstructable "
            "from cfg.base). (v0.70.0)"
        ),
    )
    rl_checkpoint_include_rollout_buffer: bool = Field(
        default=False,
        description=(
            "Include the rollout / replay buffer in the RL checkpoint "
            "so resumed runs don't lose collected experience. (v0.70.0)"
        ),
    )

    # ---- v0.70.0 Part C — MiniLLM reverse-KL on-policy distillation -------
    # Bundles teacher-mixed sampling + length-norm + pretrain anchor.
    # Schema-only; live callback wired in v0.70.1.
    minillm_enabled: bool = Field(
        default=False,
        description=(
            "Enable MiniLLM-style on-policy distillation (Gu et al. 2024). "
            "Requires task='distill' on a non-mlx backend. v0.70.0 "
            "schema-only; live callback in v0.70.1."
        ),
    )
    minillm_teacher_mix_ratio: float = Field(
        default=0.0,
        ge=0.0,
        le=1.0,
        description=(
            "Probability of sampling from the teacher distribution at "
            "rollout time. 0.0 = student-only; 1.0 = teacher-only. "
            "Typical range 0.2-0.5. (v0.70.0)"
        ),
    )
    minillm_length_normalize: bool = Field(
        default=True,
        description=(
            "Length-normalise the rollout log-probability before the "
            "reverse-KL term. Prevents long completions from dominating "
            "the gradient. (v0.70.0)"
        ),
    )
    minillm_pretrain_anchor_weight: float = Field(
        default=0.0,
        ge=0.0,
        le=1.0,
        description=(
            "Weight on the pretrain-loss anchor term (SFT on a small "
            "pretrain corpus). Prevents drift away from coherent "
            "language. Requires minillm_pretrain_anchor_path when > 0. "
            "(v0.70.0)"
        ),
    )
    minillm_pretrain_anchor_path: Optional[str] = Field(
        default=None,
        description=(
            "Path to the pretrain JSONL used by the anchor term. "
            "Required when minillm_pretrain_anchor_weight > 0. "
            "Null-byte rejected; capped at 4096 chars. (v0.70.0)"
        ),
    )
    minillm_on_policy: bool = Field(
        default=False,
        description=(
            "Use the TRUE on-policy MiniLLM teacher-mixed rollout (Gu et al. "
            "2024 §3.1): sample a fresh autoregressive rollout per step "
            "(per-token teacher/student mix) then compute length-normalised "
            "reverse-KL on it. Default off = the cheap offline distribution "
            "blend. Requires minillm_enabled=True. (v0.71.18 #257)"
        ),
    )
    minillm_rollout_length: Optional[int] = Field(
        default=None,
        ge=1,
        le=512,
        description=(
            "On-policy rollout length (number of generated tokens per step). "
            "When None the distill trainer auto-derives min(max_length, 32) "
            "for the consumer-GPU budget. The autoregressive loop re-forwards "
            "the full growing prefix each step (~O(L^2) graph), so keep this "
            "small. Requires minillm_on_policy=True. (v0.71.18 #257)"
        ),
    )

    # ---- v0.70.0 Part B — Cross-tokenizer ULD ----------------------------
    # Universal Logit Distillation (Boizard et al. 2024). Schema-only;
    # live projection module wired in v0.70.1.
    uld_strategy: Optional[
        Literal["wasserstein", "topk_align", "wasserstein_aligned"]
    ] = Field(
        default=None,
        description=(
            "Cross-tokenizer distillation strategy: 'wasserstein' "
            "(no alignment needed), 'topk_align' (requires uld_top_k), or "
            "'wasserstein_aligned' (token-sequence alignment for fully "
            "disjoint tokenizers, v0.71.18 #258). Requires task='distill' on "
            "a non-mlx backend."
        ),
    )
    uld_top_k: Optional[int] = Field(
        default=None,
        ge=1,
        le=262144,
        description=(
            "Top-K teacher logits to align (uld_strategy='topk_align' "
            "only). Bounded [1, 262144] to cap pathological vocabs. "
            "(v0.70.0)"
        ),
    )
    # ---- v0.70.0 Part A — Reward-hacking detector ------------------------
    # Schema-only release; live HF Trainer callback wired in v0.70.1.
    reward_hack_detector: Optional[Literal["info_rm", "rm_ensemble"]] = Field(
        default=None,
        description=(
            "Reward-hacking detector for GRPO/PPO. 'info_rm' tracks "
            "InfoRM cluster-separation across training; 'rm_ensemble' "
            "tracks pairwise variance across an RM ensemble. Requires "
            "task in {'grpo', 'ppo'} on a non-mlx backend. Schema-only "
            "in v0.70.0; live HF Trainer callback wired in v0.70.1."
        ),
    )
    reward_hack_halt: bool = Field(
        default=False,
        description=(
            "Auto-halt training on HACK verdict (drop_pct >= 30% in "
            "cluster separation). Requires reward_hack_detector to be "
            "set. (v0.70.0)"
        ),
    )

    # ---- v0.71.26 — Closed-loop reward-hacking auto-mitigation -----------
    reward_hack_mitigation: Literal[
        "off", "log_only", "kl_control", "pid_lagrangian"
    ] = Field(
        default="off",
        description=(
            "Closed-loop reward-hacking mitigation mode (v0.71.26). 'off' = "
            "no mitigation; 'log_only' = instrument + append a mitigation "
            "log without touching training; 'kl_control' = reversible "
            "bang-bang KL/β controller; 'pid_lagrangian' = PID-Lagrangian "
            "controller + rollback escalation ladder. Any non-'off' mode "
            "requires reward_hack_detector (the signal source) and task in "
            "{'grpo', 'ppo'} on a non-mlx backend."
        ),
    )
    reward_hack_beta_floor: float = Field(
        default=0.02,
        gt=0.0,
        description=(
            "v0.71.26 — lower β/kl_coef bound for the mitigation controller. "
            "Must be > 0 (β=0 gates off the ref-log-prob path at generation)."
        ),
    )
    reward_hack_beta_ceil: float = Field(
        default=1.0,
        gt=0.0,
        le=1000.0,
        description=(
            "v0.71.26 — upper β/kl_coef bound for the mitigation controller. "
            "Must be > reward_hack_beta_floor."
        ),
    )
    reward_hack_trip_band: float = Field(
        default=0.30,
        ge=0.0,
        le=1.0,
        description=(
            "v0.71.26 — hacking drop_pct at/above which the controller wants "
            "to RAISE β. Must be > reward_hack_release_band."
        ),
    )
    reward_hack_release_band: float = Field(
        default=0.10,
        ge=0.0,
        le=1.0,
        description=(
            "v0.71.26 — hacking drop_pct at/below which the controller wants "
            "to RELAX β. Must be < reward_hack_trip_band."
        ),
    )
    reward_hack_dwell_steps: int = Field(
        default=2,
        ge=1,
        le=100_000,
        description=(
            "v0.71.26 — consecutive trip-band steps required before the "
            "controller raises β (hysteresis)."
        ),
    )
    reward_hack_release_patience: int = Field(
        default=3,
        ge=1,
        le=100_000,
        description=(
            "v0.71.26 — consecutive release-band steps required before the "
            "controller relaxes β."
        ),
    )
    reward_hack_kl_gain: float = Field(
        default=1.5,
        gt=1.0,
        le=100.0,
        description=(
            "v0.71.26 — multiplicative β step per trip (>1). β is multiplied "
            "on trip / divided on release, clamped to [floor, ceil]."
        ),
    )
    reward_hack_signals: List[str] = Field(
        default_factory=lambda: ["info_rm"],
        max_length=4,
        description=(
            "v0.71.26 — signals combined into the controller's multi-signal "
            "vote. Allowlist (max 4): info_rm, rm_ensemble, length_trend, "
            "repetition."
        ),
    )
    # ---- v0.71.26 Stage 2 — PID-Lagrangian controller + rollback ---------
    reward_hack_pid_kp: float = Field(
        default=0.5,
        ge=0.0,
        le=1000.0,
        description="v0.71.26 — PID proportional gain (pid_lagrangian mode).",
    )
    reward_hack_pid_ki: float = Field(
        default=0.1,
        ge=0.0,
        le=1000.0,
        description="v0.71.26 — PID integral gain (pid_lagrangian mode).",
    )
    reward_hack_pid_kd: float = Field(
        default=0.05,
        ge=0.0,
        le=1000.0,
        description="v0.71.26 — PID derivative gain (pid_lagrangian mode).",
    )
    reward_hack_signal_target: float = Field(
        default=0.15,
        ge=0.0,
        lt=1.0,
        description=(
            "v0.71.26 — target hacking drop_pct the PID controller holds "
            "(pid_lagrangian mode)."
        ),
    )
    reward_hack_integral_clamp: float = Field(
        default=1.0,
        gt=0.0,
        le=1000.0,
        description=(
            "v0.71.26 — PID anti-windup bound on the integral accumulator "
            "(pid_lagrangian mode). Independent of beta_ceil."
        ),
    )
    reward_hack_rollback: bool = Field(
        default=False,
        description=(
            "v0.71.26 — enable rollback to the last-good RL checkpoint in the "
            "escalation ladder. Requires rl_checkpoint_save_every_steps set."
        ),
    )
    reward_hack_rollback_patience: int = Field(
        default=3,
        ge=1,
        le=100_000,
        description=(
            "v0.71.26 — consecutive HACK steps before a rollback is triggered."
        ),
    )
    reward_hack_max_recovery_attempts: int = Field(
        default=2,
        ge=0,
        le=1000,
        description=(
            "v0.71.26 — max rollbacks before the controller early-stops "
            "training (terminal rung of the escalation ladder)."
        ),
    )
    # ---- v0.71.26 Stage 3 — anti-gaming hardening ------------------------
    reward_hack_signal_smoothing: Literal["none", "ema", "median"] = Field(
        default="none",
        description=(
            "v0.71.26 — per-signal smoothing before the controller vote: "
            "'none', 'ema' (0.5·prev+0.5·new), or 'median' over a window."
        ),
    )
    reward_hack_smoothing_window: int = Field(
        default=8,
        ge=2,
        le=256,
        description="v0.71.26 — window length for signal smoothing.",
    )
    reward_hack_conservative_on_disagreement: bool = Field(
        default=False,
        description=(
            "v0.71.26 — when detectors disagree, keep KL high (use the MAX "
            "signal) instead of relaxing."
        ),
    )
    reward_hack_reward_shaping: bool = Field(
        default=False,
        description=(
            "v0.71.26 — apply a bounded penalty on the gamed proxy "
            "(length/repetition/sentinel) via a shaping shim over the reward "
            "fn. Requires reward_hack_shaping_strength > 0 and a control mode."
        ),
    )
    reward_hack_shaping_kind: Literal["length", "repetition", "sentinel"] = Field(
        default="length",
        description=(
            "v0.71.26 — which gamed proxy the reward-shaping shim penalises."
        ),
    )
    reward_hack_shaping_strength: float = Field(
        default=0.0,
        ge=0.0,
        le=1.0,
        description=(
            "v0.71.26 — magnitude of the bounded reward-shaping penalty [0, 1]."
        ),
    )

    @field_validator(
        "reward_hack_rollback",
        "reward_hack_conservative_on_disagreement",
        "reward_hack_reward_shaping",
        mode="before",
    )
    @classmethod
    def _validate_reward_hack_bool_fields(cls, v):
        """v0.71.26 — bool guard so YAML ``yes`` / ``1`` cannot silently coerce."""
        if v is None or isinstance(v, bool):
            return v
        raise TypeError(
            f"reward-hack bool flag must be bool, got {type(v).__name__}"
        )

    @field_validator(
        "reward_hack_dwell_steps",
        "reward_hack_release_patience",
        "reward_hack_rollback_patience",
        "reward_hack_max_recovery_attempts",
        "reward_hack_smoothing_window",
        "reward_hack_beta_floor",
        "reward_hack_beta_ceil",
        "reward_hack_trip_band",
        "reward_hack_release_band",
        "reward_hack_kl_gain",
        "reward_hack_pid_kp",
        "reward_hack_pid_ki",
        "reward_hack_pid_kd",
        "reward_hack_signal_target",
        "reward_hack_integral_clamp",
        "reward_hack_shaping_strength",
        mode="before",
    )
    @classmethod
    def _reject_bool_on_reward_hack_numerics(cls, v):
        """v0.71.26 — bool-before-int/float policy (security-review MEDIUM): a
        YAML ``yes`` must not silently coerce to 1 on a numeric tunable."""
        if isinstance(v, bool):
            raise ValueError(
                "reward-hack numeric tunable must not be bool "
                "(YAML on/off/yes/no coerces to a number)"
            )
        return v

    @field_validator("reward_hack_mitigation", mode="before")
    @classmethod
    def _coerce_reward_hack_mitigation(cls, v):
        """v0.71.26 — DWIM for the YAML-1.1 boolean coercion footgun.

        Unquoted ``off`` / ``no`` parse as bool ``False`` under YAML 1.1;
        map that back to the ``"off"`` mode so ``reward_hack_mitigation: off``
        works without quotes. ``on`` / ``yes`` / ``true`` parse as ``True``,
        which is ambiguous (no ``"on"`` mode) — reject with a hint to quote.
        """
        if v is False:
            return "off"
        if v is True:
            raise ValueError(
                "reward_hack_mitigation got boolean True (YAML coerced "
                "on/yes/true) — quote the mode explicitly, e.g. "
                "reward_hack_mitigation: 'log_only'"
            )
        return v

    @field_validator("reward_hack_halt", mode="before")
    @classmethod
    def _validate_reward_hack_halt(cls, v):
        """v0.70.0 — explicit bool guard so YAML ``yes`` / ``1`` integers
        cannot silently coerce. Matches project bool-before-int policy.
        """
        if v is None:
            return v
        if isinstance(v, bool):
            return v
        raise TypeError(
            f"reward_hack_halt must be bool, got {type(v).__name__}"
        )

    @field_validator(
        "echo_trap_enabled",
        "echo_trap_halt",
        "echo_trap_tokenizer_aware",
        mode="before",
    )
    @classmethod
    def _validate_echo_trap_bool_fields(cls, v):
        """v0.70.0 Part F — bool guards for echo-trap toggles."""
        if v is None:
            return v
        if isinstance(v, bool):
            return v
        raise TypeError(
            f"v0.70.0 echo-trap flag must be bool, got {type(v).__name__}"
        )

    @field_validator(
        "rl_checkpoint_include_optimizer",
        "rl_checkpoint_include_ref_model",
        "rl_checkpoint_include_rollout_buffer",
        mode="before",
    )
    @classmethod
    def _validate_rl_checkpoint_bool_fields(cls, v):
        """v0.70.0 Part D — bool guards for RL-checkpoint toggles."""
        if v is None:
            return v
        if isinstance(v, bool):
            return v
        raise TypeError(
            f"v0.70.0 RL-checkpoint flag must be bool, got {type(v).__name__}"
        )

    @field_validator(
        "minillm_enabled",
        "minillm_length_normalize",
        "minillm_on_policy",
        mode="before",
    )
    @classmethod
    def _validate_minillm_bool_fields(cls, v):
        """v0.70.0 Part C — bool guards for MiniLLM toggles."""
        if v is None:
            return v
        if isinstance(v, bool):
            return v
        raise TypeError(
            f"v0.70.0 MiniLLM flag must be bool, got {type(v).__name__}"
        )

    @field_validator("minillm_pretrain_anchor_path")
    @classmethod
    def _validate_minillm_anchor_path(cls, v):
        """v0.70.0 Part C — shape-only path validation. Cwd containment
        deferred to v0.70.1 runtime hook (matches v0.69.0 build_dag /
        magpie base_model policy).
        """
        if v is None:
            return None
        from soup_cli.utils.minillm import _check_path_shape

        return _check_path_shape(v)

    @field_validator("minillm_rollout_length", mode="before")
    @classmethod
    def _validate_minillm_rollout_length(cls, v):
        """v0.71.18 #257 — reject bool before Pydantic coerces True->1."""
        if v is None:
            return v
        if isinstance(v, bool):
            raise TypeError("minillm_rollout_length must not be bool")
        return v

    @field_validator(
        "fp8_attention",
        "nvfp4",
        "unsloth_bnb_4bit",
        "bnb_4bit_use_double_quant",
        "llm_int8",
        "quantize_ref_model",
        "quantize_reward_model",
        mode="before",
    )
    @classmethod
    def _validate_v053_bool_fields(cls, v):
        """v0.53.0 — explicit bool guard so YAML ``yes`` / ``1`` integers
        cannot silently coerce. Matches project bool-before-int policy.

        ``None`` falls through to Pydantic so the field's ``default=False``
        applies (review-fix — avoids silent ``None → False`` coercion that
        would mask YAML typos like ``fp8_attention: ~``).
        """
        if v is None:
            return v
        if isinstance(v, bool):
            return v
        raise TypeError(
            f"v0.53.0 flag must be bool, got {type(v).__name__}"
        )

    @field_validator("kv_cache_type", mode="before")
    @classmethod
    def _validate_kv_cache_type(cls, v):
        """v0.53.0 Part C — bool / null-byte / oversize / case-insensitive
        normalisation via the shared helper.
        """
        if v is None:
            return None
        from soup_cli.utils.kv_cache import validate_kv_cache_type

        return validate_kv_cache_type(v)

    @field_validator("teacher_model")
    @classmethod
    def _validate_teacher_model(cls, v: Optional[str]) -> Optional[str]:
        """v0.52.0 Part C — null-byte rejection + 512-char cap.

        Mirrors v0.40.5 ``reward_model`` field-validator policy. The bool
        rejection happens inside ``validate_teacher_model`` which lives in
        ``utils/distill.py`` so the runtime validator and schema agree on
        what's accepted.
        """
        if v is None:
            return v
        from soup_cli.utils.distill import validate_teacher_model

        return validate_teacher_model(v)

    @field_validator("distill_divergence", mode="before")
    @classmethod
    def _normalize_distill_divergence(cls, v):
        """v0.52.0 Part C — canonicalise ``kl`` → ``forward_kl``.

        Mirrors v0.51.0 ``_normalize_hub`` policy: the field validator runs
        the shared ``validate_*`` helper at ``mode='before'`` so the public
        schema and runtime validator agree on what's accepted.
        """
        if v is None:
            return None
        from soup_cli.utils.distill import validate_divergence

        return validate_divergence(v)

    @field_validator("distill_temperature", mode="before")
    @classmethod
    def _validate_distill_temperature(cls, v):
        """v0.52.0 Part C — bool/NaN-rejected float in [0.05, 100]."""
        if v is None:
            return None
        from soup_cli.utils.distill import validate_distill_temperature

        return validate_distill_temperature(v)

    @field_validator("distill_mode", mode="before")
    @classmethod
    def _validate_distill_mode(cls, v):
        """v0.71.12 #145 — canonicalise + reject unknown distill modes."""
        if v is None:
            return "token"
        from soup_cli.utils.distill import validate_distill_mode

        return validate_distill_mode(v)

    @field_validator("mod_capacity_factor", mode="before")
    @classmethod
    def _validate_mod_capacity_factor(cls, v):
        """v0.71.12 #84 — bool-before-float guard (bool subclasses int)."""
        if v is None:
            return 0.125
        from soup_cli.utils.mod import validate_capacity_factor

        return validate_capacity_factor(v)

    @field_validator("ebft_temperature", mode="before")
    @classmethod
    def _validate_ebft_temperature(cls, v):
        """v0.52.0 Part E — bool/NaN-rejected float in [1e-4, 100]."""
        if v is None:
            return None
        from soup_cli.utils.ebft_gdpo import validate_ebft_temperature

        return validate_ebft_temperature(v)

    @field_validator("label_names")
    @classmethod
    def _validate_label_names(cls, v):
        """v0.52.0 Part B — dedup + per-entry validation."""
        if v is None:
            return None
        from soup_cli.utils.classifier import validate_label_names

        return validate_label_names(v)

    @field_validator("mole_task_adapters")
    @classmethod
    def _validate_mole_task_adapters(cls, v):
        """v0.71.12 #222 — 2-64 deduplicated non-empty path strings."""
        if v is None:
            return None
        from soup_cli.utils.mole_routing import validate_mole_task_adapters

        return validate_mole_task_adapters(v)

    @field_validator("mole_top_k", mode="before")
    @classmethod
    def _validate_mole_top_k(cls, v):
        """v0.71.12 #222 — bool-before-int guard (bool subclasses int)."""
        if v is None:
            return None
        if isinstance(v, bool):
            raise ValueError("mole_top_k must be int, not bool")
        if not isinstance(v, int):
            raise ValueError("mole_top_k must be int")
        if v < 1:
            raise ValueError(f"mole_top_k must be >= 1, got {v}")
        return v

    @field_validator("mole_temperature", mode="before")
    @classmethod
    def _validate_mole_temperature(cls, v):
        """v0.71.12 #222 — finite float in (1e-6, 100.0]."""
        if v is None:
            return None
        if isinstance(v, bool):
            raise ValueError("mole_temperature must not be bool")
        if not isinstance(v, (int, float)):
            raise ValueError("mole_temperature must be numeric")
        import math as _math

        fv = float(v)
        if not _math.isfinite(fv):
            raise ValueError("mole_temperature must be finite")
        # Boundary matches MoleGatingConfig._check_finite_positive
        # (MIN_TEMPERATURE=1e-6 inclusive): reject < 1e-6, accept == 1e-6.
        if fv < 1e-6 or fv > 100.0:
            raise ValueError(
                f"mole_temperature must be in [1e-6, 100.0], got {fv}"
            )
        return fv

    @field_validator("num_labels", mode="before")
    @classmethod
    def _validate_num_labels(cls, v):
        """v0.52.0 Part B (security review fix) — bool-before-int guard.

        Pydantic v2's ``Field(ge=1, le=1024)`` accepts ``True`` because bool
        subclasses int; explicit guard matches the project policy
        established in v0.30.0 ``Candidate`` / v0.36.0 ``make_cache_key`` /
        v0.41.0 ``expand_layers`` / v0.50.0 GRPO numeric fields.
        """
        if v is None:
            return None
        from soup_cli.utils.classifier import validate_num_labels

        return validate_num_labels(v)

    @field_validator("reasoning_effort", mode="before")
    @classmethod
    def _validate_reasoning_effort(cls, v):
        """v0.52.0 Part G (security review fix) — canonicalise case +
        bool/null-byte/oversize rejection via the shared helper.

