export.py

"""soup export — convert a model to GGUF format for Ollama / llama.cpp."""

import json
import shutil
import subprocess
import sys
from pathlib import Path
from typing import Optional

import typer
from rich.console import Console
from rich.markup import escape
from rich.panel import Panel

console = Console()

SUPPORTED_FORMATS = (
    "gguf", "onnx", "tensorrt", "awq", "gptq",
    # v0.52.0 Part D — BitNet 1.58-bit + TQ1_0 GGUF.
    # Schema-only stubs in v0.52.0; live conversion lands in v0.52.1.
    "bitnet", "tq1_0",
    # v0.53.1 #142 — TorchAO PTQ live wiring (Int4WeightOnly / Int8DynActInt4 /
    # Float8DynActFloat8 / NVFP4). Requires --quant-config <yaml>.
    "torchao",
    # v0.53.1 #139 — UD/IQ/Apple-ARM GGUFs via llama.cpp imatrix.
    "gguf-ud",
)
GGUF_QUANT_TYPES = ("q4_0", "q4_k_m", "q5_k_m", "q8_0", "f16", "f32")
LLAMA_CPP_DIR_NAME = "llama.cpp"
# Pin to a known release tag for supply-chain safety
LLAMA_CPP_TAG = "b5270"


def export(
    model: str = typer.Option(
        ...,
        "--model",
        "-m",
        help="Path to model directory (full model or LoRA adapter)",
    ),
    fmt: str = typer.Option(
        "gguf",
        "--format",
        "-f",
        help="Export format: gguf, onnx, tensorrt, awq, gptq, bitnet, tq1_0",
    ),
    quant: str = typer.Option(
        "q4_k_m",
        "--quant",
        "-q",
        help="Quantization type: q4_0, q4_k_m, q5_k_m, q8_0, f16, f32",
    ),
    output: Optional[str] = typer.Option(
        None,
        "--output",
        "-o",
        help="Output file path. Default: <model-name>.<quant>.gguf",
    ),
    base: Optional[str] = typer.Option(
        None,
        "--base",
        "-b",
        help="Base model ID (for LoRA adapters). Auto-detected if not set.",
    ),
    llama_cpp_path: Optional[str] = typer.Option(
        None,
        "--llama-cpp",
        help="Path to llama.cpp directory. Auto-detected or cloned to ~/.soup/llama.cpp",
    ),
    onnx_task: str = typer.Option(
        "text-generation",
        "--onnx-task",
        help="ONNX export task: text-generation (causal LM) or feature-extraction (embedding)",
    ),
    deploy: Optional[str] = typer.Option(
        None,
        "--deploy",
        help="Auto-deploy after export. Currently supported: ollama",
    ),
    deploy_name: Optional[str] = typer.Option(
        None,
        "--deploy-name",
        help="Model name for deployment (used with --deploy)",
    ),
    bits: int = typer.Option(
        4,
        "--bits",
        help="Quantization bits for AWQ/GPTQ: 4 or 8",
    ),
    group_size: int = typer.Option(
        128,
        "--group-size",
        help="Group size for AWQ/GPTQ quantization",
    ),
    calibration_data: Optional[str] = typer.Option(
        None,
        "--calibration-data",
        help="Path to calibration JSONL for AWQ/GPTQ (default: use built-in sample)",
    ),
    calibration_samples: int = typer.Option(
        128,
        "--calibration-samples",
        help="Number of calibration samples for AWQ/GPTQ",
    ),
    registry_id: Optional[str] = typer.Option(
        None,
        "--registry-id",
        help="Attach exported artifact to this registry entry "
        "(default: auto-match by source --model output dir)",
    ),
    trust_remote_code: bool = typer.Option(
        False,
        "--trust-remote-code",
        help=(
            "Allow loading models that ship custom Python via auto_map. "
            "Default deny (v0.36.0). Only enable if you trust the source."
        ),
    ),
    quant_config: Optional[str] = typer.Option(
        None,
        "--quant-config",
        help=(
            "Path to YAML for torchao PTQ export (v0.53.1 #142). "
            "Required when --format=torchao."
        ),
    ),
    gguf_flavour: Optional[str] = typer.Option(
        None,
        "--gguf-flavour",
        help=(
            "Advanced GGUF format flag — UD-Q*_K_XL / IQ*_M / Q4_0_4_4 / etc. "
            "Required when --format=gguf-ud (v0.53.1 #139)."
        ),
    ),
    hub: str = typer.Option(
        "hf",
        "--hub",
        help=(
            "Source hub for the base model when --model is a LoRA adapter: "
            "hf (default) / modelscope / modelers (v0.53.10 #152)."
        ),
    ),
):
    """Export a model to GGUF, ONNX, TensorRT-LLM, AWQ, GPTQ, or TorchAO format."""
    # v0.53.10 #152 — pre-fetch the base model from a non-HF hub. ``model``
    # is typically a local merged dir / adapter dir; only ``base`` is rewritten.
    if hub and hub != "hf":
        from soup_cli.utils.hubs import apply_hub_to_cli_model