        Mirrors v0.51.0 ``_normalize_hub`` and v0.41.0 ``optimizer``
        policy of routing through the public ``validate_*`` helper at
        ``mode='before'`` so the schema and runtime helper agree on what's
        accepted.
        """
        if v is None:
            return None
        from soup_cli.utils.reasoning_effort import validate_reasoning_effort

        return validate_reasoning_effort(v)

    @field_validator("tts_emotion")
    @classmethod
    def _validate_tts_emotion_field(cls, v: Optional[str]) -> Optional[str]:
        """v0.52.0 Part A — bool / null-byte / oversize rejection (without
        family-specific allowlist; that fires in the cross-validator).
        """
        if v is None:
            return None
        if isinstance(v, bool):
            raise ValueError("tts_emotion must not be bool")
        if not isinstance(v, str):
            raise ValueError("tts_emotion must be str")
        if not v:
            raise ValueError("tts_emotion must be non-empty")
        if "\x00" in v:
            raise ValueError("tts_emotion must not contain null bytes")
        if len(v) > 32:
            raise ValueError("tts_emotion too long (max 32 chars)")
        return v

    @field_validator("hub", mode="before")
    @classmethod
    def _normalize_hub(cls, v):
        """v0.51.0 Part E review fix — accept any case (HF / Modelscope /
        MODELERS) and normalise to lowercase before the Literal check.
        Mirrors the v0.41.0 ``optimizer`` / v0.50.0 ``grpo_variant`` /
        ``rollout_backend`` policy of running the shared ``validate_*``
        helper at ``mode='before'`` so the public schema and the runtime
        validator agree on what's accepted.
        """
        # Lazy-import to avoid a hard dep cycle at module load.
        from soup_cli.utils.hubs import validate_hub_name
        if v is None:
            return v
        return validate_hub_name(v)
    # PPO-specific
    ppo_epochs: int = Field(
        default=4, ge=1, description="Number of PPO optimization epochs per batch"
    )
    ppo_clip_ratio: float = Field(
        default=0.2, gt=0, le=1.0, description="PPO clipping range for policy ratio"
    )
    ppo_kl_penalty: float = Field(
        default=0.05, ge=0, description="KL divergence penalty coefficient for PPO"
    )
    reward_model: Optional[str] = Field(
        default=None,
        description="Path or HF ID of a trained reward model for PPO",
    )

    @field_validator("reward_model")
    @classmethod
    def _validate_reward_model(cls, v: Optional[str]) -> Optional[str]:
        """v0.40.5 (#66 review fix) — reject null bytes and cap length on
        the reward_model string, matching the validation policy applied to
        cfg.base elsewhere. The Quant Menu loader (build_quantization_config_for_loader)
        already null-byte-rejects ref strings at training time; this is a
        defence-in-depth check at config-load so a crafted soup.yaml fails
        fast before any trainer is constructed.
        """
        if v is None:
            return v
        if "\x00" in v:
            raise ValueError("reward_model must not contain null bytes")
        if len(v) > 512:
            raise ValueError("reward_model must be <= 512 chars")
        return v
    # LoRA+ — different learning rates for A and B matrices
    loraplus_lr_ratio: Optional[float] = Field(
        default=None,
        gt=0,
        description="LoRA+ lr ratio: lr_B = lr × ratio. None = disabled (standard LoRA).",
    )
    # GaLore — memory-efficient full-parameter training
    use_galore: bool = Field(
        default=False,
        description="Enable GaLore (Gradient Low-Rank Projection) for memory-efficient training",
    )
    galore_rank: int = Field(
        default=128, ge=1, description="GaLore projection rank"
    )
    galore_update_proj_gap: int = Field(
        default=200, ge=1, description="GaLore projection update interval (steps)"
    )
    galore_scale: float = Field(
        default=0.25, gt=0, description="GaLore gradient scaling factor"
    )
    # MoE-specific
    moe_lora: bool = Field(
        default=False,
        description="Enable MoE-aware LoRA (ScatterMoE) — applies LoRA to expert FFN layers",
    )
    moe_aux_loss_coeff: float = Field(
        default=0.01,
        ge=0,
        description="Auxiliary load-balancing loss coefficient for MoE models",
    )
    # Performance — Liger Kernel (fused operations)
    use_liger: bool = Field(
        default=False,
        description="Enable Liger Kernel fused operations (20-60% memory savings, 20-40% speedup)",
    )
    # Performance — FlashAttention
    use_flash_attn: bool = Field(
        default=False,
        description="Enable FlashAttention (auto-detects v2/v3/v4 for faster attention)",
    )
    # Performance — Ring FlashAttention (sequence parallelism)
    use_ring_attention: bool = Field(
        default=False,
        description="Enable Ring FlashAttention for sequence parallelism across GPUs",
    )
    # Long-context — RoPE scaling
    rope_scaling_type: Optional[
        Literal["linear", "dynamic", "yarn", "longrope", "llama3"]
    ] = Field(
        default=None,
        description=(
            "RoPE scaling method for long-context: linear, dynamic, yarn, longrope, "
            "llama3 (v0.49.0)."
        ),
    )
    # v0.49.0 Part A — YaRN-specific tunables (only meaningful when
    # rope_scaling_type=='yarn'; cross-validator below enforces).
    yarn_factor: Optional[float] = Field(
        default=None,
        gt=1.0,
        le=1024.0,
        description=(
            "YaRN scaling factor (s). Optional — when omitted, the runtime falls "
            "back to ``target_length / original_length`` (HF default behaviour)."
        ),
    )
    yarn_attn_factor: Optional[float] = Field(
        default=None,
        ge=0.0,
        le=10.0,
        description="YaRN attention temperature multiplier (default 1.0 in HF).",
    )
    yarn_beta_fast: Optional[int] = Field(
        default=None,
        ge=1,
        le=1024,
        description="YaRN beta_fast cutoff (HF default 32).",
    )
    yarn_beta_slow: Optional[int] = Field(
        default=None,
        ge=1,
        le=1024,
        description="YaRN beta_slow cutoff (HF default 1).",
    )
    # v0.49.0 Part C — LongLoRA S² shifted-sparse attention.
    # Schema gate only; live forward override deferred to v0.49.1.
    use_longlora: bool = Field(
        default=False,
        description=(
            "Enable LongLoRA S² shifted-sparse attention (v0.49.0, schema-only). "
            "Requires task=sft, backend=transformers, Llama-family base. "
            "Mutually exclusive with use_ring_attention."
        ),
    )
    gradient_checkpointing: Union[
        bool, Literal["selective", "medium", "full", "auto"]
    ] = Field(
        default=False,
        description=(
            "Gradient checkpointing for memory savings on long sequences. "
            "False/True (legacy bool) or tier: 'selective' (attention only), "
            "'medium' (every other block), 'full' (all blocks), "
            "'auto' (picks based on available VRAM). (v0.28.0)."
        ),
    )
    # v0.28.0 — Cut Cross-Entropy (CCE): saves 8-24GB on large-vocab models
    use_cut_ce: bool = Field(
        default=False,
        description=(
            "Enable Cut Cross-Entropy (CCE) for large-vocab models. "
            "Saves 8-24GB VRAM on Llama 3.1 128k vocab. Requires cut_cross_entropy. "
            "Mutually exclusive with Unsloth/MLX backends."
        ),
    )
    # v0.28.0 — Kernel auto-composition (Liger + Unsloth + FlashAttn per-layer)
    kernel_auto_compose: bool = Field(
        default=False,
        description=(
            "Benchmark and auto-select the fastest kernel combination "
            "(Liger / FlashAttn / baseline) on the first few steps. (v0.28.0)."
        ),
    )
    # v0.28.0 — Cross-document attention masking for sample packing
    packing_cross_doc_attn_mask: bool = Field(
        default=False,
        description=(
            "Never worked: Soup mapped this to packing_strategy="
            "'attention_free', which is not in TRL's allowlist "
            "(bfd / bfd-requeue / wrapped) on any released trl. "
            "Use packing: true with FlashAttention instead. "
            "(v0.28.0 / #691 / #709)."
        ),
    )
    # v0.28.0 — Activation offloading (CPU/disk) for small-VRAM large-batch
    activation_offloading: Optional[Literal["cpu", "disk"]] = Field(
        default=None,
        description=(
            "Offload activations to CPU or disk during backward pass. "
            "None=off, 'cpu'=offload to RAM, 'disk'=offload to tmp file. (v0.28.0)."
        ),
    )
    # Embedding-specific
    embedding_loss: Literal["contrastive", "triplet", "cosine"] = Field(
        default="contrastive",
        description="Loss function for embedding training: contrastive, triplet, or cosine",
    )
    embedding_margin: float = Field(
        default=0.5, gt=0,
        description="Margin for contrastive/triplet loss (higher = stricter separation)",
    )
    embedding_pooling: Literal["mean", "cls", "last"] = Field(
        default="mean",
        description="Pooling strategy for sentence embeddings: mean, cls, or last token",
    )
    embedding_temperature: float = Field(
        default=0.05, gt=0,
        description="Temperature for contrastive (InfoNCE) loss — lower = stricter similarity",
    )
    # Curriculum learning — sort dataset by difficulty
    curriculum: bool = Field(
        default=False,
        description="Enable curriculum learning (sort dataset by difficulty, easy → hard)",
    )
    curriculum_metric: Literal["length", "perplexity", "loss"] = Field(
        default="length",
        description="Metric for curriculum difficulty: length, perplexity, or loss",
    )
    curriculum_buckets: int = Field(
        default=4, ge=1, le=20,
        description="Number of difficulty stages for curriculum learning",
    )
    # Curriculum-Aware dynamic re-weighting (v0.48.0 Part A — BETA)
    curriculum_dynamic: bool = Field(
        default=False,
        description=(
            "BETA: dynamically re-weight curriculum buckets every N steps via "
            "online uncertainty estimation (per-sample loss + grad norm). "
            "Requires curriculum=true. Multi-rank launches must wire an "
            "all_reduce hook on per-bucket stats (see "
            "utils.curriculum_dynamic.validate_distributed_curriculum)."
        ),
    )
    curriculum_dynamic_recompute_steps: int = Field(
        default=50, ge=1, le=100_000,
        description=(
            "Recompute curriculum bucket sampler weights every N global "
            "training steps."
        ),
    )
    curriculum_dynamic_floor: float = Field(
        default=0.05, gt=0.0, le=0.5,
        description=(
            "Minimum normalised per-bucket weight after softmax. "
            "Must be in (0.0, 1/curriculum_buckets]; the cross-validator "
            "tightens this to the per-config ceiling. Prevents bucket "
            "starvation."
        ),
    )
    curriculum_dynamic_temperature: float = Field(
        default=1.0, gt=0.0, le=100.0,
        description=(
            "Softmax temperature on the uncertainty signal. Higher = flatter "
            "distribution; lower = concentrate on hardest buckets."
        ),
    )
    # Loss watchdog — auto-stop on loss spikes
    loss_watchdog: bool = Field(
        default=False,
        description="Enable loss spike detection (auto-stop if loss exceeds threshold)",
    )
    loss_watchdog_threshold: float = Field(
        default=3.0,
        gt=0,
        le=100.0,
        description="Stop training if loss exceeds this threshold",
    )
    loss_watchdog_patience: int = Field(
        default=5,
        ge=1,
        le=1000,
        description="Consecutive high-loss steps before stopping",
    )
    # Loss spike auto-recovery (v0.32.0 Part E) — extends watchdog
    loss_spike_recovery: bool = Field(
        default=False,
        description=(
            "On watchdog trigger: rollback to last checkpoint, decay LR, "
            "and resume (instead of stopping). Requires loss_watchdog=true."
        ),
    )
    loss_spike_recovery_max_attempts: int = Field(
        default=3, ge=1, le=10,
        description="Max number of spike-recovery attempts before giving up",
    )
    loss_spike_recovery_lr_decay: float = Field(
        default=0.5, gt=0.0, lt=1.0,
        description="Multiply LR by this factor on each spike recovery (0.5 = halve)",
    )
    # ReLoRA (v0.39.0 Part B)
    relora_steps: Optional[int] = Field(
        default=None, ge=1, le=10**7,
        description=(
            "Fire ReLoRA magnitude-prune + optimizer reset every N global steps. "
            "None disables. Requires a LoRA-style PEFT (not VeRA)."
        ),
    )
    relora_warmup_ratio: float = Field(
        default=0.1, ge=0.0, le=1.0,
        description="Skip ReLoRA firings during the first warmup_ratio fraction of training",
    )
    relora_reset_optimizer: bool = Field(
        default=True,
        description="Clear optimizer state for pruned LoRA params on each ReLoRA fire",
    )
    relora_prune_ratio: float = Field(
        default=0.9, gt=0.0, lt=1.0,
        description=(
            "Fraction of LoRA weights to zero out by magnitude on each fire "
            "(0.9 keeps the top 10%). Must be < 1.0."
        ),
    )
    # Convergence detection (v0.32.0 Part F)
    convergence_detection: bool = Field(
        default=False,
        description=(
            "Watch for loss plateau / oscillation and surface advice "
            "(continue / early_stop / lower_lr) at the end of training."
        ),
    )
    convergence_window: int = Field(
        default=50, ge=5, le=10_000,
        description="Number of recent losses to inspect for plateau / oscillation",
    )
    convergence_rel_tol: float = Field(
        default=0.005, gt=0.0, le=1.0,
        description="Relative range threshold below which the window is a plateau",
    )
    # Warmup auto-schedule (v0.32.0 Part D) — reuses pre-existing warmup_ratio.
    warmup_auto: bool = Field(
        default=False,
        description=(
            "Auto-pick warmup_steps from dataset_size × epochs × warmup_ratio. "
            "Overrides any manual warmup_steps in the trainer."
        ),
    )
    # Auto mixed-precision (v0.32.0 Part C)
    auto_mixed_precision: bool = Field(
        default=False,
        description=(
            "Pick bf16/fp16 based on model + GPU compute capability. "
            "Overrides manual --bf16 / --fp16 trainer flags."
        ),
    )
    # Live grad-accum monitoring (v0.32.0 Part B)
    grad_accum_auto_tune: bool = Field(
        default=False,
        description=(
            "Monitor VRAM each step; warn (and recommend new batch/accum) "
            "when memory pressure is high. Advisory in v0.32.0; live "
            "DataLoader rebuild deferred to v0.32.1."
        ),
    )
    grad_accum_pressure_threshold: float = Field(
        default=0.92, gt=0.05, lt=0.99,
        description="VRAM utilisation fraction that triggers a recommendation",
    )
    # Freeze training — freeze bottom layers for parameter-efficient training
    freeze_layers: Optional[int] = Field(
        default=None,
        ge=1,
        le=1000,
        description="Freeze first N layers (from bottom). Train only remaining layers.",
    )
    freeze_ratio: Optional[float] = Field(
        default=None,
        gt=0.0,
        lt=1.0,
        description="Freeze this fraction of layers (0.75 = freeze 75% from bottom).",
    )
    # v0.71.23 #266 — Spectrum targeted training: full FT of selected params
    unfrozen_parameters: Optional[List[str]] = Field(
        default=None,
        description=(
            "Spectrum (#266) targeted training: regex patterns of parameter "
            "names to keep trainable — every other parameter is frozen. Full "
            "fine-tuning, LoRA off. Generate with `soup spectrum scan`. "
            "Mutually exclusive with LoRA features / freeze_layers / "
            "freeze_ratio / train_router_only; sft + transformers backend only."
        ),
    )

    @field_validator("unfrozen_parameters")
    @classmethod
    def _validate_unfrozen_parameters(
        cls, value: Optional[List[str]]
    ) -> Optional[List[str]]:
        """v0.71.23 #266 — caps + NUL + non-empty + regex-compilability."""
        if value is None:
            return None
        if len(value) > _MAX_UNFROZEN_PARAMETERS:
            raise ValueError(
                f"training.unfrozen_parameters: too many patterns "
                f"({len(value)} > {_MAX_UNFROZEN_PARAMETERS})"
            )
        for pat in value:
            # pydantic's List[str] already guarantees each entry is a str.
            if not pat:
                raise ValueError(
                    "training.unfrozen_parameters entries must be non-empty"
                )
            if "\x00" in pat:
                raise ValueError(
                    "training.unfrozen_parameters entries must not contain "
                    "null bytes"
                )
            if len(pat) > _MAX_UNFROZEN_PATTERN_LEN:
                raise ValueError(
                    f"training.unfrozen_parameters entries must be "
                    f"<= {_MAX_UNFROZEN_PATTERN_LEN} chars"
                )
            try:
                re.compile(pat)
            except re.error as exc:
                raise ValueError(
                    f"training.unfrozen_parameters: invalid regex {pat!r}: {exc}"
                ) from exc
            if _UNFROZEN_REDOS_RE.search(pat):
                raise ValueError(
                    f"training.unfrozen_parameters: pattern {pat!r} has nested "
                    f"unbounded quantifiers (ReDoS risk). Use a literal "
                    f"parameter-name prefix such as "
                    f"'model.layers.0.mlp.down_proj' (run `soup spectrum scan`)."
                )
        return value

    # v0.71.34 #267 — LISA (Layerwise Importance Sampled AdamW,
    # arXiv:2403.17919). Full-FT quality at LoRA-like memory: randomly
    # re-activate a small set of decoder layers every N steps (embeddings +
    # head always trainable). The dynamic cousin of Spectrum's static
    # unfrozen_parameters selection.
    lisa_enabled: bool = Field(
        default=False,
        description=(
            "Enable LISA layerwise importance sampling (#267): every "
            "lisa_interval_steps, freeze all decoder layers except a random "
            "lisa_num_layers set (embeddings + head always trainable). Full "
            "fine-tuning, LoRA off. sft or pretrain + transformers + text + "
            "quantization=none only; mutually exclusive with LoRA features / "
            "freeze_layers / freeze_ratio / unfrozen_parameters."
        ),
    )
    lisa_num_layers: int = Field(
        default=2, ge=1, le=64,
        description=(
            "LISA: number of decoder layers kept trainable per interval "
            "(clamped to the model's layer count). Small = LoRA-like memory."
        ),
    )
    lisa_interval_steps: int = Field(
        default=20, ge=1, le=1_000_000,
        description="LISA: re-sample the active decoder layers every N global steps.",
    )
    lisa_reset_optimizer: bool = Field(
        default=True,
        description=(
            "Clear optimizer state for decoder layers that LISA re-freezes on "
            "each interval (avoids stale Adam moments). Mirrors "
            "relora_reset_optimizer."
        ),
    )
    lisa_train_embeddings: bool = Field(
        default=True,
        description=(
            "Keep the always-on group (input embeddings + LM head + final "
            "norm) trainable every LISA interval. Default true is LISA as "
            "published (#267). Set false to freeze that group so only the "
            "sampled lisa_num_layers decoder layers train — the always-on set "
            "is the majority of LISA's memory, so this is the knob that buys "
            "the memory saving (#377). It is a real trade: the always-on set "
            "may be load-bearing for quality, so measure both ways."
        ),
    )

    @field_validator("lisa_num_layers", "lisa_interval_steps", mode="before")
    @classmethod
    def _validate_lisa_ints(cls, v: Any) -> Any:
        """v0.71.34 #267 — reject bool-as-int (bool subclasses int). Mirrors the
        v0.41.0 ``expand_layers`` / v0.50.0 GRPO numeric-field policy."""
        if isinstance(v, bool):
            raise ValueError("LISA integer fields must be int, not bool")
        return v

    # v0.72.0 BETA — Layer streaming. The frozen base lives in CPU RAM and is
    # streamed into a small pool of pre-allocated VRAM buffers one decoder
    # layer at a time, so peak VRAM is bounded by ONE layer instead of the
    # whole model. Only the LoRA adapters, their grads and optimizer state stay
    # resident. See soup_cli.utils.layer_stream.
    stream_layers: bool = Field(
        default=False,
        description=(
            "BETA — stream the frozen base layer-by-layer from CPU RAM so a "
            "model larger than VRAM can be fine-tuned. sft + transformers + "
            "text + quantization=none only; batch_size 1, no gradient "
            "accumulation. Slower than resident training, but these models did "
            "not run on the card at all."
        ),
    )
    stream_source: Literal["auto", "ram", "disk"] = Field(
        default="auto",
        description=(
            "Where the streamed base lives. 'ram' (the only tier implemented in "
            "v0.72.0) pins the base in CPU RAM; 'disk' is the v0.72.3 overflow "
            "tier; 'auto' picks RAM only when the store fits free-RAM headroom "
            "and the store plus resident extras fits the physical RAM ceiling."
        ),
    )
    stream_ngram_source: Literal["auto", "ram", "disk"] = Field(
        default="auto",
        description=(
            "Where Qwen4-Exp's frozen PLE N-gram embedding lives while layer "
            "streaming. 'disk' gathers only requested rows from the original "
            "safetensors through a read-only mmap; 'ram' keeps the table in "
            "CPU RAM; 'auto' uses RAM only when the table and selected base "
            "tier plus resident extras fit within free-RAM headroom and the "
            "physical RAM ceiling. oMLX/oQ affine PLE tables require 'disk' "
            "(or 'auto') so only selected rows are dequantized. A non-default "
            "value warns when the checkpoint has no external PLE table."
        ),
    )
    stream_buffers: int = Field(
        default=DEFAULT_STREAM_BUFFERS,
        ge=MIN_STREAM_BUFFERS,
        le=MAX_STREAM_BUFFERS,
        description=(
            "Pre-allocated VRAM layer buffers. 2 = double buffering, which is "
            "what lets the next layer load while the current one computes. A "
            "single buffer cannot overlap load with compute."
        ),
    )

    @field_validator("stream_buffers", mode="before")
    @classmethod
    def _validate_stream_buffers_int(cls, v: Any) -> Any:
        """v0.72.0 — reject bool-as-int (bool subclasses int)."""
        if isinstance(v, bool):
            raise ValueError("training.stream_buffers must be an int, not bool")
        return v

    stream_pin: Optional[bool] = Field(
        default=None,
        description=(
            "Force the page-locked (pinned) RAM store on or off. None (the "
            "default) lets the box decide: it attempts a pinned store and falls "
            "back to a pageable one — announcing the cost — when the host cannot "
            "page-lock it. 'false' forces the pageable store, the only known "
            "escape hatch when a pinning path is suspect (it was the sole "
            "mitigation while #331 was live). 'true' forces the pinned store and "
            "REFUSES the run on the RAM tier, naming the store size, if the box "
            "cannot page-lock it, rather than silently degrading — page-locking "
            "is worth up to 6.56x measured throughput, so a silent fallback "
            "spends the whole margin the feature exists to provide. On the disk "
            "tier (the base does not fit in RAM, so there is no RAM store to "
            "page-lock) and on non-CUDA targets (CPU or MPS) pinning is "
            "INAPPLICABLE rather than unsatisfiable: 'true' is announced and the "
            "run proceeds with a pageable CPU source, so the key stays committable "
            "to a config shared between CUDA and non-CUDA boxes."
        ),
    )

    stream_vram_override: Optional[int] = Field(
        default=None,
        ge=0,
        description=(
            "Bytes to assume free instead of measuring "
            "torch.cuda.mem_get_info(). mem_get_info() is a device-level "
            "driver query, so it cannot see a per-process cap "
            "(set_per_process_memory_fraction, a MIG slice, a card another "
            "process is also using), so on that hardware the pre-flight sees the "
            "whole device's free VRAM regardless of what this process can "
            "actually use. Set this to replace the measured figure in either "
            "direction: raise it to let a known-safe over-prediction through, "
            "or lower it to make the pre-flight enforce a cap the driver "
            "itself cannot report."
        ),
    )