        try:
            _, base = apply_hub_to_cli_model(model, base, hub, console=console)
        except (TypeError, ValueError) as exc:
            console.print(f"[red]{exc}[/]")
            raise typer.Exit(code=2) from exc
        except ImportError as exc:
            console.print(f"[red]{exc}[/]")
            raise typer.Exit(code=1) from exc

    model_path = Path(model)

    # --- Validate ---
    if not model_path.exists():
        console.print(f"[red]Model path not found: {model_path}[/]")
        raise typer.Exit(1)

    if fmt not in SUPPORTED_FORMATS:
        console.print(
            f"[red]Unsupported format: {fmt}[/]\n"
            f"Supported: {', '.join(SUPPORTED_FORMATS)}"
        )
        raise typer.Exit(1)

    # --- ONNX export path ---
    if fmt == "onnx":
        _export_onnx(model_path, output, base, onnx_task, trust_remote_code)
        return

    # --- TensorRT-LLM export path ---
    if fmt == "tensorrt":
        _export_tensorrt(model_path, output, base, trust_remote_code)
        return

    # --- AWQ export path ---
    if fmt == "awq":
        _export_awq(
            model_path, output, base, bits, group_size,
            calibration_data, calibration_samples, trust_remote_code,
        )
        return

    # --- GPTQ export path ---
    if fmt == "gptq":
        _export_gptq(
            model_path, output, base, bits, group_size,
            calibration_data, calibration_samples, trust_remote_code,
        )
        return

    # --- TorchAO PTQ export path (v0.53.1 #142) ---
    if fmt == "torchao":
        _export_torchao_cli(
            model_path, output, quant_config, trust_remote_code,
        )
        return

    # --- Advanced GGUF export path (UD / IQ / Apple-ARM, v0.53.1 #139) ---
    if fmt == "gguf-ud":
        _export_gguf_advanced(
            model_path=model_path,
            output=output,
            base=base,
            gguf_flavour=gguf_flavour,
            calibration_data=calibration_data,
            llama_cpp_path=llama_cpp_path,
            trust_remote_code=trust_remote_code,
        )
        return

    # --- BitNet 1.58-bit / TQ1_0 GGUF (v0.71.20 #134) ---
    # Live llama.cpp TQ1_0 ternary export. Requires a built llama.cpp
    # toolchain; the convert/quantize binaries surface a friendly
    # FileNotFoundError when absent (infra-blocked, mirrors gguf-ud).
    if fmt in ("bitnet", "tq1_0"):
        _export_bitnet_gguf(
            model_path=model_path,
            output=output,
            base=base,
            export_format=fmt,
            llama_cpp_path=llama_cpp_path,
            trust_remote_code=trust_remote_code,
        )
        return

    if quant not in GGUF_QUANT_TYPES:
        console.print(
            f"[red]Unsupported quantization: {quant}[/]\n"
            f"Supported: {', '.join(GGUF_QUANT_TYPES)}"
        )
        raise typer.Exit(1)