    @field_validator("stream_vram_override", mode="before")
    @classmethod
    def _validate_stream_vram_override_int(cls, v: Any) -> Any:
        """Reject bool-as-int (bool subclasses int), mirrors stream_buffers."""
        if isinstance(v, bool):
            raise ValueError("training.stream_vram_override must be an int (bytes), not bool")
        return v

    stream_vram_probe: bool = Field(
        default=False,
        description=(
            "Decide the layer-streaming VRAM pre-flight on a MEASUREMENT "
            "instead of the fitted formula. One real forward+backward runs at "
            "the configured shape after the streamed model is built, and its "
            "peak decides whether the run proceeds; the prediction is printed "
            "beside it so a divergence is visible. The formula under-predicts "
            "past seq 4352 (measured 0.934x the real peak at seq 5120 and "
            "0.787x at 6144), which is the direction that does not announce "
            "itself — an OOM on Linux, a silent spill to host memory on "
            "Windows. Against the probe the same formula reads 0.992x at seq "
            "4096 and 0.830x at 5120, because the probe itself runs "
            "12.5-14.3% above the real training step — the direction that "
            "makes it safe as a gate. Off by default: it costs one step "
            "(1-5 s measured) and it can refuse a run the formula accepts. "
            "task='sft' only: the "
            "probe runs a plain causal-LM forward+backward, which IS the SFT "
            "step but not a preference loss. At the one preference shape "
            "measured it reads +13.5% high (probe 6.021 GB against a real "
            "DPO step's 5.304 GB) — the same safe direction it shows for SFT "
            "— but one shape is not a validation, so the restriction stands "
            "until it is measured across shapes."
        ),
    )
    stream_disk_kind: Optional[Literal["nvme", "ssd", "hdd"]] = Field(
        default=None,
        description=(
            "Override the auto-detected disk media type for the streaming disk "
            "overflow tier. Detection classifies a device by a measured "
            "sequential read when the kernel's rotational flag is unreliable — "
            "virtio and other paravirtual disks report spinning with no media "
            "hint, so a fast cloud disk is otherwise refused (#365). Set this "
            "for the case where even that is wrong: 'nvme' forces the disk tier "
            "on, 'ssd'/'hdd' force it off. The resolved value is printed beside "
            "what was detected."
        ),
    )

    # Sample packing — pack multiple short samples into one sequence
    packing: bool = Field(
        default=False,
        description="Pack multiple short samples into one sequence for faster training",
    )
    # v0.37.0 — Multipack First-Fit-Decreasing bin-packing sampler
    multipack: bool = Field(
        default=False,
        description=(
            "Use FFD bin-packing sampler to maximise tokens-per-batch on "
            "uneven-length data. Mutually exclusive with packing. Only "
            "supported for sft / pretrain tasks (transformers backend). "
            "(v0.37.0)."
        ),
    )
    # NEFTune — noisy embeddings for better fine-tuning
    neftune_alpha: Optional[float] = Field(
        default=None,
        ge=0.0,
        le=50.0,
        description="NEFTune noise alpha (0-50). Adds noise to embeddings for better chat quality.",
    )
    # Training Intelligence — Part G of v0.25.0
    # Forgetting detection
    forgetting_detection: bool = Field(
        default=False,
        description="Enable periodic general-knowledge eval to detect catastrophic forgetting",
    )
    forgetting_eval_steps: int = Field(
        default=100, ge=10, le=10000,
        description="Run forgetting eval every N steps",
    )
    forgetting_threshold: float = Field(
        default=0.10, ge=0.01, le=0.50,
        description="Warn if accuracy drops > threshold from baseline (0.01-0.50)",
    )
    forgetting_benchmark: Literal["mini_mmlu", "mini_common_sense", "mini_instruction"] = Field(
        default="mini_mmlu",
        description="Built-in mini benchmark used for forgetting detection",
    )
    forgetting_stop: bool = Field(
        default=False,
        description="Auto-stop training on severe forgetting (red-level alert)",
    )
    # Checkpoint intelligence
    checkpoint_intelligence: bool = Field(
        default=False,
        description="Enable auto-best-checkpoint tracking by quality (not just loss)",
    )
    checkpoint_eval_steps: int = Field(
        default=200, ge=50, le=10000,
        description="Run checkpoint quality eval every N steps",
    )
    checkpoint_eval_metric: Literal["judge", "mmlu", "custom", "composite"] = Field(
        default="composite",
        description="Metric used for checkpoint quality selection",
    )
    checkpoint_eval_tasks: Optional[str] = Field(
        default=None,
        description="Optional JSONL file with custom eval tasks for checkpoint scoring",
    )
    checkpoint_keep_top: int = Field(
        default=3, ge=1, le=20,
        description="Keep top-N checkpoints by quality, delete the rest",
    )
    early_stop_on_regression: bool = Field(
        default=False,
        description="Stop training when quality regresses across consecutive evals",
    )
    early_stop_patience: int = Field(
        default=2, ge=1, le=10,
        description="Consecutive regressions before early stopping (1-10)",
    )
    # Eval-Gated Training — Part B of v0.26.0
    eval_gate: Optional["EvalGateConfig"] = Field(
        default=None,
        description="Optional EvalGateConfig — block training on regressions",
    )
    # Multi-GPU Mastery — v0.27.0
    use_fsdp2_compile: bool = Field(
        default=False,
        description=(
            "Enable torch.compile on top of FSDP2 for +20-30% training speed. "
            "Requires --fsdp, CUDA, and backend=transformers."
        ),
    )
    parallelism: Literal["data", "pipeline"] = Field(
        default="data",
        description=(
            "Distributed strategy: 'data' (DDP/FSDP/DeepSpeed) or 'pipeline' "
            "(pipeline parallel, v0.27.0 wiring only)."
        ),
    )
    pipeline_stages: int = Field(
        default=1, ge=1, le=16,
        description=(
            "Number of pipeline parallel stages. Ignored when "
            "parallelism='data'."
        ),
    )

    @model_validator(mode="after")
    def _validate_verifiable_reward(self) -> "TrainingConfig":
        """RLVR: reward_fn='verifiable' requires verifiable_domain.

        Comma-aware (v0.71.40 #311): ``"accuracy,verifiable"`` must fail at
        config-parse time exactly like the bare ``"verifiable"`` form, not only
        at trainer construction.
        """
        segments = (
            [s.strip() for s in self.reward_fn.split(",")]
            if isinstance(self.reward_fn, str)
            else []
        )
        if "verifiable" in segments and self.verifiable_domain is None:
            raise ValueError(
                "reward_fn='verifiable' requires verifiable_domain "
                "(one of: math, code, json_schema)"
            )
        return self

    @model_validator(mode="after")
    def _validate_grpo_stability_pairings(self) -> "TrainingConfig":
        """v0.50.0 Part D — surfaces probable footguns in stability knobs.

        ``mask_truncated_completions=True`` without ``tis_threshold`` is a
        no-op (the mask is built from the threshold). Reject loudly rather
        than silently no-op (mirrors v0.32.0 spike-recovery + watchdog
        cross-validator policy).
        """
        if self.mask_truncated_completions and self.tis_threshold is None:
            raise ValueError(
                "mask_truncated_completions requires tis_threshold to be set "
                "(the truncation mask is derived from the importance-sampling "
                "threshold)"
            )
        return self

    @field_validator(
        "ref_model_ema_alpha",
        "tis_threshold",
        "off_policy_mask_threshold",
        "replay_buffer_size",
        "grpo_delta",
        mode="before",
    )
    @classmethod
    def _reject_bool_on_grpo_numerics(cls, v: object, info: object) -> object:
        """v0.50.0 (tdd-guide HIGH fix) — explicit bool rejection on every
        numeric stability/RL knob. Matches v0.30.0 ``Candidate`` /
        v0.41.0 Part B ``lr_groups`` / v0.43.0 Part B ``Tournament`` policy.

        Pydantic v2 coerces ``True`` → ``1`` and ``False`` → ``0`` on int /
        float fields by default; that would silently accept a misconfigured
        YAML where a user typed ``true`` instead of a numeric literal.
        """
        if isinstance(v, bool):
            raise ValueError(
                f"{getattr(info, 'field_name', 'field')} must not be bool"
            )
        return v

    @field_validator("grpo_delta", mode="after")
    @classmethod
    def _validate_grpo_delta_finite(cls, v: Optional[float]) -> Optional[float]:
        """v0.50.0 Part A (security review fix) — explicit NaN/Inf rejection.

        Pydantic's ``gt=0.0, le=1.0`` incidentally rejects NaN (since
        ``NaN > 0.0`` is False), but the rejection is implicit. Make it
        explicit so a future Pydantic change cannot regress the guard.
        Mirrors v0.32.0 ``save_lr_finder_report`` / v0.47.0 Part A
        ``build_forge_plan`` policy.
        """
        if v is None:
            return v
        import math as _math

        if not _math.isfinite(v):
            raise ValueError("grpo_delta must be finite (no NaN/Inf)")
        return v

    @model_validator(mode="after")
    def _validate_grpo_variant_delta(self) -> "TrainingConfig":
        """v0.50.0 Part A / #744 — grpo_variant='two_sided' requires grpo_delta.

        grpo_variant='gspo' optionally accepts grpo_delta as sequence clipping radius.
        Setting grpo_delta on any other variant (or with no variant) is rejected.
        """
        if self.grpo_variant == "two_sided" and self.grpo_delta is None:
            raise ValueError(
                "grpo_variant='two_sided' requires grpo_delta "
                "(symmetric clipping radius, (0, 1])"
            )
        if self.grpo_delta is not None and self.grpo_variant not in ("two_sided", "gspo"):
            raise ValueError(
                "grpo_delta is only valid when grpo_variant is 'two_sided' or 'gspo'; "
                f"got grpo_variant={self.grpo_variant!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_cross_doc_attn_mask(self) -> "TrainingConfig":
        """packing_cross_doc_attn_mask never mapped to a valid TRL strategy."""
        if self.packing_cross_doc_attn_mask:
            raise ValueError(
                "packing_cross_doc_attn_mask is not supported: it never mapped "
                "to a valid TRL packing_strategy (allowlist is 'bfd', "
                "'bfd-requeue', or 'wrapped'). Use packing: true with a "
                "FlashAttention attn_implementation; TRL's default bfd "
                "strategy already isolates packed documents when FA is present"
            )
        return self

    @model_validator(mode="after")
    def _validate_multipack_packing_exclusive(self) -> "TrainingConfig":
        """Multipack and packing are mutually exclusive — pick one (v0.37.0).

        Both rewrite the batch composition; running them together produces
        ill-defined sample boundaries. Plan: long term, multipack subsumes
        packing — but for v0.37.0 we keep them as separate opt-ins.
        """
        if self.multipack and self.packing:
            raise ValueError(
                "multipack and packing are mutually exclusive — "
                "pick one (multipack uses FFD bin-packing; packing "
                "uses TRL's basic packer). For most uses, multipack=true "
                "is the better choice."
            )
        return self

    @model_validator(mode="after")
    def _validate_curriculum_dynamic_requires_curriculum(self) -> "TrainingConfig":
        """v0.48.0 Part A — dynamic re-weighting layers on the static
        curriculum bucketer; it cannot run alone."""
        if self.curriculum_dynamic and not self.curriculum:
            raise ValueError(
                "curriculum_dynamic requires curriculum=true "
                "(dynamic re-weighting needs the static bucketer)."
            )
        # Cross-check: floor must leave room above uniform/N.
        if self.curriculum_dynamic:
            ceiling = 1.0 / max(self.curriculum_buckets, 1)
            if self.curriculum_dynamic_floor > ceiling:
                raise ValueError(
                    f"curriculum_dynamic_floor={self.curriculum_dynamic_floor} "
                    f"must be <= 1/curriculum_buckets ({ceiling:.4f})."
                )
        return self

    @field_validator(
        "yarn_factor",
        "yarn_attn_factor",
        "yarn_beta_fast",
        "yarn_beta_slow",
        mode="before",
    )
    @classmethod
    def _reject_bool_yarn(cls, value: Any) -> Any:
        """v0.49.0 Part A — bool is a subclass of int/float in Python; Pydantic
        would silently accept ``True``. Reject explicitly (project bool-as-int
        policy, mirrors v0.30.0 Candidate / v0.34.0 estimate_run_cost_usd)."""
        if isinstance(value, bool):
            raise ValueError(
                "bool is not a valid value for a YaRN tunable (use a real number)"
            )
        return value

    @model_validator(mode="after")
    def _validate_yarn_fields_require_yarn_type(self) -> "TrainingConfig":
        """v0.49.0 Part A — yarn_* fields are no-ops unless
        ``rope_scaling_type='yarn'``. Surface the misconfig loudly at config
        load rather than silently dropping the values.
        """
        yarn_fields = {
            "yarn_factor": self.yarn_factor,
            "yarn_attn_factor": self.yarn_attn_factor,
            "yarn_beta_fast": self.yarn_beta_fast,
            "yarn_beta_slow": self.yarn_beta_slow,
        }
        set_fields = [name for name, value in yarn_fields.items() if value is not None]
        if set_fields and self.rope_scaling_type != "yarn":
            raise ValueError(
                f"{', '.join(set_fields)} only apply when rope_scaling_type='yarn' "
                f"(got rope_scaling_type={self.rope_scaling_type!r})."
            )
        return self

    @model_validator(mode="after")
    def _validate_longlora_ring_attn_exclusive(self) -> "TrainingConfig":
        """v0.49.0 Part C — LongLoRA's S² shifted-sparse attention is a custom
        forward override that conflicts with ring/FA-v3 custom-mask attention
        paths."""
        if self.use_longlora and self.use_ring_attention:
            raise ValueError(
                "use_longlora is incompatible with use_ring_attention "
                "(both rewrite the attention kernel — pick one)."
            )
        return self

    @model_validator(mode="after")
    def _validate_spike_recovery_requires_watchdog(self) -> "TrainingConfig":
        """Spike recovery is a watchdog hook — it needs the watchdog enabled."""
        if self.loss_spike_recovery and not self.loss_watchdog:
            raise ValueError(
                "loss_spike_recovery requires loss_watchdog=true "
                "(spike recovery is triggered by the watchdog)"
            )
        return self

    @model_validator(mode="after")
    def _validate_prequantized_no_qat(self) -> "TrainingConfig":
        """v0.38.0 — Pre-quantized formats + QAT is incompatible.

        GPTQ / AWQ / HQQ / AQLM / EETQ / MXFP4 / FP8 checkpoints all carry
        their own scale; routing them through torchao QAT or float8 prepare
        would corrupt the dequantized weights. Mirrors LlamaFactory's similar
        guard at quantization.py:117 / :199 / :211.
        """
        from soup_cli.utils.quant_menu import is_quant_menu_format

        if is_quant_menu_format(self.quantization) and self.quantization_aware:
            raise ValueError(
                f"quantization={self.quantization!r} is incompatible with "
                f"quantization_aware ({self.quantization_aware!r}). "
                "Pre-quantized checkpoints carry their own scale; "
                "QAT/FP8 prepare cannot compose. Set quantization_aware: false."
            )
        return self

    @model_validator(mode="after")
    def _validate_bnb_quant_storage_only_with_4bit(self) -> "TrainingConfig":
        """v0.38.0 Part G — bnb_4bit_quant_storage applies only to BNB 4-bit
        and MXFP4 (which is a BNB 4-bit variant). Setting it on any other
        format is a silent no-op — fail fast.
        """
        if self.bnb_4bit_quant_storage is None:
            return self
        if self.quantization not in ("4bit", "mxfp4"):
            raise ValueError(
                f"bnb_4bit_quant_storage={self.bnb_4bit_quant_storage!r} "
                f"requires quantization in {{'4bit', 'mxfp4'}}, got "
                f"{self.quantization!r}."
            )
        return self

    @model_validator(mode="after")
    def _validate_fp8_recipe_requires_fp8(self) -> "TrainingConfig":
        """fp8_recipe is only meaningful when quantization_aware='fp8'."""
        if self.fp8_recipe != "tensorwise" and self.quantization_aware != "fp8":
            raise ValueError(
                f"fp8_recipe='{self.fp8_recipe}' requires quantization_aware='fp8'. "
                "Either set quantization_aware: 'fp8' or remove the fp8_recipe field."
            )
        return self

    @field_validator("optimizer")
    @classmethod
    def _validate_optimizer(cls, value: str) -> str:
        """v0.41.0 Part A — optimizer allowlist."""
        from soup_cli.utils.optimizer_zoo import validate_optimizer_name

        return validate_optimizer_name(value)

    @field_validator("lr_groups", mode="before")
    @classmethod
    def _validate_lr_groups(cls, value):
        """v0.41.0 Part B — parse + validate lr_groups."""
        if value is None:
            return None
        from soup_cli.utils.lr_groups import parse_lr_groups

        parsed = parse_lr_groups(value)
        if parsed is None:
            return None
        # Re-emit as the raw schema shape (list of {pattern, lr} dicts) so
        # round-tripping through model_dump preserves user-visible structure.
        return [{"pattern": g.pattern, "lr": g.lr} for g in parsed]

    @field_validator("freeze_trainable_layers", mode="before")
    @classmethod
    def _validate_freeze_trainable_layers(cls, value):
        """v0.41.0 Part C — magnitude capped at 1000."""
        if value is None:
            return None
        from soup_cli.utils.block_expansion import (
            validate_freeze_trainable_layers,
        )

        return validate_freeze_trainable_layers(value)

    @field_validator("expand_layers", mode="before")
    @classmethod
    def _validate_expand_layers_field(cls, value):
        """v0.41.0 Part C — block expansion bounds + bool rejection.

        Pydantic's `Field(ge=1, le=64)` accepts ``True`` (subclass of int);
        the explicit validator rejects bool and routes through the shared
        helper so the int bounds stay single-source-of-truth.
        """
        if value is None:
            return None
        from soup_cli.utils.block_expansion import validate_expand_layers

        return validate_expand_layers(value)

    @model_validator(mode="after")
    def _validate_load_in_aliases(self) -> "TrainingConfig":
        """v0.41.0 Part C — load_in_8bit / load_in_16bit aliases.

        Mutually exclusive. When set to True, they override ``quantization``
        only if the user did not explicitly pick a Quant Menu format
        (gptq / awq / hqq:* / aqlm / eetq / mxfp4 / fp8). Mixing alias=True
        with Quant Menu raises rather than silently overriding the explicit
        pick. Uses ``is True`` (project policy) so an explicit ``False``
        from the user is treated as "no preference", never silently
        rewriting the field.
        """
        l8 = self.load_in_8bit
        l16 = self.load_in_16bit
        if l8 is True and l16 is True:
            raise ValueError(
                "load_in_8bit and load_in_16bit are mutually exclusive — "
                "pick one."
            )
        if l8 is not True and l16 is not True:
            return self
        # Defer the import: utils.quant_menu is loaded lazily elsewhere.
        from soup_cli.utils.quant_menu import is_quant_menu_format

        if is_quant_menu_format(self.quantization):
            raise ValueError(
                f"load_in_8bit / load_in_16bit cannot be combined with "
                f"quantization={self.quantization!r} (Quant Menu format). "
                "Either remove the alias or set quantization to '4bit', "
                "'8bit', or 'none'."
            )
        # Direct assignment routes through Pydantic v2 BaseModel.__setattr__
        # so any future field_validator on ``quantization`` still fires.
        # ``object.__setattr__`` would silently bypass that path.
        if l8 is True and self.quantization != "8bit":
            self.quantization = "8bit"
        elif l16 is True and self.quantization != "none":
            self.quantization = "none"
        return self

    @model_validator(mode="after")
    def _validate_block_expansion_pair(self) -> "TrainingConfig":
        """v0.41.0 Part C — expand_layers + freeze_trainable_layers pair."""
        if self.expand_layers is not None and self.freeze_trainable_layers is None:
            raise ValueError(
                "expand_layers requires freeze_trainable_layers (LLaMA Pro "
                "freezes the original layers and trains only the new blocks). "
                "Set freeze_trainable_layers: <signed int>."
            )
        return self

    # ---- v0.61.0 Part A — Unlearning ---------------------------------------
    # Schema-only release: validators here are reused by the SoupConfig
    # cross-validator + UnlearnTrainerWrapper. Live trainer in v0.61.1.
    unlearn_method: Optional[Literal["npo", "simnpo", "rmu"]] = Field(
        default=None,
        description=(
            "Unlearning method backend — required when task='unlearn'. "
            "npo (Negative Preference Optimization, DPO-shaped negative-only "
            "loss); simnpo (length-normalised NPO without ref model); rmu "
            "(Representation Misdirection Unlearning, residual-stream noise). "
            "Schema-only in v0.61.0; live trainer deferred to v0.61.1."
        ),
    )
    unlearn_alpha: Optional[float] = Field(
        default=None,
        description=(
            "Retain-set weighting in the unlearn loss (forget vs retain "
            "mixing coefficient). 0.0 = pure forget loss; higher values "
            "increasingly favour the retain set. Bounded [0.0, 10.0]. "
            "(v0.61.0)"
        ),
    )

    @field_validator("unlearn_method", mode="before")
    @classmethod
    def _validate_unlearn_method(cls, v):
        """v0.61.0 Part A — bool / null-byte / oversize / case-insensitive
        normalisation via the shared helper.

        Mirrors v0.51.0 ``_normalize_hub`` / v0.52.0 ``_validate_reasoning_effort``
        policy of routing through the public ``validate_*`` helper at
        ``mode='before'`` so the schema and runtime helper agree on what's
        accepted.
        """
        if v is None:
            return None
        from soup_cli.utils.unlearning import validate_unlearn_method

        return validate_unlearn_method(v)

    @field_validator("unlearn_alpha", mode="before")
    @classmethod
    def _validate_unlearn_alpha(cls, v):
        """v0.61.0 Part A — bool/NaN/Inf-rejected float bounded [0.0, 10.0]."""
        if v is None:
            return None
        from soup_cli.utils.unlearning import validate_unlearn_alpha

        return validate_unlearn_alpha(v)

    # ---- v0.62.0 Part B — RA-DIT (Retrieval-Augmented Dual Instruction
    # Tuning, Meta 2023). Schema-only: a YAML can declare ``ra_dit_stage``
    # so a recipe locks the right pairing; live two-stage orchestration
    # ships in v0.62.1 (mirrors the v0.50.0 / v0.61.0 stub-then-live
    # pattern).
    ra_dit_stage: Optional[Literal["retriever", "generator"]] = Field(
        default=None,
        description=(
            "RA-DIT pipeline stage. 'retriever' trains the sentence-"
            "transformer via the v0.16 embedding trainer; 'generator' "
            "runs RAFT-style SFT on `data.format='raft'`. Composes with "
            "the v0.62.0 Part A RAFT recipe. (v0.62.0 Part B)"
        ),
    )
    ra_dit_retriever_model: Optional[str] = Field(
        default=None,
        description=(
            "Optional retriever model id (e.g. "
            "`sentence-transformers/all-mpnet-base-v2`) used by the "
            "generator stage to pre-encode distractor docs. (v0.62.0 "
            "Part B)"
        ),
    )

    @field_validator("ra_dit_stage", mode="before")
    @classmethod
    def _validate_ra_dit_stage(cls, v):
        """v0.62.0 Part B — case-insensitive normalisation via shared helper."""
        if v is None:
            return None
        from soup_cli.utils.ra_dit import validate_ra_dit_stage

        return validate_ra_dit_stage(v)

    @field_validator("ra_dit_retriever_model", mode="before")
    @classmethod
    def _validate_ra_dit_retriever_model(cls, v):
        """v0.62.0 Part B — bool/null-byte/oversize rejection on retriever id."""
        if v is None:
            return None
        from soup_cli.utils.ra_dit import validate_ra_dit_retriever_model

        return validate_ra_dit_retriever_model(v)

    # ---- v0.62.0 Part D — Citation-faithful FT ----------------------------
    citation_faithful: bool = Field(
        default=False,
        description=(
            "Opt INTO citation-precision / recall scoring + a loss-mask "
            "rule that emphasises citation spans. Requires "
            "`data.format='raft'`. Schema-only in v0.62.0; live span-mask "
            "ships in v0.62.1. (v0.62.0 Part D)"
        ),
    )
    citation_style: Optional[Literal["bracket", "inline", "footnote"]] = Field(
        default=None,
        description=(
            "Citation rendering style. 'bracket' = `[doc-1]` inline tag "
            "(canonical RAFT default); 'inline' / 'footnote' are stub "
            "placeholders for v0.62.1. (v0.62.0 Part D)"
        ),
    )
    citation_recall_threshold: Optional[float] = Field(
        default=None,
        description=(
            "Reject final-save when measured citation recall < this "
            "threshold. Bounded [0.0, 1.0]. Composes with v0.56.0 "
            "diagnose-gate. (v0.62.0 Part D)"
        ),
    )

    @field_validator("citation_style", mode="before")
    @classmethod
    def _validate_citation_style(cls, v):
        """v0.62.0 Part D — case-insensitive normalisation via shared helper."""
        if v is None:
            return None
        from soup_cli.utils.citation_faithful import validate_citation_style

        return validate_citation_style(v)

    @field_validator("citation_recall_threshold", mode="before")
    @classmethod
    def _validate_citation_recall_threshold(cls, v):
        """v0.62.0 Part D — bool/NaN/Inf-rejected float bounded [0.0, 1.0]."""
        if v is None:
            return None
        from soup_cli.utils.citation_faithful import validate_citation_threshold

        return validate_citation_threshold(v)

    # ---- v0.62.0 Part E — GRACE codebook ----------------------------------
    grace_codebook: bool = Field(
        default=False,
        description=(
            "Opt INTO the GRACE codebook — discrete latent-space (key, "
            "value) store for thousands of sequential knowledge edits "
            "without norm-blowup. Schema-only in v0.62.0; live lookup / "
            "write ships in v0.62.1. (v0.62.0 Part E)"
        ),
    )
    grace_codebook_size: Optional[int] = Field(
        default=None,
        description=(
            "Codebook entry count. Required when grace_codebook=True. "
            "Bounded [1, 100_000]. (v0.62.0 Part E)"
        ),
    )
    grace_codebook_dim: Optional[int] = Field(
        default=None,
        description=(
            "Codebook entry dim (residual-stream width). Required when "
            "grace_codebook=True. Bounded [1, 16_384]. (v0.62.0 Part E)"
        ),
    )

    @field_validator("grace_codebook_size", mode="before")
    @classmethod
    def _validate_grace_codebook_size(cls, v):
        """v0.62.0 Part E — bool-rejected positive int <= MAX_CODEBOOK_SIZE."""
        if v is None:
            return None
        from soup_cli.utils.grace_codebook import validate_grace_codebook_size

        return validate_grace_codebook_size(v)

    @field_validator("grace_codebook_dim", mode="before")
    @classmethod
    def _validate_grace_codebook_dim(cls, v):
        """v0.62.0 Part E — bool-rejected positive int <= MAX_CODEBOOK_DIM."""
        if v is None:
            return None
        from soup_cli.utils.grace_codebook import validate_grace_codebook_dim

        return validate_grace_codebook_dim(v)


class ShipConfig(BaseModel):
    """`soup ship` verdict config (v0.71.39) — mirrors ``EvalGateConfig``.

    Committing the gate config to ``soup.yaml`` (under ``eval.ship``) makes the
    SHIP / DON'T-SHIP decision reviewable in a PR diff and reproducible across
    runs. ``soup ship --config soup.yaml`` reads these as defaults; an explicit
    CLI flag always wins (CLI > config > hard default).
    """

    task_eval: Optional[str] = Field(
        default=None,
        description="JSONL of leg-1 task-win eval tasks",
    )
    # Keep these values in sync with soup_cli.utils.ship_verdict.TASK_MODES.
    task_mode: Literal["metric", "judge_score", "pairwise"] = Field(
        default="metric",
        description="Leg-1 mode: metric | judge_score | pairwise (judge win-rate)",
    )
    general_suite: Optional[str] = Field(
        default=None,
        description="Comma list of leg-2 benchmarks (default: bundled offline suite)",
    )
    forgetting_threshold: float = Field(
        default=0.05, ge=0.0, le=1.0,
        description="Max allowed leg-2 drop in absolute points before DON'T SHIP",
    )
    judge_model: Optional[str] = Field(
        default=None,
        description="Judge model URL for task_mode judge_score / pairwise",
    )
    baseline: Optional[str] = Field(
        default=None,
        description=(
            "registry://<id> | stamped baseline JSON "
            "({scores, provenance.scorer_revision}) of base leg-2 scores"
        ),
    )
    # v0.73.2 shipped `--noise-floor` (#376) without its config surface, so it
    # was the one gate-policy flag that could not be committed to soup.yaml
    # (#406). Bounds import from ship_verdict so the schema and the CLI
    # validator (_validate_noise_floor_flag) share one source of truth.
    noise_floor: Optional[int] = Field(
        default=None,
        ge=MIN_NOISE_FLOOR_RUNS,
        le=MAX_NOISE_FLOOR_RUNS,
        description=(
            "Repeats of the BASE run used to measure a leg-2 noise floor; a "
            "leg-2 drop within the floor is treated as noise, not regression. "
            f"Bounded [{MIN_NOISE_FLOOR_RUNS}, {MAX_NOISE_FLOOR_RUNS}]. A live "
            "input: measured when producing evidence, refused under --evidence."
        ),
    )

    @field_validator("noise_floor", mode="before")
    @classmethod
    def _validate_noise_floor_int(cls, v: Any) -> Any:
        """Reject bool-as-int (bool subclasses int), mirrors stream_buffers and
        the ``--noise-floor`` CLI validator."""
        if isinstance(v, bool):
            raise ValueError("eval.ship.noise_floor must be an int, not bool")
        return v


class EvalConfig(BaseModel):
    """Evaluation configuration for auto-eval after training."""

    auto_eval: bool = Field(
        default=False,
        description="Run evaluation automatically after training completes",
    )
    benchmarks: Optional[List[str]] = Field(
        default=None,
        description="lm-evaluation-harness benchmark names to run",
    )
    custom_tasks: Optional[str] = Field(
        default=None,
        description="Path to custom eval JSONL file",
    )
    judge: Optional[dict] = Field(
        default=None,
        description="LLM-as-a-judge config: model, rubric, provider",
    )
    ship: Optional[ShipConfig] = Field(
        default=None,
        description="soup ship verdict defaults (task_eval / task_mode / "
        "general_suite / forgetting_threshold / judge_model / baseline / "
        "noise_floor)",
    )


# --- v0.71.26 reward-hack mitigation validation helpers ---

# Control tunables that are meaningless unless a mitigation mode is set. Setting
# any to a non-default value while reward_hack_mitigation='off' is a silent
# no-op footgun (mirrors the v0.70.0 minillm offenders-list policy). Extended
# per stage (Stage 2/3 tunables added with their fields).
# Stage-2 (PID-Lagrangian + rollback) tunables — meaningful only in
# pid_lagrangian mode. Setting one under any other mode is a no-op footgun.
_REWARD_HACK_STAGE2_DEFAULTS: dict[str, Any] = {
    "reward_hack_pid_kp": 0.5,
    "reward_hack_pid_ki": 0.1,
    "reward_hack_pid_kd": 0.05,
    "reward_hack_signal_target": 0.15,
    "reward_hack_integral_clamp": 1.0,
    "reward_hack_rollback": False,
    "reward_hack_rollback_patience": 3,
    "reward_hack_max_recovery_attempts": 2,
}

# Stage-3 (anti-gaming) tunables — meaningful for any non-off mode.
_REWARD_HACK_STAGE3_DEFAULTS: dict[str, Any] = {
    "reward_hack_signal_smoothing": "none",
    "reward_hack_smoothing_window": 8,
    "reward_hack_conservative_on_disagreement": False,
    "reward_hack_reward_shaping": False,
    "reward_hack_shaping_kind": "length",
    "reward_hack_shaping_strength": 0.0,
}

_REWARD_HACK_TUNABLE_DEFAULTS: dict[str, Any] = {
    "reward_hack_beta_floor": 0.02,
    "reward_hack_beta_ceil": 1.0,
    "reward_hack_trip_band": 0.30,
    "reward_hack_release_band": 0.10,
    "reward_hack_dwell_steps": 2,
    "reward_hack_release_patience": 3,
    "reward_hack_kl_gain": 1.5,
    # tuple (not list) so a caller cannot mutate this module-level default.
    "reward_hack_signals": ("info_rm",),
    **_REWARD_HACK_STAGE2_DEFAULTS,
    **_REWARD_HACK_STAGE3_DEFAULTS,
}


def _customized_reward_hack_tunables(tcfg: Any) -> list[str]:
    """Return the reward-hack control tunables set to a non-default value."""
    offenders: list[str] = []
    for field_name, default in _REWARD_HACK_TUNABLE_DEFAULTS.items():
        current = getattr(tcfg, field_name, default)
        # Normalise list/tuple so a list value compares equal to a tuple default.
        if isinstance(default, tuple) and isinstance(current, (list, tuple)):
            if tuple(current) != default:
                offenders.append(field_name)
        elif current != default:
            offenders.append(field_name)
    return offenders


def _validate_reward_hack_controller(tcfg: Any) -> None:
    """Validate the mitigation-controller config (only when a mode is active).

    Numeric consistency (β floor < ceil, release < trip band), the signal
    allowlist, and the β-schedule mutual exclusion.
    """
    floor = tcfg.reward_hack_beta_floor
    ceil = tcfg.reward_hack_beta_ceil
    if floor >= ceil:
        raise ValueError(
            f"reward_hack_beta_floor ({floor}) must be < "
            f"reward_hack_beta_ceil ({ceil})"
        )
    release = tcfg.reward_hack_release_band
    trip = tcfg.reward_hack_trip_band
    if release >= trip:
        raise ValueError(
            f"reward_hack_release_band ({release}) must be < "
            f"reward_hack_trip_band ({trip})"
        )
    from soup_cli.utils.reward_hack_control import SIGNAL_NAMES

    # The controller votes on the ACTIVE detector's signal plus the auxiliary
    # signals. Listing the other detector's name (never produced) or omitting
    # the active detector silently drops the primary signal from the vote —
    # reject both so the config is coherent (python-review CRITICAL #1).
    signals = list(tcfg.reward_hack_signals or [])
    allowed = {tcfg.reward_hack_detector, "length_trend", "repetition"}
    for name in signals:
        if name not in SIGNAL_NAMES:
            raise ValueError(
                f"reward_hack_signals contains unknown signal {name!r}; "
                f"valid: {sorted(SIGNAL_NAMES)}"
            )
        if name not in allowed:
            raise ValueError(
                f"reward_hack_signals contains {name!r}, but the active "
                f"detector is {tcfg.reward_hack_detector!r}; valid signals "
                f"are {sorted(allowed)}"
            )
    if tcfg.reward_hack_detector is not None and tcfg.reward_hack_detector not in signals:
        raise ValueError(
            "reward_hack_signals must include the active detector "
            f"{tcfg.reward_hack_detector!r} (its signal is the primary vote)"
        )
    # A control mode drives the KL/ref dynamics; a competing β schedule
    # (ref_model_ema_alpha regenerates the reference) fights it — reject.
    if tcfg.reward_hack_mitigation in ("kl_control", "pid_lagrangian"):
        if getattr(tcfg, "ref_model_ema_alpha", None) is not None:
            raise ValueError(
                "reward_hack_mitigation kl_control/pid_lagrangian is mutually "
                "exclusive with ref_model_ema_alpha (both drive the KL/ref "
                "dynamics); pick one"
            )
    # v0.71.26 Stage 2 — PID / rollback tunables require pid_lagrangian mode.
    if tcfg.reward_hack_mitigation != "pid_lagrangian":
        stage2_offenders = [
            name
            for name, default in _REWARD_HACK_STAGE2_DEFAULTS.items()
            if getattr(tcfg, name, default) != default
        ]
        if stage2_offenders:
            raise ValueError(
                f"PID/rollback tunables {stage2_offenders} require "
                "reward_hack_mitigation='pid_lagrangian'"
            )
    # Rollback needs an RL-checkpoint cadence to roll back to.
    if tcfg.reward_hack_rollback and tcfg.rl_checkpoint_save_every_steps is None:
        raise ValueError(
            "reward_hack_rollback=True requires rl_checkpoint_save_every_steps "
            "to be set (a cadence to roll back to)"
        )
    # max_recovery_attempts=0 with rollback would early-stop on the first HACK
    # streak WITHOUT a single rollback — a footgun (code-review MEDIUM).
    if tcfg.reward_hack_rollback and tcfg.reward_hack_max_recovery_attempts < 1:
        raise ValueError(
            "reward_hack_rollback=True requires "
            "reward_hack_max_recovery_attempts >= 1 (0 would early-stop "
            "before any rollback)"
        )
    # v0.71.26 Stage 3 — reward shaping MUTATES rewards, so it is only valid
    # for a control mode (log_only must stay observe-only).
    if tcfg.reward_hack_reward_shaping:
        if tcfg.reward_hack_mitigation not in ("kl_control", "pid_lagrangian"):
            raise ValueError(
                "reward_hack_reward_shaping requires a control mode "
                "(kl_control / pid_lagrangian); log_only is observe-only"
            )
        if tcfg.reward_hack_shaping_strength <= 0.0:
            raise ValueError(
                "reward_hack_reward_shaping=True requires "
                "reward_hack_shaping_strength > 0"
            )


class SoupConfig(BaseModel):
    """Root config for soup.yaml."""

    base: str = Field(..., description="Base model name or path (HF model ID)")
    task: Literal[
        "sft", "dpo", "grpo", "ppo", "reward_model", "kto", "orpo", "simpo", "ipo",
        "bco", "preference", "pretrain", "embedding", "prm",
        # v0.52.0 Modality II — TTS / classifier-family / distillation.
        "tts", "classifier", "reranker", "cross_encoder", "distill",
        # v0.61.0 Part A — Unlearning (NPO / SimNPO / RMU).
        "unlearn",
        # v0.67.0 Part C — MoLE per-token adapter routing (Mixture of LoRA Experts).
        "moe_lora_routing",
        # v0.71.31 — Online DPO (on-policy generation judged by a pairwise
        # judge OR a reward_model in the loop).
        "online_dpo",
        # v0.71.32 — ASR (Whisper) fine-tuning.
        "asr",
    ] = Field(
        default="sft",
        description=(
            "Training task type. v0.50.0 Part E added 'prm'; v0.52.0 adds "
            "'tts' (TTS fine-tuning), 'classifier' / 'reranker' / "
            "'cross_encoder' (classification heads), and 'distill' "
            "(knowledge distillation). v0.61.0 adds 'unlearn' (NPO / "
            "SimNPO / RMU). v0.67.0 adds 'moe_lora_routing' (per-token "
            "gating over N task LoRAs)."
        ),
    )
    modality: Literal["text", "vision", "audio", "audio_out"] = Field(
        default="text",
        description=(
            "Training modality: text (default), vision (multimodal), audio "
            "(audio-input), or audio_out (audio-output — paired with task='tts', "
            "v0.52.0)."
        ),
    )
    backend: Literal["transformers", "unsloth", "mlx"] = Field(
        default="transformers",
        description=(
            "Training backend: transformers (default), unsloth (2-5x faster on "
            "CUDA), or mlx (Apple Silicon M1-M4)"
        ),
    )
    data: DataConfig
    training: TrainingConfig = Field(default_factory=TrainingConfig)
    output: str = Field(default="./output", description="Output directory for trained model")
    experiment_name: Optional[str] = Field(default=None, description="Experiment name for tracking")
    eval: Optional[EvalConfig] = Field(
        default=None,
        description="Evaluation configuration for auto-eval after training",
    )
    advise: Optional[AdviseConfig] = Field(
        default=None,
        description=(
            "Pre-flight decision settings consumed by `soup advise` "
            "(v0.54.0 — schema-only on SoupConfig)."
        ),
    )

    @field_validator("experiment_name")
    @classmethod
    def experiment_name_safe(cls, value: Optional[str]) -> Optional[str]:
        """Disallow path separators and null bytes in experiment_name."""
        if value is None:
            return value
        if re.search(r'[/\\:\x00]', value):
            raise ValueError(
                "experiment_name must not contain path separators (/ \\ :) or null bytes"
            )
        return value

    @model_validator(mode="before")
    @classmethod
    def _remap_root_level_misplaced_keys(cls, values):
        """v0.40.1 Part B — QA finding C2: users naturally write top-level
        ``lora:`` (LlamaFactory / Axolotl convention) but Soup nests it
        under ``training``. Without remap, Pydantic silently drops the
        misplaced key — including ``lora.init_strategy`` validation.

        Migrate root-level ``lora`` into ``training.lora`` so nested
        validation (Literal["random","pissa","olora"]) actually fires.

        Caller's dict is never mutated — we work on shallow copies, matching
        v0.33.0 #47 / v0.40.0 Part B immutability policy.
        """
        if not isinstance(values, dict):
            return values
        # Detect any misplaced key first so we avoid copying when not needed.
        misplaced_keys = [k for k in ("lora",) if k in values]
        if not misplaced_keys:
            return values
        new_values = dict(values)
        new_training = dict(new_values.get("training") or {})
        for misplaced in misplaced_keys:
            if misplaced in new_training:
                raise ValueError(
                    f"{misplaced!r} found at both root and training level — "
                    f"keep only one (training.{misplaced} preferred)."
                )
            new_training[misplaced] = new_values.pop(misplaced)
        new_values["training"] = new_training
        return new_values

    @model_validator(mode="after")
    def _validate_v028_speed_memory_supported_tasks(self) -> "SoupConfig":
        """v0.28.0 speed/memory features: every transformer-backend trainer
        is wired in v0.35.0 (#60). MLX backend trainers are still
        unsupported. Emit a precise ValueError that names the actual reason
        (MLX backend vs unknown task) so users get the right fix.
        """
        from soup_cli.utils.v028_features import supports_v028_features

        if supports_v028_features(self.task) and self.backend != "mlx":
            return self
        tcfg = self.training
        offenders: list[str] = []
        if tcfg.use_cut_ce:
            offenders.append("use_cut_ce")
        if tcfg.quantization_aware == "fp8":
            offenders.append('quantization_aware="fp8"')
        if tcfg.activation_offloading is not None:
            offenders.append("activation_offloading")
        if tcfg.kernel_auto_compose:
            offenders.append("kernel_auto_compose")
        if not offenders:
            return self
        # Distinct reasons get distinct messages so users don't waste time
        # blaming MLX when their task is the actual offender.
        if self.backend == "mlx":
            raise ValueError(
                f"v0.28.0 features {offenders} are not supported on the "
                f"Apple Silicon mlx backend (no equivalent kernels). "
                "Switch to backend='transformers' or remove these flags."
            )
        raise ValueError(
            f"v0.28.0 features {offenders} are not wired for "
            f"task={self.task!r}. Supported tasks: see "
            "soup_cli.utils.v028_features.supports_v028_features."
        )

    @model_validator(mode="after")
    def _validate_multipack_supported_tasks(self) -> "SoupConfig":
        """v0.37.0 — multipack only ships for sft / pretrain on transformers.

        Multipack rewrites the DataLoader sampler; preference / RLHF tasks
        in v0.37.0 still use the per-pair sampler shape from TRL. MLX
        backend has its own DataLoader path and is not wired.
        """
        if not self.training.multipack:
            return self
        from soup_cli.utils.multipack import supports_multipack

        if self.backend == "mlx":
            raise ValueError(
                "multipack=true is not supported on the mlx backend "
                "in v0.37.0 (sampler injection is HF Trainer-specific). "
                "Use backend='transformers' or set multipack: false."
            )
        if not supports_multipack(self.task):
            raise ValueError(
                f"multipack=true is not supported for task={self.task!r} "
                "in v0.37.0 (only sft and pretrain are wired). "
                "Set multipack: false or switch task."
            )
        return self

    @model_validator(mode="after")
    def _validate_curriculum_dynamic_supported(self) -> "SoupConfig":
        """v0.48.0 Part A — Curriculum-Aware dynamic re-weighting.