    # --- Check if LoRA adapter (needs merge first) ---
    adapter_config_path = model_path / "adapter_config.json"
    is_adapter = adapter_config_path.exists()
    merge_dir = None

    if is_adapter:
        console.print("[yellow]LoRA adapter detected - merging with base model first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model from adapter_config.json.[/]\n"
                "Please specify with [bold]--base[/] flag."
            )
            raise typer.Exit(1)

        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(
            str(model_path), base_model, str(merge_dir), trust_remote_code,
        )
        model_path = merge_dir

    # --- Find llama.cpp ---
    llama_dir = _find_llama_cpp(llama_cpp_path)

    # --- Convert to GGUF ---
    model_name = Path(model).name
    if output:
        output_path = Path(output)
    else:
        output_path = Path(model).parent / f"{model_name}.{quant}.gguf"

    console.print(
        Panel(
            f"Model:  [bold]{model_path}[/]\n"
            f"Format: [bold]{fmt}[/]\n"
            f"Quant:  [bold]{quant}[/]\n"
            f"Output: [bold]{output_path}[/]",
            title="Export Plan",
        )
    )

    try:
        # Step 1: Convert HF model to GGUF (f16)
        convert_script = llama_dir / "convert_hf_to_gguf.py"
        if not convert_script.exists():
            console.print(
                f"[red]convert_hf_to_gguf.py not found in {llama_dir}[/]\n"
                "Make sure llama.cpp is properly cloned."
            )
            raise typer.Exit(1)

        if quant in ("f16", "f32"):
            # Direct conversion without quantization
            outtype = "f16" if quant == "f16" else "f32"
            console.print(f"[dim]Converting to GGUF ({outtype})...[/]")
            _run_convert(convert_script, model_path, output_path, outtype)
        else:
            # Convert to f16 first, then quantize
            f16_path = output_path.parent / f"{model_name}.f16.gguf"
            console.print("[dim]Converting to GGUF (f16)...[/]")
            _run_convert(convert_script, model_path, f16_path, "f16")

            # Quantize
            console.print(f"[dim]Quantizing to {quant}...[/]")
            _run_quantize(llama_dir, f16_path, output_path, quant)

            # Clean up intermediate f16 file
            if f16_path.exists() and f16_path != output_path:
                f16_path.unlink()

    finally:
        # Clean up temporary merge directory
        if merge_dir and merge_dir.exists():
            console.print("[dim]Cleaning up temporary merge files...[/]")
            shutil.rmtree(merge_dir, ignore_errors=True)

    if not output_path.exists():
        console.print("[red]Export failed - output file not created.[/]")
        raise typer.Exit(1)

    # v0.33.0 #35: optional auto-attach to registry entry
    _maybe_attach_export(
        artifact_path=str(output_path), kind="gguf",
        explicit_id=registry_id, source_model=str(Path(model)),
    )

    file_size = output_path.stat().st_size
    size_str = _format_size(file_size)

    console.print(
        Panel(
            f"Output: [bold]{output_path}[/]\n"
            f"Size:   [bold]{size_str}[/]\n"
            f"Quant:  [bold]{quant}[/]\n\n"
            f"Use with Ollama:\n"
            f"  1. Create a Modelfile:\n"
            f"     [bold]echo 'FROM {output_path}' > Modelfile[/]\n"
            f"  2. Create the model:\n"
            f"     [bold]ollama create {model_name} -f Modelfile[/]\n"
            f"  3. Run it:\n"
            f"     [bold]ollama run {model_name}[/]",
            title="[bold green]Export Complete![/]",
        )
    )