        BETA: v0.53.5 #115 widens the allowlist to every transformer-backend
        trainer (sft / pretrain / dpo / grpo / kto / orpo / simpo / ipo / bco /
        reward_model / embedding / ppo / preference) — the v0.53.5
        DynamicCurriculumCallback is shared via ``utils.peft_wiring``.
        MLX backend remains rejected (callback is HF Trainer-specific).
        """
        if not self.training.curriculum_dynamic:
            return self
        if self.backend == "mlx":
            raise ValueError(
                "curriculum_dynamic is not supported on the mlx backend "
                "(callback is HF Trainer-specific). "
                "Use backend='transformers' or set curriculum_dynamic: false."
            )
        supported = {
            "sft", "pretrain", "dpo", "grpo", "kto", "orpo", "simpo", "ipo",
            "bco", "reward_model", "embedding", "ppo", "preference",
        }
        if self.task not in supported:
            raise ValueError(
                f"curriculum_dynamic is not supported for task={self.task!r} "
                "(only transformer-backend trainers in v0.53.5). "
                "Set curriculum_dynamic: false or switch task."
            )
        return self

    @model_validator(mode="after")
    def _validate_longlora_compat(self) -> "SoupConfig":
        """v0.49.0 Part C — LongLoRA S² shifted-sparse attention requires
        ``task=sft``, ``backend=transformers``, and a Llama-family base.

        Live forward override is deferred to v0.49.1 (mirrors v0.27.0 MII /
        v0.37.0 multipack stub-then-live pattern); the schema gate prevents
        misconfiguration today.
        """
        if not self.training.use_longlora:
            return self
        from soup_cli.utils.longlora import validate_longlora_compat

        try:
            validate_longlora_compat(
                model_name=self.base,
                task=self.task,
                backend=self.backend,
                use_ring_attention=self.training.use_ring_attention,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_grpo_variant_supported(self) -> "SoupConfig":
        """v0.50.0 Part A — ``grpo_variant`` only valid on task='grpo' and
        transformers/unsloth backends. MLX rejected with distinct message
        (matches v0.34.0 review-fix policy of distinct error reasons).

        Live loss kernels for non-standard variants are deferred to v0.50.1;
        a yellow advisory at trainer construction time will name the
        deferred wiring (mirrors v0.40.0 Part D ``NotImplementedError``
        stub-then-live pattern).
        """
        if self.training.grpo_variant is None:
            return self
        if self.task != "grpo":
            raise ValueError(
                f"grpo_variant is only valid when task='grpo'; "
                f"got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "grpo_variant is not supported on backend=mlx in v0.50.0 "
                "(MLX GRPO is scaffolded; new RL objectives transformers-only)"
            )
        return self

    @model_validator(mode="after")
    def _validate_long_context_grpo(self) -> "SoupConfig":
        """v0.50.0 Part B — ``long_context_grpo`` compatibility gate.

        Delegates to :func:`grpo_long_context.validate_long_context_grpo_compat`
        so the rules are single-source-of-truth (mirrors v0.49.0 LongLoRA).
        Live Tiled MLP wiring is deferred to v0.56.0.
        """
        if not self.training.long_context_grpo:
            return self
        from soup_cli.utils.grpo_long_context import (
            validate_long_context_grpo_compat,
        )

        try:
            validate_long_context_grpo_compat(
                task=self.task,
                backend=self.backend,
                use_ring_attention=self.training.use_ring_attention,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_prm_compat(self) -> "SoupConfig":
        """v0.50.0 Part E — ``task='prm'`` schema gate.

        Delegates to :func:`prm.validate_prm_compat` so the rules are
        single-source-of-truth. Live PRM trainer wrapper is deferred to
        v0.50.1.
        """
        if self.task != "prm":
            return self
        from soup_cli.utils.prm import validate_prm_compat

        try:
            validate_prm_compat(
                task=self.task,
                data_format=self.data.format,
                backend=self.backend,
                modality=self.modality,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_vision_grpo(self) -> "SoupConfig":
        """v0.50.0 Part E — ``vision_grpo=True`` compat gate."""
        if not self.training.vision_grpo:
            return self
        from soup_cli.utils.prm import validate_vision_grpo_compat

        try:
            validate_vision_grpo_compat(
                task=self.task,
                modality=self.modality,
                backend=self.backend,
                base=self.base,  # v0.53.3 #129 — name-regex VLM probe
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_grpo_stability_task_gate(self) -> "SoupConfig":
        """v0.50.0 Part D — GRPO-specific stability knobs require task='grpo'.

        Surfaces a probable footgun where a user sets one of the seven new
        GRPO stability fields on a non-GRPO task (where they are a silent
        no-op). Mirrors v0.49.0 LongLoRA / v0.48.0 curriculum_dynamic
        task-gate policy.
        """
        tcfg = self.training
        grpo_only_fields = {
            "ref_model_ema_alpha": tcfg.ref_model_ema_alpha,
            "replay_buffer_size": tcfg.replay_buffer_size,
            "async_grpo_prefetch": tcfg.async_grpo_prefetch,
            "tis_threshold": tcfg.tis_threshold,
            "mask_truncated_completions": tcfg.mask_truncated_completions,
            "defer_rerolling": tcfg.defer_rerolling,
            "skip_zero_advantage": tcfg.skip_zero_advantage,
            "off_policy_mask_threshold": tcfg.off_policy_mask_threshold,
            "grpo_fp16": tcfg.grpo_fp16,
        }
        # Bool defaults are False; Optional defaults are None.
        active = [
            name for name, value in grpo_only_fields.items()
            if value not in (None, False)
        ]
        if not active:
            return self
        if self.task != "grpo":
            raise ValueError(
                f"GRPO stability fields {active} require task='grpo'; "
                f"got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                f"GRPO stability fields {active} are not supported on "
                "backend=mlx in v0.50.0"
            )
        return self

    @model_validator(mode="after")
    def _validate_grpo_fp16_amp_exclusive(self) -> "SoupConfig":
        """v0.53.3 #128 — ``grpo_fp16`` and ``auto_mixed_precision`` are
        mutually exclusive.

        Both flags pick the mixed-precision dtype but go through different
        codepaths (``grpo_fp16`` forces ``fp16=True, bf16=False`` on
        GRPOConfig directly; ``auto_mixed_precision`` runs the v0.32.0
        per-model + per-GPU picker). Combining them is a footgun where the
        downstream behaviour depends on order-of-evaluation — fail fast at
        config-load with a friendly message naming both flags so the user
        picks one.
        """
        # Short-circuit when task is not 'grpo' so the v0.50.0 stability
        # task-gate error fires first (code-review HIGH fix — keeps a
        # consistent "wrong-task" diagnosis ahead of the mutual-exclusion
        # one, regardless of validator execution order).
        if self.task != "grpo":
            return self
        if self.training.grpo_fp16 and self.training.auto_mixed_precision:
            raise ValueError(
                "grpo_fp16=True and auto_mixed_precision=True are mutually "
                "exclusive — both pick the mixed-precision dtype but go "
                "through different codepaths. Pick one: grpo_fp16 forces "
                "FP16 (unsloth parity), auto_mixed_precision uses the "
                "v0.32.0 per-GPU picker."
            )
        return self

    @model_validator(mode="after")
    def _validate_hub_supported(self) -> "SoupConfig":
        """v0.51.0 Part E — ``hub`` other than ``hf`` requires a non-mlx
        backend.

        ``mlx-lm`` has no ModelScope/Modelers download integration, so a
        config that pairs ``backend: mlx`` + ``hub: modelscope`` would fail
        at runtime with a confusing ``mlx-lm`` error. Reject loudly at
        config-load with a distinct message (matches v0.34.0 review-fix
        policy).
        """
        if self.training.hub == "hf":
            return self
        if self.backend == "mlx":
            raise ValueError(
                f"hub={self.training.hub!r} is not supported on "
                "backend=mlx (mlx-lm only downloads from HF Hub). "
                "Use hub='hf' on the mlx backend."
            )
        return self

    @model_validator(mode="after")
    def _validate_tts_compat(self) -> "SoupConfig":
        """v0.52.0 Part A — ``task='tts'`` gate."""
        tcfg = self.training
        if self.task != "tts" and tcfg.tts_family is None and tcfg.tts_emotion is None:
            return self
        if self.task == "tts":
            from soup_cli.utils.tts import (
                validate_emotion_tag,
                validate_tts_compat,
            )

            try:
                validate_tts_compat(
                    task=self.task,
                    modality=self.modality,
                    backend=self.backend,
                )
            except ValueError as exc:
                raise ValueError(str(exc)) from exc
            if tcfg.tts_family is None:
                raise ValueError(
                    "task='tts' requires training.tts_family in "
                    "(orpheus, sesame_csm, llasa, spark, oute)"
                )
            if tcfg.tts_emotion is not None:
                try:
                    validate_emotion_tag(tcfg.tts_emotion, family=tcfg.tts_family)
                except ValueError as exc:
                    raise ValueError(str(exc)) from exc
            return self
        # tts_family / tts_emotion outside task='tts' is a silent-no-op
        # footgun; reject loudly (mirrors v0.50.0 GRPO stability policy).
        if tcfg.tts_family is not None:
            raise ValueError(
                f"training.tts_family={tcfg.tts_family!r} requires task='tts'; "
                f"got task={self.task!r}"
            )
        if tcfg.tts_emotion is not None:
            raise ValueError(
                f"training.tts_emotion={tcfg.tts_emotion!r} requires task='tts'; "
                f"got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_classifier_compat(self) -> "SoupConfig":
        """v0.52.0 Part B — classifier / reranker / cross_encoder gate.

        Lazy-import policy (code-review fix): guard the import behind the
        cheap str-only ``self.task`` check so the common ``task='sft'`` hot
        path does not pay an import cost on every config load.
        """
        tcfg = self.training
        classifier_tasks = {"classifier", "reranker", "cross_encoder"}
        classifier_fields_set = (
            tcfg.num_labels is not None
            or tcfg.classifier_kind is not None
            or tcfg.label_names is not None
            # v0.71.12 #146 — classifier_lora is an opt-in (default False); a
            # True value outside the classifier family is a silent-no-op
            # footgun, so it counts as a classifier-only field.
            or bool(getattr(tcfg, "classifier_lora", False))
        )
        if self.task not in classifier_tasks and not classifier_fields_set:
            return self
        from soup_cli.utils.classifier import (
            is_classifier_task,
            validate_classifier_compat,
        )

        if is_classifier_task(self.task):
            try:
                validate_classifier_compat(
                    task=self.task,
                    backend=self.backend,
                    modality=self.modality,
                )
            except ValueError as exc:
                raise ValueError(str(exc)) from exc
            if tcfg.num_labels is None:
                raise ValueError(
                    f"task={self.task!r} requires training.num_labels "
                    "(positive int <= 1024)"
                )
            if (
                tcfg.label_names is not None
                and len(tcfg.label_names) != tcfg.num_labels
            ):
                raise ValueError(
                    f"len(label_names)={len(tcfg.label_names)} does not "
                    f"match num_labels={tcfg.num_labels}"
                )
            return self
        # Reject classifier-only fields when task is not a classifier task.
        for field in ("num_labels", "classifier_kind", "label_names"):
            value = getattr(tcfg, field)
            if value is not None:
                raise ValueError(
                    f"training.{field} requires task in "
                    "(classifier, reranker, cross_encoder); "
                    f"got task={self.task!r}"
                )
        if getattr(tcfg, "classifier_lora", False):
            raise ValueError(
                "training.classifier_lora requires task in "
                "(classifier, reranker, cross_encoder); "
                f"got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_distill_compat(self) -> "SoupConfig":
        """v0.52.0 Part C — ``task='distill'`` gate."""
        tcfg = self.training
        # v0.71.12 #145 — distill_mode defaults to "token"; a non-default
        # "sequence" counts as a distill-only field (silent-no-op footgun
        # rejection outside task='distill').
        distill_mode_set = tcfg.distill_mode != "token"
        distill_fields_set = (
            tcfg.teacher_model is not None
            or tcfg.distill_divergence is not None
            or tcfg.distill_temperature is not None
            or distill_mode_set
        )
        if self.task == "distill":
            from soup_cli.utils.distill import validate_distill_compat

            try:
                validate_distill_compat(
                    task=self.task,
                    backend=self.backend,
                    teacher_model=tcfg.teacher_model,
                )
            except ValueError as exc:
                raise ValueError(str(exc)) from exc
            return self
        if distill_fields_set:
            offenders = [
                name for name, value in (
                    ("teacher_model", tcfg.teacher_model),
                    ("distill_divergence", tcfg.distill_divergence),
                    ("distill_temperature", tcfg.distill_temperature),
                    ("distill_mode", tcfg.distill_mode if distill_mode_set else None),
                ) if value is not None
            ]
            raise ValueError(
                f"Distillation fields {offenders} require task='distill'; "
                f"got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_bitnet_compat(self) -> "SoupConfig":
        """v0.52.0 Part D — ``quantization='bitnet_1.58'`` gate."""
        if self.training.quantization != "bitnet_1.58":
            return self
        from soup_cli.utils.bitnet import validate_bitnet_compat

        try:
            validate_bitnet_compat(
                task=self.task,
                backend=self.backend,
                modality=self.modality,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_ebft_compat(self) -> "SoupConfig":
        """v0.52.0 Part E — ``ebft_variant`` requires SFT, non-MLX."""
        tcfg = self.training
        if tcfg.ebft_variant is None and tcfg.ebft_temperature is None:
            return self
        if tcfg.ebft_variant is None and tcfg.ebft_temperature is not None:
            raise ValueError(
                "training.ebft_temperature requires training.ebft_variant "
                "to be set"
            )
        from soup_cli.utils.ebft_gdpo import validate_ebft_compat

        try:
            validate_ebft_compat(task=self.task, backend=self.backend)
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_gdpo_compat(self) -> "SoupConfig":
        """v0.52.0 Part E — ``gdpo_variant`` requires DPO/preference, non-MLX."""
        if self.training.gdpo_variant is None:
            return self
        from soup_cli.utils.ebft_gdpo import validate_gdpo_compat

        try:
            validate_gdpo_compat(task=self.task, backend=self.backend)
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_reasoning_effort_task_gate(self) -> "SoupConfig":
        """v0.52.0 Part G (code-review fix) — surface the silent-no-op
        footgun when ``reasoning_effort`` / ``train_on_eot`` is set on a
        task they cannot influence.

        Mirrors v0.50.0 ``_validate_grpo_stability_task_gate`` policy.
        ``reasoning_effort`` only makes sense on SFT-family training
        (sft / pretrain / distill / classifier-family) because the live
        formatter (v0.52.1) will inject a system-prefix token. The other
        tasks (DPO / GRPO / KTO / ORPO / SimPO / IPO / BCO / preference /
        PPO / reward_model / embedding / prm / tts) do not consume it.

        ``train_on_eot`` is an SFT loss-mask flag; setting it on
        DPO/GRPO/etc. is a silent no-op.
        """
        tcfg = self.training
        sft_family_tasks = {
            "sft", "pretrain", "distill",
            "classifier", "reranker", "cross_encoder",
        }
        if tcfg.reasoning_effort is not None and self.task not in sft_family_tasks:
            raise ValueError(
                f"training.reasoning_effort={tcfg.reasoning_effort!r} requires "
                f"task in {sorted(sft_family_tasks)}; got task={self.task!r}"
            )
        if tcfg.train_on_eot and self.task not in sft_family_tasks:
            raise ValueError(
                f"training.train_on_eot=true requires task in "
                f"{sorted(sft_family_tasks)}; got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_moe_expert_quant_compat(self) -> "SoupConfig":
        """v0.52.0 Part F — ``moe_expert_quant`` + ``train_router_only`` gates."""
        tcfg = self.training
        from soup_cli.utils.moe_quant import (
            validate_moe_expert_quant_compat,
            validate_train_router_only_compat,
        )

        if tcfg.moe_expert_quant is not None:
            try:
                validate_moe_expert_quant_compat(
                    backend=self.backend, moe_lora=tcfg.moe_lora,
                )
            except ValueError as exc:
                raise ValueError(str(exc)) from exc
        if tcfg.train_router_only:
            try:
                validate_train_router_only_compat(
                    backend=self.backend, moe_lora=tcfg.moe_lora,
                )
            except ValueError as exc:
                raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_unfrozen_parameters(self) -> "SoupConfig":
        """v0.71.23 #266 — Spectrum targeted-training compatibility gates.

        ``unfrozen_parameters`` is full fine-tuning of a hand-picked parameter
        set (LoRA off), so it is mutually exclusive with the LoRA feature
        flags and with the other parameter-freezing mechanisms. Wired only in
        the transformers SFT trainer.
        """
        tcfg = self.training
        if not tcfg.unfrozen_parameters:
            return self
        if self.task != "sft":
            raise ValueError(
                f"training.unfrozen_parameters (Spectrum targeted training) "
                f"requires task='sft'; got task={self.task!r}"
            )
        if self.backend != "transformers":
            raise ValueError(
                f"training.unfrozen_parameters requires backend='transformers'; "
                f"got backend={self.backend!r}"
            )
        if self.modality != "text":
            raise ValueError(
                f"training.unfrozen_parameters requires modality='text' "
                f"(the Spectrum branch is wired in the text SFT trainer); "
                f"got modality={self.modality!r}"
            )
        if tcfg.quantization != "none":
            raise ValueError(
                f"training.unfrozen_parameters (Spectrum full fine-tuning) "
                f"requires quantization='none' (got {tcfg.quantization!r}); "
                f"quantized weights cannot be trained directly. For a quantized "
                f"run, drop unfrozen_parameters and use LoRA (QLoRA)."
            )
        freeze_conflicts = []
        if tcfg.freeze_layers is not None:
            freeze_conflicts.append("freeze_layers")
        if tcfg.freeze_ratio is not None:
            freeze_conflicts.append("freeze_ratio")
        if tcfg.train_router_only:
            freeze_conflicts.append("train_router_only")
        if tcfg.expand_layers is not None:
            freeze_conflicts.append("expand_layers")
        if tcfg.freeze_trainable_layers is not None:
            freeze_conflicts.append("freeze_trainable_layers")
        if freeze_conflicts:
            raise ValueError(
                f"training.unfrozen_parameters is mutually exclusive with "
                f"{', '.join(freeze_conflicts)} (each independently selects "
                f"which parameters train)"
            )
        lcfg = tcfg.lora
        lora_conflicts = []
        if lcfg.use_dora:
            lora_conflicts.append("lora.use_dora")
        if lcfg.use_vera:
            lora_conflicts.append("lora.use_vera")
        if lcfg.use_olora:
            lora_conflicts.append("lora.use_olora")
        if lcfg.use_rslora:
            lora_conflicts.append("lora.use_rslora")
        if lcfg.target_parameters:
            lora_conflicts.append("lora.target_parameters")
        if tcfg.moe_lora:
            lora_conflicts.append("moe_lora")
        if tcfg.use_longlora:
            lora_conflicts.append("use_longlora")
        if tcfg.relora_steps is not None:
            lora_conflicts.append("relora_steps")
        if tcfg.loraplus_lr_ratio is not None:
            lora_conflicts.append("loraplus_lr_ratio")
        if lora_conflicts:
            raise ValueError(
                f"training.unfrozen_parameters (Spectrum full fine-tuning, "
                f"LoRA off) is mutually exclusive with LoRA features: "
                f"{', '.join(lora_conflicts)}"
            )
        return self

    @model_validator(mode="after")
    def _validate_lisa_compat(self) -> "SoupConfig":
        """v0.71.34 #267 — LISA compatibility gates.

        LISA is full-FT of a rotating set of decoder layers (LoRA off), so it
        shares Spectrum's ``unfrozen_parameters`` gate: transformers + text +
        quantization=none, mutually exclusive with the LoRA feature flags, the
        other freeze mechanisms, and ``unfrozen_parameters`` itself.

        #307 — the task gate is ``_LISA_SUPPORTED_TASKS`` rather than ``sft``
        alone: continued pre-training is the same full-FT-of-active-layers
        mechanism, and ``trainer/pretrain.py`` wires the callback the same way.
        """
        tcfg = self.training
        if not tcfg.lisa_enabled:
            # Footgun: a non-default lisa_* while LISA is off almost certainly
            # means the user forgot lisa_enabled=true.
            if (
                tcfg.lisa_num_layers != 2
                or tcfg.lisa_interval_steps != 20
                or tcfg.lisa_reset_optimizer is not True
                or tcfg.lisa_train_embeddings is not True
            ):
                raise ValueError(
                    "training.lisa_* set but lisa_enabled is false — set "
                    "lisa_enabled=true to use LISA layer sampling."
                )
            return self
        if self.task not in _LISA_SUPPORTED_TASKS:
            raise ValueError(
                f"training.lisa_enabled (LISA layerwise sampling) requires "
                f"task in "
                f"{', '.join(repr(t) for t in _LISA_SUPPORTED_TASKS)}; "
                f"got task={self.task!r}"
            )
        if self.backend != "transformers":
            raise ValueError(
                f"training.lisa_enabled requires backend='transformers'; "
                f"got backend={self.backend!r}"
            )
        if self.modality != "text":
            raise ValueError(
                f"training.lisa_enabled requires modality='text' (the LISA "
                f"callback is wired in the text SFT and pretrain trainers); "
                f"got modality={self.modality!r}"
            )
        if tcfg.quantization != "none":
            raise ValueError(
                f"training.lisa_enabled (LISA full fine-tuning) requires "
                f"quantization='none' (got {tcfg.quantization!r}); quantized "
                f"weights cannot be trained directly."
            )
        freeze_conflicts = []
        if tcfg.freeze_layers is not None:
            freeze_conflicts.append("freeze_layers")
        if tcfg.freeze_ratio is not None:
            freeze_conflicts.append("freeze_ratio")
        if tcfg.train_router_only:
            freeze_conflicts.append("train_router_only")
        if tcfg.expand_layers is not None:
            freeze_conflicts.append("expand_layers")
        if tcfg.freeze_trainable_layers is not None:
            freeze_conflicts.append("freeze_trainable_layers")
        if tcfg.unfrozen_parameters:
            freeze_conflicts.append("unfrozen_parameters")
        if freeze_conflicts:
            raise ValueError(
                f"training.lisa_enabled is mutually exclusive with "
                f"{', '.join(freeze_conflicts)} (each independently selects "
                f"which parameters train)"
            )
        lcfg = tcfg.lora
        lora_conflicts = []
        if lcfg.use_dora:
            lora_conflicts.append("lora.use_dora")
        if lcfg.use_vera:
            lora_conflicts.append("lora.use_vera")
        if lcfg.use_olora:
            lora_conflicts.append("lora.use_olora")
        if lcfg.use_rslora:
            lora_conflicts.append("lora.use_rslora")
        if lcfg.target_parameters:
            lora_conflicts.append("lora.target_parameters")
        if tcfg.moe_lora:
            lora_conflicts.append("moe_lora")
        if tcfg.use_longlora:
            lora_conflicts.append("use_longlora")
        if tcfg.relora_steps is not None:
            lora_conflicts.append("relora_steps")
        if tcfg.loraplus_lr_ratio is not None:
            lora_conflicts.append("loraplus_lr_ratio")
        if lora_conflicts:
            raise ValueError(
                f"training.lisa_enabled (LISA full fine-tuning, LoRA off) is "
                f"mutually exclusive with LoRA features: "
                f"{', '.join(lora_conflicts)}"
            )
        return self

    @model_validator(mode="after")
    def _validate_full_finetune(self) -> "SoupConfig":
        """#340 — ``lora.r: 0`` = full fine-tuning (no adapter).

        The third member of the "LoRA off" family, sharing Spectrum's and
        LISA's gate: sft + transformers + text + ``quantization='none'``,
        mutually exclusive with the LoRA feature flags and with the other two
        (each independently decides what trains).

        Scoped to ``task in ('sft', 'embedding')`` (#340, #700). ``classifier`` /
        ``reranker`` / ``cross_encoder`` (``classifier_lora``, v0.71.12 #146) and
        ``asr`` (``asr_lora``, v0.71.32) have gated LoRA behind their own opt-in flag
        for releases, so ``lora.r: 0`` already parses and is already harmless
        there; a blanket gate would break configs that work today. Tasks that
        do pass the rank straight to peft keep their existing behaviour
        (peft's "`r` should be a positive integer value") rather than gaining
        a new refusal this issue never measured.
        """
        tcfg = self.training
        if tcfg.lora.r != 0 or self.task not in ("sft", "embedding"):
            return self
        if tcfg.stream_layers:
            # Streaming has its own, more specific refusal (the decoder lives
            # on the meta device, so there is nothing to full fine-tune).
            # Let it speak rather than reporting a quantization mismatch.
            return self
        if self.backend != "transformers":
            raise ValueError(
                f"training.lora.r=0 (full fine-tuning) requires "
                f"backend='transformers'; got backend={self.backend!r}. The "
                f"full-FT branch is wired in the transformers SFT and embedding trainers."
            )
        if self.modality != "text":
            raise ValueError(
                f"training.lora.r=0 (full fine-tuning) requires "
                f"modality='text'; got modality={self.modality!r}. The "
                f"vision / audio setups always attach an adapter."
            )
        if tcfg.quantization != "none":
            raise ValueError(
                f"training.lora.r=0 (full fine-tuning) requires "
                f"quantization='none' (got {tcfg.quantization!r}); quantized "
                f"weights cannot be trained directly. For a quantized run use "
                f"LoRA (QLoRA) with lora.r >= 1."
            )
        mode_conflicts = []
        if tcfg.unfrozen_parameters:
            mode_conflicts.append("unfrozen_parameters")
        if tcfg.lisa_enabled:
            mode_conflicts.append("lisa_enabled")
        if tcfg.train_router_only:
            mode_conflicts.append("train_router_only")
        if mode_conflicts:
            raise ValueError(
                f"training.lora.r=0 (full fine-tuning) is mutually exclusive "
                f"with {', '.join(mode_conflicts)} (each independently selects "
                f"which parameters train)"
            )
        lcfg = tcfg.lora
        lora_conflicts = []
        if lcfg.use_dora:
            lora_conflicts.append("lora.use_dora")
        if lcfg.use_vera:
            lora_conflicts.append("lora.use_vera")
        if lcfg.use_olora:
            lora_conflicts.append("lora.use_olora")
        if lcfg.use_rslora:
            lora_conflicts.append("lora.use_rslora")
        if lcfg.rank_pattern is not None:
            lora_conflicts.append("lora.rank_pattern")
        if lcfg.alpha_pattern is not None:
            lora_conflicts.append("lora.alpha_pattern")
        if lcfg.target_parameters:
            lora_conflicts.append("lora.target_parameters")
        if getattr(lcfg, "init_strategy", "random") != "random":
            lora_conflicts.append("lora.init_strategy")
        if tcfg.moe_lora:
            lora_conflicts.append("moe_lora")
        if tcfg.use_longlora:
            lora_conflicts.append("use_longlora")
        if tcfg.relora_steps is not None:
            lora_conflicts.append("relora_steps")
        if tcfg.loraplus_lr_ratio is not None:
            lora_conflicts.append("loraplus_lr_ratio")
        if lora_conflicts:
            raise ValueError(
                f"training.lora.r=0 means full fine-tuning (no adapter), so it "
                f"is mutually exclusive with LoRA features: "
                f"{', '.join(lora_conflicts)}. Set lora.r >= 1 to use them."
            )
        return self

    @model_validator(mode="after")
    def _validate_lora_target_parameters_scope(self) -> "SoupConfig":
        """#573 — raw-parameter LoRA is live in resident SFT/pretrain only."""
        targets = self.training.lora.target_parameters
        if not targets:
            return self
        if self.task not in ("sft", "pretrain"):
            raise ValueError(
                "training.lora.target_parameters requires task='sft' or "
                f"task='pretrain'; got task={self.task!r}"
            )
        if self.backend != "transformers":
            raise ValueError(
                "training.lora.target_parameters requires backend='transformers'; "
                f"got backend={self.backend!r}"
            )
        if self.modality != "text":
            raise ValueError(
                "training.lora.target_parameters requires modality='text'; "
                f"got modality={self.modality!r}"
            )
        if self.training.stream_layers:
            raise ValueError(
                "training.lora.target_parameters is not yet supported with "
                "training.stream_layers=true; use resident SFT/pretrain"
            )
        return self

    @model_validator(mode="after")
    def _validate_stream_layers_compat(self) -> "SoupConfig":
        """v0.72.0 BETA — layer-streaming compatibility gates.

        Scope is deliberately narrow for the first cut: RAM tier, bf16, sft,
        Llama/Qwen, batch 1, no gradient accumulation. Every refusal names the
        release that lifts it, so a rejected config tells the user what to wait
        for rather than just saying no.
        """
        tcfg = self.training
        if not tcfg.stream_layers:
            # Footgun: a non-default stream_* while streaming is off almost
            # certainly means the user forgot stream_layers=true.
            if (
                tcfg.stream_source != "auto"
                or tcfg.stream_ngram_source != "auto"
                or tcfg.stream_buffers != 2
                or tcfg.stream_vram_override is not None
                or tcfg.stream_vram_probe
                or tcfg.stream_disk_kind is not None
                or tcfg.stream_pin is not None
            ):
                raise ValueError(
                    "training.stream_source / training.stream_ngram_source / "
                    "training.stream_buffers / "
                    "training.stream_vram_override / training.stream_vram_probe "
                    "/ training.stream_disk_kind / training.stream_pin set but "
                    "stream_layers is false; set stream_layers=true to stream the "
                    "base layer-by-layer."
                )
            return self
        # v0.72.4 — the four preference losses join SFT. DPO and KTO take their
        # reference from the SAME streamed base with adapters disabled (TRL's
        # `null_ref_context`), so the reference costs no extra weights: measured
        # 0.914x SFT peak VRAM where a second instance was 9.92x.
        if self.task in _STREAM_ROLLOUT_TASKS:
            raise ValueError(
                f"training.stream_layers cannot be used with task={self.task!r}: "
                f"it needs generation rollouts, which re-read every layer once "
                f"per generated token. That destroys the amortisation streaming "
                f"depends on (one weight read per step, not per token), so this "
                f"is a permanent exclusion rather than an unimplemented one."
            )
        if self.task not in _STREAM_SUPPORTED_TASKS:
            raise ValueError(
                f"training.stream_layers supports task in "
                f"{sorted(_STREAM_SUPPORTED_TASKS)}; got task={self.task!r}."
            )
        if tcfg.stream_vram_probe and self.task != "sft":
            # v0.73.1 #349 — the probe runs a plain causal-LM forward+backward,
            # which IS the SFT step. A preference loss concatenates chosen and
            # rejected and reduces logits to per-token log-probs, so the probe is
            # measuring a different computation and its agreement with the real
            # step is not established. ONE shape was measured (SmolLM2-135M,
            # batch 2 x seq 2048: probe 6.02 GB against a real DPO step's
            # 5.30 GB, i.e. +13.5%, the same safe direction it shows for SFT) —
            # one point is not a validation, and the failure mode if the sign
            # ever flips is a gate that waves through over-budget runs, which is
            # worse than no probe. Restricted until it is measured across shapes.
            raise ValueError(
                f"training.stream_vram_probe supports task='sft' only; got "
                f"task={self.task!r}. The probe measures a causal-LM step, which "
                f"is the SFT step but not a preference loss — its agreement with "
                f"DPO/ORPO/SimPO/KTO is measured at one shape only, so it is not "
                f"offered for them yet. Use the predicted budget, or "
                f"training.stream_vram_override if you have measured this "
                f"configuration yourself."
            )
        if self.backend != "transformers":
            raise ValueError(
                f"training.stream_layers requires backend='transformers'; got "
                f"backend={self.backend!r}. Streaming replaces the model load "
                f"path, which unsloth and mlx own themselves."
            )
        if self.modality != "text":
            raise ValueError(
                f"training.stream_layers requires modality='text'; got "
                f"modality={self.modality!r}."
            )
        if tcfg.quantization not in ("none", "4bit"):
            raise ValueError(
                f"training.stream_layers supports quantization='none' or "
                f"'4bit' (NF4); got {tcfg.quantization!r}. Other quantisations "
                f"store weights in formats that cannot be streamed into a "
                f"pooled buffer."
            )
        # v0.72.3: the disk overflow tier is live. It is still NVMe-only —
        # `choose_tier` refuses spinning disks, where each step costs two seeks
        # per layer (plan P11) and the run thrashes rather than merely running
        # slower. `soup doctor` reports the detected media type.
        # v0.72.3: any concrete batch size is supported — bigger batches are
        # where streaming PAYS OFF, since one weight read is amortised over more
        # tokens. "auto" is still refused: it resolves by OOM-probing a resident
        # model, and under streaming that model never loads, so the probe would
        # measure something that does not exist.
        if tcfg.batch_size == "auto":
            raise ValueError(
                "training.stream_layers cannot use batch_size='auto': the probe "
                "sizes a RESIDENT model, which a streaming run never loads. Set "
                "a concrete batch_size — the pre-flight predicts peak VRAM for "
                "it and refuses the run if it will not fit."
            )
        # v0.72.3: accumulation is supported. Measured on this box, it is
        # per-TOKEN I/O-neutral — layer reads per 1k tokens held constant across
        # accum 1/2/4 — because N micro-batches read the base N times but also
        # process N times the tokens. Its cost is opportunity cost: reaching the
        # same effective batch by raising batch_size was 2.52x faster. The
        # pre-flight says so rather than the schema refusing it, because
        # accumulation is the ONLY way to raise effective batch once the VRAM
        # budget is exhausted (peak moved 0.842 -> 0.846 GB across accum 1->4).
        # v0.72.4 — KTO's KL term is degenerate at a per-device batch of 1, so
        # TRL refuses it outright ("Actual (not effective) batch size must be
        # > 1"). Under streaming that ValueError arrives only AFTER the RAM
        # pre-flight, the checkpoint sharding and — at quantization='4bit' —
        # the NF4 quantisation pass: minutes of disk I/O on a real base, to
        # fail on a config that was already invalid. Refuse it at parse time.
        if (
            self.task == "kto"
            and isinstance(tcfg.batch_size, int)
            and tcfg.batch_size < 2
        ):
            raise ValueError(
                "task='kto' requires training.batch_size >= 2 (TRL's KL term is "
                "degenerate at batch 1). Checked here rather than in the "
                "trainer so a streaming run fails before sharding the "
                "checkpoint, not minutes into it."
            )
        if tcfg.lora.r < 1:
            raise ValueError(
                "training.stream_layers requires LoRA (training.lora.r >= 1) — "
                "the streamed base is frozen and its decoder weights live on "
                "the meta device, so full fine-tuning (lora.r=0) is not merely "
                "unwise here, it is impossible: there would be nothing "
                "trainable and the run would be a no-op."
            )
        # LoRA variants that READ the real base weight during
        # get_peft_model() cannot work against a meta skeleton: DoRA computes a
        # weight norm, PiSSA/OLoRA take an SVD of it, VeRA shares projections
        # sized from it. Refuse by name rather than let torch raise an opaque
        # "Fan in and fan out can not be computed" from the meta tensor.
        if tcfg.lora.use_dora:
            raise ValueError(
                "training.stream_layers is incompatible with lora.use_dora: "
                "DoRA initialises from the base weight's norm, but the "
                "streamed base is on the meta device at adapter-build time. "
                "Use plain LoRA (lora.use_rslora is fine)."
            )
        if tcfg.lora.use_vera:
            raise ValueError(
                "training.stream_layers is incompatible with lora.use_vera: "
                "VeRA's shared projections are built from the materialised "
                "base weights, which streaming keeps on the meta device."
            )
        if tcfg.moe_expert_quant is not None:
            raise ValueError(
                "training.stream_layers is incompatible with "
                f"training.moe_expert_quant={tcfg.moe_expert_quant!r}: expert "
                "quantization is applied only by the resident model-construction "
                "path and would otherwise be silently ignored. Leave "
                "moe_expert_quant unset when streaming."
            )
        if getattr(tcfg.lora, "init_strategy", "random") != "random":
            raise ValueError(
                f"training.stream_layers requires lora.init_strategy='random' "
                f"(got {tcfg.lora.init_strategy!r}): PiSSA/OLoRA/LoftQ "
                f"initialise the adapter from an SVD of the base weight, which "
                f"is on the meta device under streaming."
            )
        conflicts = []
        if tcfg.unfrozen_parameters:
            conflicts.append("unfrozen_parameters")
        if tcfg.lisa_enabled:
            conflicts.append("lisa_enabled")
        if tcfg.packing:
            conflicts.append("packing")
        if tcfg.multipack:
            conflicts.append("multipack")
        if tcfg.use_fsdp2_compile:
            conflicts.append("use_fsdp2_compile")
        if tcfg.train_router_only:
            conflicts.append("train_router_only")
        if tcfg.expand_layers is not None:
            conflicts.append("expand_layers")
        if conflicts:
            raise ValueError(
                f"training.stream_layers is mutually exclusive with "
                f"{', '.join(conflicts)}: streaming owns the model-construction "
                f"path (meta skeleton + per-layer weight substitution) and "
                f"cannot share it with a feature that rewrites or re-freezes "
                f"the same layers."
            )
        return self

    @model_validator(mode="after")
    def _validate_rollout_backend(self) -> "SoupConfig":
        """v0.50.0 Part C — ``rollout_backend`` requires task='grpo' and a
        non-mlx backend. Live launcher wired in v0.71.21 (#125):
        openenv requires ``rollout_func`` and ``rollout_func`` is
        openenv-only (silent-no-op footgun rejection)."""
        if (
            self.training.rollout_func is not None
            and self.training.rollout_backend != "openenv"
        ):
            raise ValueError(
                "rollout_func requires rollout_backend='openenv'; got "
                f"rollout_backend={self.training.rollout_backend!r}"
            )
        if self.training.rollout_backend is None:
            return self
        if self.task != "grpo":
            raise ValueError(
                f"rollout_backend requires task='grpo'; got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "rollout_backend is not supported on backend=mlx in v0.50.0"
            )
        if (
            self.training.rollout_backend == "openenv"
            and self.training.rollout_func is None
        ):
            raise ValueError(
                "rollout_backend='openenv' requires training.rollout_func "
                "('module.path:function_name')"
            )
        return self

    @model_validator(mode="after")
    def _validate_prm_reward(self) -> "SoupConfig":
        """v0.71.30 — PRM-guided GRPO gate.

        ``prm_reward`` runs a PRM (base CausalLM + reward head) forward as the
        GRPO reward, so it requires ``task='grpo'`` on ``backend='transformers'``
        with ``modality='text'``. A non-default ``prm_aggregate`` while
        ``prm_reward`` is unset silently no-ops → reject as a footgun.
        """
        if self.training.prm_reward is None:
            if self.training.prm_aggregate != "min":
                raise ValueError(
                    "prm_aggregate is only meaningful with prm_reward set; "
                    f"got prm_aggregate={self.training.prm_aggregate!r} and "
                    "prm_reward=None"
                )
            return self
        if self.task != "grpo":
            raise ValueError(
                f"prm_reward requires task='grpo'; got task={self.task!r}"
            )
        if self.backend != "transformers":
            raise ValueError(
                "prm_reward requires backend='transformers' (the PRM reward "
                f"runs a transformers forward); got backend={self.backend!r}"
            )
        if self.modality != "text":
            raise ValueError(
                f"prm_reward requires modality='text'; got modality={self.modality!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_online_dpo_compat(self) -> "SoupConfig":
        """v0.71.31 — Online DPO gate.

        ``task='online_dpo'`` generates on-policy and needs exactly one reward
        signal — a pairwise judge (``online_dpo_judge``) OR a ``reward_model``.
        It runs a transformers generation + LoRA loop, so it requires
        ``backend='transformers'`` with ``modality='text'``. Any
        ``online_dpo_*`` field set on another task silently no-ops → reject as a
        footgun.
        """
        t = self.training
        online_set = (
            t.online_dpo_judge is not None
            or t.online_dpo_loss_type != "sigmoid"
            or t.online_dpo_max_new_tokens != 64
        )
        if self.task == "online_dpo":
            if self.backend != "transformers":
                raise ValueError(
                    "task='online_dpo' requires backend='transformers'; "
                    f"got backend={self.backend!r}"
                )
            if self.modality != "text":
                raise ValueError(
                    "task='online_dpo' requires modality='text'; "
                    f"got modality={self.modality!r}"
                )
            has_judge = t.online_dpo_judge is not None
            has_rm = t.reward_model is not None
            if has_judge and has_rm:
                raise ValueError(
                    "task='online_dpo': set exactly one of "
                    "training.online_dpo_judge or training.reward_model, not both"
                )
            if not has_judge and not has_rm:
                raise ValueError(
                    "task='online_dpo' needs a judge (training.online_dpo_judge) "
                    "or a training.reward_model"
                )
        elif online_set:
            raise ValueError(
                "training.online_dpo_* fields require task='online_dpo'"
            )
        return self

    @model_validator(mode="after")
    def _validate_reward_fn_multi_compat(self) -> "SoupConfig":
        """v0.71.40 #311 — a comma-separated reward_fn is GRPO-only.

        Only ``trainer/grpo.py::_select_reward_fn`` resolves a multi-reward spec
        into a ``reward_funcs=[...]`` list; ``trainer/ppo.py`` (and every other
        consumer) calls the single-name loader, so a comma there would silently
        pass config validation and then crash at training start with
        ``Unknown reward function``. Footgun-reject it up front (mirrors the
        online_dpo / asr / lisa task gates).
        """
        rf = self.training.reward_fn
        if isinstance(rf, str) and "," in rf and self.task != "grpo":
            raise ValueError(
                f"a comma-separated training.reward_fn (an ensemble, {rf!r}) is "
                f"only supported for task='grpo'; got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_asr_compat(self) -> "SoupConfig":
        """v0.71.32 — ASR (Whisper) gate.

        ``task='asr'`` runs a transformers ``Seq2SeqTrainer`` on
        ``WhisperForConditionalGeneration``; it requires
        ``backend='transformers'`` (mlx / unsloth have no Whisper path). The
        ``asr_language`` / ``asr_task`` knobs only affect the ASR decoder, so
        setting them on another task silently no-ops → reject as a footgun.
        """
        t = self.training
        asr_set = (
            t.asr_language is not None
            or t.asr_task != "transcribe"
            or bool(t.asr_lora)
        )
        if self.task == "asr":
            if self.backend != "transformers":
                raise ValueError(
                    "task='asr' requires backend='transformers'; "
                    f"got backend={self.backend!r}"
                )
            if self.data.format not in ("asr", "auto"):
                raise ValueError(
                    "task='asr' requires data.format='asr' (or 'auto'); "
                    f"got data.format={self.data.format!r}"
                )
        elif asr_set:
            raise ValueError(
                "training.asr_language / asr_task / asr_lora require task='asr'"
            )
        return self

    @model_validator(mode="after")
    def _validate_replay_compat(self) -> "SoupConfig":
        """v0.71.36 — continual-learning rehearsal gate.

        Replay interleaves rows from an OLD dataset into train so the model
        does not forget the previous task. v1 covers the plain
        instruction / continued-pretraining paths only.

        packing / multipack concatenate rows into fixed-length blocks, so
        the replay ratio stops being meaningful at block boundaries —
        reject rather than silently mis-mix. Setting replay_ratio /
        replay_seed without data.replay silently no-ops, so reject that as
        a footgun.
        """
        data = self.data
        replay_knobs_set = (
            data.replay_ratio != 0.1 or data.replay_seed is not None
        )
        if data.replay is not None:
            if self.task not in ("sft", "pretrain"):
                raise ValueError(
                    "data.replay requires task='sft' or task='pretrain'; "
                    f"got task={self.task!r}"
                )
            if self.training.packing:
                raise ValueError(
                    "data.replay is incompatible with training.packing "
                    "(packing concatenates rows into fixed blocks, so the "
                    "replay ratio stops being meaningful)"
                )
            if self.training.multipack:
                raise ValueError(
                    "data.replay is incompatible with training.multipack "
                    "(bin-packing breaks the replay ratio)"
                )
        elif replay_knobs_set:
            raise ValueError(
                "data.replay_ratio / data.replay_seed require data.replay"
            )
        return self

    @model_validator(mode="after")
    def _validate_interleave_compat(self) -> "SoupConfig":
        """#443 — data.interleave requires data.train to be a list, and is
        fully parsed here rather than in the field validator:
        num_datasets = len(data.train) is a parse-time constant now that
        train can be list-shaped.

        packing / multipack concatenate rows into fixed-length blocks, so a
        per-source mixture ratio stops being meaningful at block
        boundaries — reject rather than silently mis-mix. Mirrors
        _validate_replay_compat's identical reasoning for the same
        packing/multipack conflict, one validator up.