    # --- Auto-deploy to Ollama if requested ---
    if deploy:
        _auto_deploy_ollama(output_path, model_name, deploy, deploy_name)


def _detect_base_model(adapter_config_path: Path) -> Optional[str]:
    """Read base_model_name_or_path from adapter_config.json."""
    try:
        with open(adapter_config_path, encoding="utf-8") as f:
            config = json.load(f)
        return config.get("base_model_name_or_path")
    except (json.JSONDecodeError, OSError):
        return None


def _merge_adapter(
    adapter_path: str,
    base_model: str,
    output_dir: str,
    trust_remote_code: bool = False,
):
    """Merge LoRA adapter with base model."""
    import torch
    from peft import PeftModel
    from transformers import AutoModelForCausalLM, AutoTokenizer

    from soup_cli.utils.trust_remote import (
        model_requires_trust_remote_code,
        resolve_trust_remote_code,
    )

    requires = model_requires_trust_remote_code(adapter_path) or False
    trc = resolve_trust_remote_code(
        base_model,
        requested=trust_remote_code,
        console=console,
        requires_remote_code=requires,
    )

    console.print(f"[dim]Loading base model: {base_model}...[/]")
    model = AutoModelForCausalLM.from_pretrained(
        base_model,
        dtype=torch.float16,
        trust_remote_code=trc,
        device_map="cpu",
    )

    console.print(f"[dim]Loading LoRA adapter: {adapter_path}...[/]")
    model = PeftModel.from_pretrained(model, adapter_path)

    console.print("[dim]Merging weights...[/]")
    model = model.merge_and_unload()

    out = Path(output_dir)
    out.mkdir(parents=True, exist_ok=True)
    model.save_pretrained(str(out))

    tokenizer = AutoTokenizer.from_pretrained(adapter_path, trust_remote_code=trc)
    tokenizer.save_pretrained(str(out))
    console.print("[green]Adapter merged successfully.[/]")


# llama.cpp's own requirements.txt pins `torch~=2.2.1` against the CPU wheel
# index plus an old `transformers`. Installing it into the user's interpreter
# silently DOWNGRADES a CUDA torch to CPU-only and breaks their training setup
# (observed live on Windows during the v0.71.35 GGUF validation: torch
# 2.5.1+cu -> 2.2.2+cpu, transformers 4.57 -> 4.46). Soup's `[train]` extra
# already provides torch / transformers / numpy, so install ONLY the extra
# packages the convert script needs, unpinned, and never touch the rest.
_CONVERT_EXTRA_DEPS = ("gguf", "sentencepiece", "protobuf")


def _install_convert_deps() -> None:
    """Install the convert script's extra deps without disturbing torch."""
    try:
        subprocess.run(
            [sys.executable, "-m", "pip", "install", "-q", *_CONVERT_EXTRA_DEPS],
            check=True,
            capture_output=True,
            text=True,
        )
    except (subprocess.CalledProcessError, FileNotFoundError) as exc:
        # Non-fatal: the user may already have them, or manage deps themselves.
        detail = getattr(exc, "stderr", "") or type(exc).__name__
        console.print(
            "[yellow]Could not auto-install the GGUF convert dependencies "
            f"({', '.join(_CONVERT_EXTRA_DEPS)}).[/]\n"
            f"[dim]{escape(str(detail)[:200])}[/]\n"
            "Install them manually if the conversion fails."
        )


def _find_llama_cpp(user_path: Optional[str] = None) -> Path:
    """Find or clone llama.cpp directory."""
    from soup_cli.utils.constants import SOUP_DIR

    # 1. User-specified path
    if user_path:
        path = Path(user_path)
        if path.exists():
            return path
        console.print(f"[red]llama.cpp not found at: {path}[/]")
        raise typer.Exit(1)

    # 2. LLAMA_CPP_PATH env var
    import os

    env_path = os.environ.get("LLAMA_CPP_PATH")
    if env_path:
        path = Path(env_path)
        if path.exists():
            return path