        #459 extends #443's local-files-only v1 to two more shapes, each a
        DECIDED dispatch rather than an emergent one — every entry is
        classified once (local file / remote URI / HF-hub name) and the
        classes may not mix within one data.train list:

        - All entries local files or remote URIs, data.streaming=true: the
          streaming interleave path (loader._load_interleaved_streaming_datasets)
          delegates to datasets.interleave_datasets / concatenate_datasets —
          see that function's docstring for the strategy-name mapping. A
          remote URI entry without streaming=true still refuses (no
          non-streaming multi-remote-file loader exists).
        - All entries HF-hub dataset names: the hub interleave path
          (loader._load_interleaved_hub_datasets), always eager (never
          streamed) in this cut — streaming N separately-shaped hub
          datasets through their own split negotiation is a distinct,
          larger effort this issue does not implement, so that combination
          keeps refusing, by name.
        - Anything else (a list mixing hub names with local/remote entries)
          keeps refusing — there is no decided answer for how a hub split
          and a local file's row count should reconcile.
        """
        from pathlib import Path

        from soup_cli.utils.data_pipeline import parse_interleave

        data = self.data
        train_is_list = isinstance(data.train, list)

        if data.interleave is not None:
            if not train_is_list:
                raise ValueError(
                    "data.interleave requires data.train to be a list of "
                    ">= 2 local file paths, remote URIs, or HF-hub dataset "
                    "names"
                )
            # Full parse now that num_datasets is a parse-time constant —
            # validates strategy/probs shape against the actual dataset
            # count (parse_interleave is otherwise unmodified by #443).
            parse_interleave(data.interleave, num_datasets=len(data.train))

            if self.training.packing:
                raise ValueError(
                    "data.interleave is incompatible with training.packing "
                    "(packing concatenates rows into fixed blocks, so a "
                    "per-source mixture ratio stops being meaningful)"
                )
            if self.training.multipack:
                raise ValueError(
                    "data.interleave is incompatible with "
                    "training.multipack (bin-packing breaks the "
                    "per-source mixture ratio)"
                )

            # #459 — classify every entry, then dispatch. Kept local to this
            # validator (rather than exported) since loader.py has its own
            # copy for the actual load-time dispatch; the two must agree,
            # so keep the classification RULE (suffix-in-allowlist /
            # "://"-in-entry) the only thing either site encodes, not the
            # classify function itself — see loader._classify_train_entry's
            # docstring.
            #
            # _local_file_extensions duplicates loader.SUPPORTED_EXTENSIONS
            # literally (not imported — that would import loader.py, which
            # imports DataConfig from this module, an import cycle; also
            # loader.py intentionally carries the torch-adjacent deps this
            # module stays light of). A suffix must be one of these to
            # count as 'local' (#468 review fix) — "any non-empty
            # Path.suffix" previously misclassified any hub name with a
            # version number (``teknium/OpenHermes-2.5``,
            # ``mlfoundations/dclm-baseline-1.0``) as a local file.
            _local_file_extensions = {".jsonl", ".json", ".csv", ".parquet", ".txt"}

            def _kind(entry: str) -> str:
                # Scheme-agnostic "://" sniff (#468 review fix), not an
                # is_remote_uri allowlist check — mirrors loader.py's
                # _looks_like_remote_uri. The scheme allowlist is enforced
                # downstream, at load time, by validate_remote_uri (refuses
                # a non-allowlisted scheme BY NAME); classifying only
                # allowlisted schemes as 'remote' here let e.g. an
                # https://... entry with a familiar suffix fall through to
                # 'local' and reach hf_load unvalidated instead.
                if "://" in entry:
                    return "remote"
                if Path(entry).suffix.lower() in _local_file_extensions:
                    return "local"
                return "hub"

            kinds = {_kind(entry) for entry in data.train}

            if kinds == {"hub"}:
                if data.streaming:
                    raise ValueError(
                        "data.interleave with an all-HF-hub-dataset-name "
                        "data.train does not support data.streaming=true — "
                        "streaming N separately-shaped hub datasets and "
                        "reconciling their splits is unimplemented; drop "
                        "streaming or use local files"
                    )
            elif kinds <= {"local", "remote"}:
                if not data.streaming and "remote" in kinds:
                    raise ValueError(
                        "data.interleave list entries that are remote URIs "
                        "require data.streaming=true (no non-streaming "
                        "multi-remote-file loader exists) — set "
                        "data.streaming: true or use only local file paths"
                    )
            else:
                raise ValueError(
                    "data.interleave list entries must be all local file "
                    "paths / remote URIs, or all HF-hub dataset names — "
                    f"not a mix (got kinds={sorted(kinds)})"
                )
        elif train_is_list:
            raise ValueError(
                "data.train as a list requires data.interleave to be set "
                "— otherwise the mixture strategy is undefined"
            )
        return self

    @model_validator(mode="after")
    def _validate_vllm_sleep_mode(self) -> "SoupConfig":
        """v0.50.0 Part B — ``vllm_sleep_mode`` requires task='grpo' and a
        vLLM-compatible backend (transformers/unsloth).

        Sleep mode is a between-rollouts feature; setting it on a non-RL
        task is a probable footgun and silently no-ops, so reject loudly.
        """
        if not self.training.vllm_sleep_mode:
            return self
        if self.task != "grpo":
            raise ValueError(
                f"vllm_sleep_mode requires task='grpo'; got task={self.task!r}"
            )
        from soup_cli.utils.grpo_long_context import (
            validate_vllm_sleep_mode_compat,
        )

        try:
            validate_vllm_sleep_mode_compat(backend=self.backend)
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    # ---- v0.53.0 Quant Menu II cross-validators ----------------------------

    @model_validator(mode="after")
    def _validate_fp8_attention_compat(self) -> "SoupConfig":
        """v0.53.0 Part D — ``fp8_attention=True`` requires
        ``quantization_aware='fp8'`` and a non-mlx backend. Silent-no-op
        footgun rejection (mirrors v0.32.0 spike-recovery policy).
        """
        tcfg = self.training
        if not tcfg.fp8_attention:
            return self
        from soup_cli.utils.advanced_precision import (
            validate_fp8_attention_compat,
        )

        try:
            validate_fp8_attention_compat(
                fp8_attention=tcfg.fp8_attention,
                quantization_aware=tcfg.quantization_aware,
                backend=self.backend,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_nvfp4_compat(self) -> "SoupConfig":
        """v0.53.0 Part D — ``nvfp4=True`` requires non-mlx + text-modality.
        Blackwell SM-capability check is runtime-only (live wiring v0.53.1).
        """
        tcfg = self.training
        if not tcfg.nvfp4:
            return self
        from soup_cli.utils.advanced_precision import validate_nvfp4_compat

        try:
            validate_nvfp4_compat(
                nvfp4=tcfg.nvfp4, backend=self.backend, modality=self.modality,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_unsloth_bnb_4bit_compat(self) -> "SoupConfig":
        """v0.53.0 Part D — ``unsloth_bnb_4bit=True`` requires
        ``backend='unsloth'`` and ``quantization='4bit'``.
        """
        tcfg = self.training
        if not tcfg.unsloth_bnb_4bit:
            return self
        from soup_cli.utils.advanced_precision import (
            validate_unsloth_bnb_4bit_compat,
        )

        try:
            validate_unsloth_bnb_4bit_compat(
                unsloth_bnb_4bit=tcfg.unsloth_bnb_4bit,
                backend=self.backend,
                quantization=tcfg.quantization,
            )
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_bnb_4bit_double_quant(self) -> "SoupConfig":
        """v0.53.0 Part E — ``bnb_4bit_use_double_quant=True`` requires
        ``quantization='4bit'`` (silent-no-op footgun otherwise).

        #321 — the field is tri-state (`None` = unset = shipped default). The
        footgun fires only on an explicit ``True``: ``None`` and ``False`` carry
        no intent to double-quantize, so a non-4bit config is not rejected — and
        because unset serializes as ``None`` (not ``True``), a dumped-and-reloaded
        config no longer trips this, unlike the old ``model_fields_set`` gate.
        """
        tcfg = self.training
        if tcfg.bnb_4bit_use_double_quant is not True:
            return self
        if tcfg.quantization != "4bit":
            raise ValueError(
                "training.bnb_4bit_use_double_quant=true requires "
                f"training.quantization='4bit'; got "
                f"quantization={tcfg.quantization!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_llm_int8_alias(self) -> "SoupConfig":
        """v0.53.0 Part E — ``llm_int8=True`` requires ``quantization='8bit'``.

        Unlike v0.41.0 ``load_in_8bit`` (which rewrites quantization), the
        ``llm_int8`` flag is a pure assertion: the user explicitly says
        "this is an LLM.int8 run" and we enforce the matching quantization
        rather than silently rewriting it.
        """
        tcfg = self.training
        if not tcfg.llm_int8:
            return self
        if tcfg.quantization != "8bit":
            raise ValueError(
                "training.llm_int8=true requires training.quantization='8bit'; "
                f"got quantization={tcfg.quantization!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_quantize_ref_reward(self) -> "SoupConfig":
        """v0.53.0 Part E — ``quantize_ref_model`` requires a ref-model task
        and ``quantize_reward_model`` requires a reward-model task.
        Silent-no-op footgun rejection.

        Ref-model tasks (review fix): all preference-family trainers PLUS
        ``grpo`` (KL to ref policy) and ``kto`` (unpaired preference, also
        keeps a frozen ref). ``ppo`` also has a ref but uses a separately
        named ``policy_ref`` checkpoint; covered by the reward path too.
        """
        tcfg = self.training
        ref_tasks = {
            "dpo", "ipo", "simpo", "orpo", "bco", "kto",
            "preference", "grpo", "ppo",
        }
        reward_tasks = {"ppo", "reward_model"}
        if tcfg.quantize_ref_model and self.task not in ref_tasks:
            raise ValueError(
                "training.quantize_ref_model=true requires a task with a "
                "reference model "
                f"(one of {sorted(ref_tasks)}); got task={self.task!r}"
            )
        if tcfg.quantize_reward_model and self.task not in reward_tasks:
            raise ValueError(
                "training.quantize_reward_model=true requires task in "
                f"{sorted(reward_tasks)}; got task={self.task!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_kv_cache_type_supported(self) -> "SoupConfig":
        """v0.53.0 Part C — ``kv_cache_type`` schema gate.

        Currently only ``fp8`` is gated (Hopper-only — MLX rejected). The
        three remaining types (``q8_0`` / ``bf16`` / ``f16``) pass through
        for every backend; v0.53.1 live wiring MAY need to narrow this
        further (e.g. MLX serve may not support ``q8_0``). The schema-only
        permissive policy is deliberate this release — kept here so the
        v0.53.1 contributor sees the gate site immediately.

        Hopper SM-capability check (compute_cap >= 9.0) is runtime-only.
        """
        kv = self.training.kv_cache_type
        if kv is None:
            return self
        if self.backend == "mlx" and kv == "fp8":
            raise ValueError(
                "training.kv_cache_type='fp8' is not supported on the mlx "
                "backend (Hopper-only). Use kv_cache_type in {q8_0,bf16,f16} "
                "or switch backend."
            )
        return self

    @model_validator(mode="after")
    def _validate_relora_supported_tasks(self) -> "SoupConfig":
        """v0.40.6 (#67) — ReLoRA callback wired in every transformer-backend
        trainer (sft / dpo / grpo / kto / orpo / simpo / ipo / ppo /
        reward_model / pretrain / embedding / bco).

        MLX backend still rejected: the callback is HF Trainer-specific.
        """
        if self.training.relora_steps is None:
            return self
        if self.backend == "mlx":
            raise ValueError(
                "relora_steps is not supported on the mlx backend "
                "(callback is HF Trainer-specific). "
                "Use backend='transformers' or remove relora_steps."
            )
        return self

    @model_validator(mode="after")
    def _validate_quant_menu_supported_tasks(self) -> "SoupConfig":
        """v0.40.5 (#66) — Quant Menu (gptq/awq/hqq:Nbit/aqlm/eetq/mxfp4/fp8)
        is wired across every transformer-backend trainer (sft / dpo / grpo /
        kto / orpo / simpo / ipo / ppo / reward_model / pretrain / embedding /
        bco). MLX backend still rejected (no equivalent kernels).

        v0.71.19 (#81) — vision / audio modality wiring landed: the SFT
        ``_setup_vision_transformers`` / ``_setup_audio_transformers`` paths now
        thread the unified ``build_quantization_config_for_loader``, so the
        modality gate is dropped (only the mlx-backend gate remains).
        """
        from soup_cli.utils.quant_menu import is_quant_menu_format

        quant = self.training.quantization
        # bnb 4bit / 8bit / none always apply universally — pre-existing.
        if not is_quant_menu_format(quant):
            return self
        if self.backend == "mlx":
            raise ValueError(
                f"quantization={quant!r} is not supported on the mlx backend "
                "(no equivalent kernels). Use backend='transformers' or "
                "switch to quantization in {'4bit', '8bit', 'none'}."
            )
        return self

    @model_validator(mode="after")
    def _validate_preference_dispatcher(self) -> "SoupConfig":
        """v0.40.0 Part B — task='preference' requires preference_loss OR
        preference_loss_weights (Part D). Setting either field outside
        task='preference' is rejected to keep the two config surfaces disjoint.
        """
        loss = self.training.preference_loss
        weights = self.training.preference_loss_weights
        if self.task == "preference":
            if loss is None and weights is None:
                raise ValueError(
                    "task='preference' requires either training.preference_loss "
                    "(in {dpo, simpo, orpo, ipo, bco}) or "
                    "training.preference_loss_weights (multi-objective dict)."
                )
            return self
        if loss is not None:
            raise ValueError(
                f"training.preference_loss={loss!r} is only meaningful for "
                f"task='preference'; got task={self.task!r}. Either set "
                "task='preference' or remove preference_loss."
            )
        if weights is not None:
            raise ValueError(
                "training.preference_loss_weights is only meaningful for "
                f"task='preference'; got task={self.task!r}. Either set "
                "task='preference' or remove preference_loss_weights."
            )
        return self

    @model_validator(mode="after")
    def _validate_dpo_variants_supported_tasks(self) -> "SoupConfig":
        """v0.40.0 Part C — β-schedule + ref-model regen are DPO-family only.

        Allowed: task in {dpo, ipo} OR (task='preference' AND
        preference_loss in {dpo, ipo}). Rejected on mlx backend.
        """
        tcfg = self.training
        sched = tcfg.dpo_beta_schedule
        end = tcfg.dpo_beta_end
        regen = tcfg.dpo_ref_regen_epochs
        if sched is None and end is None and regen is None:
            return self
        # End/schedule mutual requirement.
        if sched is not None and end is None:
            raise ValueError(
                "dpo_beta_schedule requires dpo_beta_end (the target β at "
                "end of training). Set dpo_beta_end or remove dpo_beta_schedule."
            )
        if end is not None and sched is None:
            raise ValueError(
                "dpo_beta_end requires dpo_beta_schedule. Set "
                "dpo_beta_schedule in {linear, cosine, exponential} or "
                "remove dpo_beta_end."
            )
        # Backend gate.
        if self.backend == "mlx":
            raise ValueError(
                "DPO variants (dpo_beta_schedule / dpo_ref_regen_epochs) are "
                "not supported on the mlx backend in v0.40.0 (TRL trainer "
                "internals required). Use backend='transformers'."
            )
        # Task gate — DPO family only.
        family_ok = self.task in ("dpo", "ipo") or (
            self.task == "preference"
            and tcfg.preference_loss in ("dpo", "ipo")
        )
        if not family_ok:
            raise ValueError(
                f"DPO variants (dpo_beta_schedule / dpo_ref_regen_epochs) "
                f"require task in {{dpo, ipo}} or task='preference' with "
                f"preference_loss in {{dpo, ipo}}; got task={self.task!r}, "
                f"preference_loss={tcfg.preference_loss!r}."
            )
        return self

    @model_validator(mode="after")
    def _validate_preference_loss_weights(self) -> "SoupConfig":
        """v0.40.0 Part D — multi-objective preference_loss_weights gate.

        Allowed: task='preference' only. Mutually exclusive with the scalar
        preference_loss. Validates value bounds + sum-to-1 + key allowlist.
        """
        tcfg = self.training
        weights = tcfg.preference_loss_weights
        if weights is None:
            return self
        if not isinstance(weights, dict):
            raise ValueError(
                "preference_loss_weights must be a dict, e.g. "
                "{'dpo': 0.7, 'bco': 0.3}."
            )
        if self.task != "preference":
            raise ValueError(
                "preference_loss_weights requires task='preference'; got "
                f"task={self.task!r}."
            )
        if tcfg.preference_loss is not None:
            raise ValueError(
                "preference_loss_weights and (scalar) preference_loss are "
                "mutually exclusive — pick one."
            )
        if self.backend == "mlx":
            raise ValueError(
                "preference_loss_weights is not supported on the mlx backend "
                "in v0.40.0. Use backend='transformers'."
            )
        if not (2 <= len(weights) <= 5):
            raise ValueError(
                f"preference_loss_weights must have between 2 and 5 entries "
                "(single-entry blends are equivalent to the scalar "
                "preference_loss field; use that instead); got "
                f"{len(weights)}."
            )
        allowed = {"dpo", "simpo", "orpo", "ipo", "bco"}
        for key in weights:
            if not isinstance(key, str):
                raise ValueError(
                    f"preference_loss_weights keys must be strings; "
                    f"got {type(key).__name__}."
                )
            if "\x00" in key:
                raise ValueError(
                    "preference_loss_weights keys cannot contain null bytes."
                )
        unknown = set(weights.keys()) - allowed
        if unknown:
            raise ValueError(
                f"preference_loss_weights keys must be in {sorted(allowed)}; "
                f"unknown: {sorted(unknown)}."
            )
        for key, value in weights.items():
            if isinstance(value, bool) or not isinstance(value, (int, float)):
                raise ValueError(
                    f"preference_loss_weights[{key!r}] must be a number; "
                    f"got {type(value).__name__}."
                )
            if not (0 < float(value) <= 1):
                raise ValueError(
                    f"preference_loss_weights[{key!r}]={value!r} must be in (0, 1]."
                )
        total = sum(float(v) for v in weights.values())
        if abs(total - 1.0) > 1e-6:
            raise ValueError(
                f"preference_loss_weights must sum to 1.0 (±1e-6); got {total!r}."
            )
        return self

    @model_validator(mode="after")
    def _validate_unlearn_compat(self) -> "SoupConfig":
        """v0.61.0 Part A — ``task='unlearn'`` cross-validator.

        Enforces:
        - ``unlearn_method`` is set when ``task='unlearn'``.
        - ``unlearn_method`` is rejected on any other task (silent no-op
          footgun — mirrors v0.52.0 distill / classifier task-gate).
        - ``data.forget_set`` is present when ``task='unlearn'``.
        - Backend != mlx (live wiring deferred to v0.61.1).
        """
        tcfg = self.training
        method = tcfg.unlearn_method

        # method-set-outside-unlearn rejection (silent-no-op footgun).
        if method is not None and self.task != "unlearn":
            raise ValueError(
                f"training.unlearn_method={method!r} requires task='unlearn'; "
                f"got task={self.task!r}. Remove unlearn_method or set "
                f"task='unlearn'."
            )

        # unlearn_alpha-without-method rejection.
        if tcfg.unlearn_alpha is not None and method is None:
            raise ValueError(
                "training.unlearn_alpha requires training.unlearn_method "
                "to be set."
            )

        if self.task != "unlearn":
            return self

        # task='unlearn' requires the method.
        if method is None:
            raise ValueError(
                "task='unlearn' requires training.unlearn_method in "
                "{npo, simnpo, rmu}."
            )

        # task='unlearn' requires the forget_set.
        if not self.data.forget_set:
            raise ValueError(
                "task='unlearn' requires data.forget_set (path or HF "
                "dataset id pointing at rows to unlearn)."
            )

        # Delegate backend gate to the pure helper so the runtime path
        # and schema-load path stay consistent.
        from soup_cli.utils.unlearning import validate_unlearn_compat

        try:
            validate_unlearn_compat(task=self.task, backend=self.backend)
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_grace_codebook_compat(self) -> "SoupConfig":
        """v0.62.0 Part E — GRACE codebook cross-validator.

        Rules:
        * ``grace_codebook=True`` requires BOTH ``grace_codebook_size`` and
          ``grace_codebook_dim`` to be set (no codebook can be allocated
          without both knobs).
        * Setting ``grace_codebook_size`` / ``grace_codebook_dim`` without
          ``grace_codebook=True`` is a silent-no-op footgun — rejected.
        """
        tcfg = self.training
        flag = tcfg.grace_codebook
        size = tcfg.grace_codebook_size
        dim = tcfg.grace_codebook_dim

        if not flag and (size is not None or dim is not None):
            raise ValueError(
                "training.grace_codebook_size / grace_codebook_dim require "
                "training.grace_codebook=true."
            )
        if flag and (size is None or dim is None):
            raise ValueError(
                "training.grace_codebook=true requires BOTH "
                "training.grace_codebook_size and training.grace_codebook_dim."
            )
        return self

    @model_validator(mode="after")
    def _validate_citation_faithful_compat(self) -> "SoupConfig":
        """v0.62.0 Part D — citation-faithful FT cross-validator.

        Rules:
        * ``citation_faithful=True`` requires ``data.format='raft'`` (the
          RAFT row carries the doc references; other formats can't supply
          ground-truth citation IDs).
        * ``citation_faithful=True`` requires ``task in {sft, pretrain}``
          (the span-mask runtime that v0.62.1 will ship only makes sense
          for the SFT family; mirrors v0.52.0 distill / classifier
          task-gate policy — review M3 fix).
        * ``citation_style`` set without ``citation_faithful=True`` is a
          silent-no-op footgun — rejected (mirrors v0.61.0 unlearn_alpha /
          v0.62.0 Part B ra_dit_retriever_model policy).
        * Same rejection for ``citation_recall_threshold`` without the flag.
        """
        tcfg = self.training

        if tcfg.citation_style is not None and not tcfg.citation_faithful:
            raise ValueError(
                "training.citation_style requires "
                "training.citation_faithful=true."
            )
        if (
            tcfg.citation_recall_threshold is not None
            and not tcfg.citation_faithful
        ):
            raise ValueError(
                "training.citation_recall_threshold requires "
                "training.citation_faithful=true."
            )
        if tcfg.citation_faithful:
            if self.data.format != "raft":
                raise ValueError(
                    "training.citation_faithful=true requires "
                    f"data.format='raft'; got data.format={self.data.format!r}. "
                    "Citation-faithful FT pairs with the v0.62.0 Part A "
                    "RAFT data format (which carries the doc references)."
                )
            if self.task not in ("sft", "pretrain"):
                raise ValueError(
                    "training.citation_faithful=true requires "
                    f"task in {{sft, pretrain}}; got task={self.task!r}. "
                    "Citation-faithful FT is an SFT-family feature; the "
                    "live span-mask runtime ships in v0.62.1."
                )
        return self

    @model_validator(mode="after")
    def _validate_ra_dit_compat(self) -> "SoupConfig":
        """v0.62.0 Part B — RA-DIT stage / task pairing.

        Each stage requires the matching base task:

        * ``retriever`` -> ``task='embedding'``
        * ``generator`` -> ``task='sft'``

        Also rejects ``ra_dit_retriever_model`` set without ``ra_dit_stage``
        (silent no-op footgun — mirrors v0.61.0 ``unlearn_alpha`` policy).
        """
        tcfg = self.training
        stage = tcfg.ra_dit_stage

        if tcfg.ra_dit_retriever_model is not None and stage is None:
            raise ValueError(
                "training.ra_dit_retriever_model requires "
                "training.ra_dit_stage to be set ('retriever' or 'generator')."
            )

        if stage is None:
            return self

        from soup_cli.utils.ra_dit import validate_ra_dit_compat

        try:
            validate_ra_dit_compat(stage=stage, task=self.task)
        except ValueError as exc:
            raise ValueError(str(exc)) from exc
        return self

    @model_validator(mode="after")
    def _validate_mole_routing_compat(self) -> "SoupConfig":
        """v0.67.0 Part C / v0.71.12 #222 — MoLE per-token routing gate.

        Rules:
        * MoLE fields (``mole_task_adapters`` / ``mole_top_k`` /
          ``mole_temperature``) set outside ``task='moe_lora_routing'`` are
          rejected — silent-no-op footgun (mirrors v0.52.0 distill / v0.62.0
          citation task-gates).
        * ``task='moe_lora_routing'`` rejects ``backend='mlx'`` (the live gate
          needs torch dispatch).
        * ``task='moe_lora_routing'`` requires ``mole_task_adapters`` (2-64
          deduplicated paths — validated by the field validator).
        * ``mole_top_k`` must not exceed ``len(mole_task_adapters)``.
        """
        tcfg = self.training
        mole_fields_set = (
            tcfg.mole_task_adapters is not None
            or tcfg.mole_top_k is not None
            or tcfg.mole_temperature is not None
        )

        if self.task != "moe_lora_routing":
            if mole_fields_set:
                offenders = [
                    name
                    for name, val in (
                        ("mole_task_adapters", tcfg.mole_task_adapters),
                        ("mole_top_k", tcfg.mole_top_k),
                        ("mole_temperature", tcfg.mole_temperature),
                    )
                    if val is not None
                ]
                raise ValueError(
                    f"MoLE field(s) {offenders} require task='moe_lora_routing' "
                    f"(got task={self.task!r})."
                )
            return self

        if self.backend == "mlx":
            raise ValueError(
                "MoLE routing (task='moe_lora_routing') is not supported on "
                "the mlx backend (the gating kernel needs torch dispatch)."
            )
        if tcfg.mole_task_adapters is None:
            raise ValueError(
                "task='moe_lora_routing' requires training.mole_task_adapters "
                "(2-64 pre-trained task-LoRA paths to route over)."
            )
        if (
            tcfg.mole_top_k is not None
            and tcfg.mole_top_k > len(tcfg.mole_task_adapters)
        ):
            raise ValueError(
                f"mole_top_k={tcfg.mole_top_k} exceeds "
                f"len(mole_task_adapters)={len(tcfg.mole_task_adapters)}."
            )
        return self

    @model_validator(mode="after")
    def _validate_mlx_task_support(self) -> "SoupConfig":
        """MLX backend only supports sft, dpo, and grpo tasks (v0.25.0).

        DPO and GRPO wrappers are scaffolding in v0.25.0 — they raise
        NotImplementedError at ``train()`` time because upstream mlx-lm has
        not yet shipped DPO/GRPO training helpers. Users who pick them will
        instead see this friendly error at config-load time.
        """
        if self.backend != "mlx":
            return self
        if self.task == "sft":
            return self
        raise ValueError(
            f"MLX backend only ships SFT in v0.25.0; task='{self.task}' "
            "is not yet implemented (upstream mlx-lm does not expose a "
            f"training helper). Use backend=transformers for task={self.task}."
        )

    @model_validator(mode="after")
    def _validate_uld_compat(self) -> "SoupConfig":
        """v0.70.0 Part B — Universal Logit Distillation gate.

        ``uld_strategy`` is only meaningful when ``task='distill'`` —
        cross-tokenizer distillation has no analogue outside the
        distillation trainer. Rejected on other tasks with a friendly
        message, and on MLX backend with a distinct message.

        Composes with v0.52 distillation task: when set, the
        :class:`uld.ULDConfig` validation fires (top_k cross-validation,
        vocab-size bounds) at config-load.
        """
        tcfg = self.training
        strategy = tcfg.uld_strategy
        top_k = tcfg.uld_top_k
        if strategy is None and top_k is None:
            return self
        if strategy is None:
            # top_k without strategy is a silent no-op footgun.
            raise ValueError(
                "uld_top_k requires uld_strategy to be set"
            )
        if self.task != "distill":
            raise ValueError(
                "uld_strategy / uld_top_k are only valid when "
                f"task='distill'; got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "uld_strategy is not supported on backend=mlx in v0.70.0 "
                "(cross-tokenizer distillation is transformers-only)"
            )
        # Cross-check: topk_align requires top_k.
        if strategy == "topk_align" and top_k is None:
            raise ValueError(
                "uld_strategy='topk_align' requires uld_top_k to be set"
            )
        if strategy != "topk_align" and top_k is not None:
            raise ValueError(
                "uld_top_k is only valid when uld_strategy='topk_align'; "
                f"got uld_strategy={strategy!r}"
            )
        return self

    @model_validator(mode="after")
    def _validate_echo_trap_compat(self) -> "SoupConfig":
        """v0.70.0 Part F — echo-trap detector task gate.

        ``echo_trap_enabled`` (and ``echo_trap_halt``) only meaningful
        on RL tasks (grpo / ppo). Setting ``echo_trap_halt`` without
        ``echo_trap_enabled`` is a silent no-op footgun — reject.
        """
        tcfg = self.training
        if (
            not tcfg.echo_trap_enabled
            and not tcfg.echo_trap_halt
            and not tcfg.echo_trap_tokenizer_aware
        ):
            return self
        if not tcfg.echo_trap_enabled and (
            tcfg.echo_trap_halt or tcfg.echo_trap_tokenizer_aware
        ):
            raise ValueError(
                "echo_trap_halt / echo_trap_tokenizer_aware require "
                "echo_trap_enabled=True"
            )
        if self.task not in ("grpo", "ppo"):
            raise ValueError(
                "echo_trap_enabled / echo_trap_halt / "
                "echo_trap_tokenizer_aware are only valid on "
                f"task in {{'grpo', 'ppo'}}; got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "echo_trap_enabled is not supported on backend=mlx in "
                "v0.70.0"
            )
        return self

    @model_validator(mode="after")
    def _validate_rl_checkpoint_compat(self) -> "SoupConfig":
        """v0.70.0 Part D — mid-epoch RL checkpoint task gate.

        ``rl_checkpoint_save_every_steps`` is only meaningful on RL
        tasks (grpo / ppo). Non-RL tasks already have HF Trainer's
        per-epoch checkpointing. Rejected on other tasks with a
        friendly message.
        """
        tcfg = self.training
        if tcfg.rl_checkpoint_save_every_steps is None:
            return self
        if self.task not in ("grpo", "ppo"):
            raise ValueError(
                "rl_checkpoint_save_every_steps is only valid on RL tasks "
                f"(grpo / ppo); got task={self.task!r}. Non-RL tasks use "
                "HF Trainer's per-epoch checkpointing already."
            )
        if self.backend == "mlx":
            raise ValueError(
                "rl_checkpoint_save_every_steps is not supported on "
                "backend=mlx in v0.70.0"
            )
        return self

    @model_validator(mode="after")
    def _validate_minillm_compat(self) -> "SoupConfig":
        """v0.70.0 Part C — MiniLLM compatibility gate.

        ``minillm_enabled`` requires ``task='distill'`` on a non-mlx
        backend. Setting any minillm_* tunable without
        ``minillm_enabled=True`` is rejected (silent no-op footgun
        mirroring v0.52 distill / v0.62 grace_codebook policy).
        """
        tcfg = self.training
        any_field_set = (
            tcfg.minillm_teacher_mix_ratio != 0.0
            or tcfg.minillm_length_normalize is not True
            or tcfg.minillm_pretrain_anchor_weight != 0.0
            or tcfg.minillm_pretrain_anchor_path is not None
            or tcfg.minillm_on_policy is True
            or tcfg.minillm_rollout_length is not None
        )
        if not tcfg.minillm_enabled and not any_field_set:
            return self
        if not tcfg.minillm_enabled and any_field_set:
            offenders = []
            if tcfg.minillm_teacher_mix_ratio != 0.0:
                offenders.append("minillm_teacher_mix_ratio")
            if tcfg.minillm_length_normalize is not True:
                offenders.append("minillm_length_normalize")
            if tcfg.minillm_pretrain_anchor_weight != 0.0:
                offenders.append("minillm_pretrain_anchor_weight")
            if tcfg.minillm_pretrain_anchor_path is not None:
                offenders.append("minillm_pretrain_anchor_path")
            if tcfg.minillm_on_policy is True:
                offenders.append("minillm_on_policy")
            if tcfg.minillm_rollout_length is not None:
                offenders.append("minillm_rollout_length")
            raise ValueError(
                f"MiniLLM tunables {offenders} require minillm_enabled=True"
            )
        if self.task != "distill":
            raise ValueError(
                "minillm_enabled requires task='distill'; "
                f"got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "minillm_enabled is not supported on backend=mlx in v0.70.0"
            )
        # Cross-check: anchor weight + path mutual requirements.
        if (
            tcfg.minillm_pretrain_anchor_weight > 0.0
            and tcfg.minillm_pretrain_anchor_path is None
        ):
            raise ValueError(
                "minillm_pretrain_anchor_weight > 0 requires "
                "minillm_pretrain_anchor_path to be set"
            )
        if (
            tcfg.minillm_pretrain_anchor_weight == 0.0
            and tcfg.minillm_pretrain_anchor_path is not None
        ):
            raise ValueError(
                "minillm_pretrain_anchor_path is set but "
                "minillm_pretrain_anchor_weight is 0 (silent no-op)"
            )
        # v0.71.18 #257 — rollout_length only applies to the on-policy path.
        if tcfg.minillm_rollout_length is not None and not tcfg.minillm_on_policy:
            raise ValueError(
                "minillm_rollout_length requires minillm_on_policy=True "
                "(unused by the offline distribution blend)"
            )
        return self

    @model_validator(mode="after")
    def _validate_reward_hack_compat(self) -> "SoupConfig":
        """v0.70.0 Part A — reward-hacking detector task / backend gate.

        ``reward_hack_detector`` + ``reward_hack_halt`` are only meaningful
        for RL tasks (grpo / ppo). Rejected outside those tasks with a
        friendly message that names the offending fields. MLX backend
        rejected with a distinct message (matches v0.34.0 / v0.50.0
        review-fix policy of distinct error reasons).
        """
        tcfg = self.training
        detector = tcfg.reward_hack_detector
        halt = tcfg.reward_hack_halt
        mitigation = getattr(tcfg, "reward_hack_mitigation", "off")
        # v0.71.26 — footgun: control tunables set while mitigation is off.
        if mitigation == "off":
            offenders = _customized_reward_hack_tunables(tcfg)
            if offenders:
                raise ValueError(
                    f"reward-hack tunables {offenders} require "
                    "reward_hack_mitigation to be set (not 'off')"
                )
        if detector is None and not halt and mitigation == "off":
            return self
        # halt without detector is a silent no-op footgun — reject.
        if detector is None and halt:
            raise ValueError(
                "reward_hack_halt=True requires reward_hack_detector to be set"
            )
        # v0.71.26 — a non-'off' mitigation mode needs the detector as its
        # signal source; setting it without a detector is a silent no-op.
        if mitigation != "off" and detector is None:
            raise ValueError(
                f"reward_hack_mitigation={mitigation!r} requires "
                "reward_hack_detector to be set (the signal source)"
            )
        # v0.71.26 — the task / backend gate runs BEFORE the controller-config
        # checks so a task mismatch surfaces the actionable error (not a
        # numeric-bounds error) — python-review HIGH #3.
        if self.task not in ("grpo", "ppo"):
            raise ValueError(
                "reward_hack_detector / reward_hack_halt / "
                "reward_hack_mitigation are only valid on "
                f"task in {{'grpo', 'ppo'}}; got task={self.task!r}"
            )
        if self.backend == "mlx":
            raise ValueError(
                "reward_hack_detector / reward_hack_mitigation are not "
                "supported on backend=mlx (RL detectors are transformers-only)"
            )
        # Controller config (numeric bounds, signal allowlist, β-schedule
        # mutual exclusion) only when a mode is active.
        if mitigation != "off":
            _validate_reward_hack_controller(tcfg)
        return self


# --- Built-in templates ---

# DEPRECATED (v0.39.0 Part E) — these inline templates are kept for back-compat.
# The canonical source is `soup_cli/templates/*.yaml` with `manifest.json`.
# Both sources are asserted equal in tests/test_templates_yaml.py — when editing
# a template, update both. Planned removal: v0.41.0+ once external consumers
# have migrated to the YAML registry.
TEMPLATES: dict[str, str] = {
    "chat": """# Soup template: Chat Assistant
# Fine-tune a model for conversational chat

base: meta-llama/Llama-3.1-8B-Instruct
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/train.jsonl
  format: alpaca
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 2e-5
  batch_size: auto
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit

output: ./output
""",
    "code": """# Soup template: Code Model
# Fine-tune a model for code generation / completion

base: codellama/CodeLlama-7b-Instruct-hf
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/code_train.jsonl
  format: alpaca
  val_split: 0.1
  max_length: 4096

training:
  epochs: 2
  lr: 1e-5
  batch_size: auto
  lora:
    r: 128
    alpha: 32
    target_modules: auto
  quantization: 4bit

output: ./output
""",
    "reasoning": """# Soup template: Reasoning / GRPO
# Fine-tune a model for chain-of-thought reasoning with GRPO

base: meta-llama/Llama-3.1-8B-Instruct
task: grpo
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/reasoning_train.jsonl
  format: sharegpt
  val_split: 0.1
  max_length: 4096

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 8
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  grpo_beta: 0.1
  num_generations: 4
  reward_fn: accuracy

output: ./output
""",
    "vision": """# Soup template: Vision / Multimodal
# Fine-tune a vision-language model for image understanding

base: meta-llama/Llama-3.2-11B-Vision-Instruct
task: sft
modality: vision
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/vision_train.jsonl
  format: llava
  image_dir: ./data/images
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit

output: ./output
""",
    "medical": """# Soup template: Medical / Domain Expert
# Fine-tune a model with domain-specific knowledge

base: meta-llama/Llama-3.1-8B-Instruct
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/medical_train.jsonl
  format: alpaca
  val_split: 0.15
  max_length: 2048

training:
  epochs: 5
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 8
  lora:
    r: 128
    alpha: 32
    target_modules: auto
  quantization: 4bit

output: ./output
""",
    "kto": """# Soup template: KTO (Kahneman-Tversky Optimization)
# Align a model using unpaired preference data (no need for chosen+rejected pairs)
#
# Data format (JSONL):
#   {"prompt": "What is 2+2?", "completion": "4", "label": true}
#   {"prompt": "What is 2+2?", "completion": "Fish", "label": false}

base: meta-llama/Llama-3.1-8B-Instruct
task: kto
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/kto_train.jsonl
  format: kto
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  kto_beta: 0.1

output: ./output
""",
    "orpo": """# Soup template: ORPO (Odds Ratio Preference Optimization)
# Align a model without a reference model — simpler than DPO
#
# Data format (JSONL):
#   {"prompt": "What is 2+2?", "chosen": "4", "rejected": "Fish"}

base: meta-llama/Llama-3.1-8B-Instruct
task: orpo
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/preference_train.jsonl
  format: dpo
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  orpo_beta: 0.1

output: ./output
""",
    "bco": """# Soup template: BCO (Binary Classifier Optimization)
# Preference alignment via binary classification of chosen vs rejected.
#
# Data format (JSONL) — same as DPO:
#   {"prompt": "What is 2+2?", "chosen": "4", "rejected": "Fish"}

base: meta-llama/Llama-3.1-8B-Instruct
task: bco
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/preference_train.jsonl
  format: dpo
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  bco_beta: 0.1

output: ./output
""",
    "simpo": """# Soup template: SimPO (Simple Preference Optimization)
# Reference-free preference alignment with length-normalized rewards
#
# Data format (JSONL):
#   {"prompt": "What is 2+2?", "chosen": "4", "rejected": "Fish"}

base: meta-llama/Llama-3.1-8B-Instruct
task: simpo
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/preference_train.jsonl
  format: dpo
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  simpo_gamma: 0.5
  cpo_alpha: 1.0

output: ./output
""",
    "ipo": """# Soup template: IPO (Identity Preference Optimization)
# A theoretically grounded variant of DPO with stronger regularization
#
# Data format (JSONL):
#   {"prompt": "What is 2+2?", "chosen": "4", "rejected": "Fish"}

base: meta-llama/Llama-3.1-8B-Instruct
task: ipo
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/preference_train.jsonl
  format: dpo
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  ipo_tau: 0.1

output: ./output
""",
    "pretrain": """# Soup template: Continued Pre-training
# Continue pre-training a model on raw text data (domain adaptation)
#
# Data format (JSONL):
#   {"text": "Your raw text document here..."}
#
# Or plain .txt files (one document per line or entire file as one document).

base: meta-llama/Llama-3.1-8B
task: pretrain
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/corpus.jsonl
  format: plaintext
  val_split: 0.05
  max_length: 4096

training:
  epochs: 1
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 8
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit

output: ./output_pretrain
""",
    "moe": """# Soup template: MoE (Mixture of Experts) Fine-tuning
# Fine-tune a Mixture of Experts model with ScatterMoE LoRA
#
# Supported MoE models: Qwen3-30B-A3B, Mixtral-8x7B, DeepSeek-V3, etc.

base: Qwen/Qwen3-30B-A3B
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/train.jsonl
  format: alpaca
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 8
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  moe_lora: true
  moe_aux_loss_coeff: 0.01

output: ./output
""",
    "longcontext": """# Soup template: Long-Context Fine-tuning (128k+)
# Extend model context window for long-document understanding
#
# Uses RoPE scaling + gradient checkpointing + FlashAttention for 128k tokens.
# Optionally enable Liger Kernel for additional memory savings.

base: meta-llama/Llama-3.1-8B-Instruct
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/long_context_train.jsonl
  format: alpaca
  val_split: 0.05
  max_length: 131072

training:
  epochs: 1
  lr: 5e-6
  batch_size: 1
  gradient_accumulation_steps: 16
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  gradient_checkpointing: true
  rope_scaling_type: dynamic
  use_flash_attn: true
  # use_liger: true       # pip install "soup-cli[liger]" for fused ops
  # use_ring_attention: true  # Multi-GPU sequence parallelism

output: ./output_longctx
""",
    "embedding": """# Soup template: Embedding Model Fine-tuning
# Fine-tune a sentence embedding model (BGE, E5, GTE, etc.)
#
# Data format (JSONL) — contrastive pairs:
#   {"anchor": "What is Python?", "positive": "Python is a programming language."}
#
# Data format (JSONL) — triplets:
#   {"anchor": "query", "positive": "relevant doc", "negative": "unrelated doc"}

base: BAAI/bge-base-en-v1.5
task: embedding
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/embedding_train.jsonl
  format: embedding
  val_split: 0.1
  max_length: 512

training:
  epochs: 3
  lr: 2e-5
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: none
  embedding_loss: contrastive
  embedding_margin: 0.5
  embedding_pooling: mean

output: ./output_embedding
""",
    "audio": """# Soup template: Audio / Speech
# Fine-tune an audio-language model for speech understanding
#
# Supported models: Qwen2-Audio, Whisper (via transformers)
#
# Data format (JSONL):
#   {"audio": "path/to/audio.wav", "messages": [
#     {"role": "user", "content": "Transcribe."},
#     {"role": "assistant", "content": "Hello world."}]}

base: Qwen/Qwen2-Audio-7B-Instruct
task: sft
modality: audio
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/audio_train.jsonl
  format: audio
  audio_dir: ./data/audio
  val_split: 0.1
  max_length: 2048

training:
  epochs: 3
  lr: 1e-5
  batch_size: auto
  gradient_accumulation_steps: 8
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit

output: ./output_audio
""",
    "tool-calling": """# Soup template: Tool-Calling / Agentic Fine-tuning
# Fine-tune a model to call tools / functions correctly
#
# Data format (JSONL):
#   {
#     "messages": [{"role": "user", "content": "What's the weather in Tokyo?"}],
#     "tools": [{"type": "function", "function": {
#       "name": "get_weather",
#       "description": "Get current weather for a city",
#       "parameters": {"type": "object", "properties": {"city": {"type": "string"}}}
#     }}],
#     "tool_calls": [{"function": {
#       "name": "get_weather",
#       "arguments": "{\\"city\\": \\"Tokyo\\"}"
#     }}]
#   }

base: meta-llama/Llama-3.1-8B-Instruct
task: sft
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/tool_calling_train.jsonl
  format: tool-calling
  val_split: 0.1
  max_length: 4096

training:
  epochs: 3
  lr: 2e-4
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 16
    alpha: 32
    target_modules: auto
  quantization: 4bit

output: ./output
""",
    "rlhf": """# Soup template: Full RLHF Pipeline (SFT + Reward Model + PPO)
# Three-stage training: 1) SFT warmup, 2) Reward model, 3) PPO alignment
#
# Usage:
#   Step 1: soup train --config soup_sft.yaml       # SFT warmup
#   Step 2: soup train --config soup_rm.yaml         # Train reward model
#   Step 3: soup train --config soup_ppo.yaml        # PPO with reward model
#
# This template generates the PPO config (step 3).
# For steps 1-2, use: soup init --template chat (SFT) and edit task to reward_model.

base: meta-llama/Llama-3.1-8B-Instruct
task: ppo
# backend: unsloth  # 2-5x faster, pip install "soup-cli[fast]"

data:
  train: ./data/prompts.jsonl
  format: chatml
  val_split: 0.1
  max_length: 2048

training:
  epochs: 1
  lr: 1e-6
  batch_size: auto
  gradient_accumulation_steps: 4
  lora:
    r: 64
    alpha: 16
    target_modules: auto
  quantization: 4bit
  reward_model: ./output_rm
  ppo_epochs: 4
  ppo_clip_ratio: 0.2
  ppo_kl_penalty: 0.05

output: ./output_ppo
""",
}