    # 3. Check ~/.soup/llama.cpp
    # SOUP_DIR is a bare name (".soup"), so it MUST be anchored to the home
    # directory the way tracker.py / registry/store.py do. Using it relatively
    # made the lookup cwd-dependent: llama.cpp was never found in the canonical
    # ~/.soup, and the auto-clone dropped a fresh ~200 MB checkout into whatever
    # directory the user happened to run from (v0.71.35 GGUF validation).
    soup_llama = Path.home() / SOUP_DIR / LLAMA_CPP_DIR_NAME
    if soup_llama.exists() and (soup_llama / "convert_hf_to_gguf.py").exists():
        return soup_llama

    # 4. Auto-clone
    console.print("[yellow]llama.cpp not found. Cloning to ~/.soup/llama.cpp...[/]")
    console.print("[dim]This is a one-time setup for GGUF export.[/]")

    try:
        subprocess.run(
            ["git", "clone", "--depth", "1", "--branch", LLAMA_CPP_TAG,
             "https://github.com/ggerganov/llama.cpp.git", str(soup_llama)],
            check=True,
            capture_output=True,
            text=True,
        )
        _install_convert_deps()
        console.print("[green]llama.cpp cloned successfully.[/]")
        return soup_llama
    except subprocess.CalledProcessError as exc:
        console.print(f"[red]Failed to clone llama.cpp: {exc.stderr}[/]")
        console.print(
            "Please clone manually:\n"
            f"  [bold]git clone https://github.com/ggerganov/llama.cpp.git {soup_llama}[/]\n"
            "Or specify path: [bold]--llama-cpp /path/to/llama.cpp[/]"
        )
        raise typer.Exit(1)
    except FileNotFoundError:
        console.print(
            "[red]git not found.[/] Please install git or clone llama.cpp manually:\n"
            f"  [bold]git clone https://github.com/ggerganov/llama.cpp.git {soup_llama}[/]"
        )
        raise typer.Exit(1)


def _run_convert(script: Path, model_dir: Path, output_path: Path, outtype: str):
    """Run llama.cpp convert_hf_to_gguf.py script."""
    output_path.parent.mkdir(parents=True, exist_ok=True)

    cmd = [
        sys.executable, str(script),
        str(model_dir),
        "--outfile", str(output_path),
        "--outtype", outtype,
    ]
    result = subprocess.run(cmd, capture_output=True, text=True)
    if result.returncode != 0:
        console.print(f"[red]Conversion failed:[/]\n{result.stderr}")
        raise typer.Exit(1)


def _run_quantize(llama_dir: Path, input_path: Path, output_path: Path, quant_type: str):
    """Run llama-quantize (or llama.cpp/build/bin/llama-quantize)."""
    # Try to find the quantize binary
    quantize_bin = _find_quantize_binary(llama_dir)
    if not quantize_bin:
        console.print(
            "[red]llama-quantize binary not found.[/]\n"
            "Build llama.cpp first:\n"
            f"  [bold]cd {llama_dir} && make llama-quantize[/]\n"
            "Or use [bold]--quant f16[/] to skip quantization."
        )
        raise typer.Exit(1)

    cmd = [str(quantize_bin), str(input_path), str(output_path), quant_type.upper()]
    result = subprocess.run(cmd, capture_output=True, text=True)
    if result.returncode != 0:
        console.print(f"[red]Quantization failed:[/]\n{result.stderr}")
        raise typer.Exit(1)


# MSVC / Xcode are multi-config generators: they nest binaries under a
# per-configuration subdirectory (build/bin/Release/llama-quantize.exe) rather
# than the flat build/bin/ that single-config generators (Make/Ninja) produce.
# Without these, `soup export --format gguf` cannot find a correctly-built
# llama.cpp on Windows (v0.71.35 GGUF-on-Windows validation, #70/#144).
_CMAKE_CONFIG_DIRS = ("Release", "RelWithDebInfo", "MinSizeRel", "Debug")


def _find_quantize_binary(llama_dir: Path) -> Optional[Path]:
    """Find the llama-quantize binary."""
    # Check common locations
    candidates = [
        llama_dir / "build" / "bin" / "llama-quantize",
        llama_dir / "build" / "bin" / "llama-quantize.exe",
        llama_dir / "llama-quantize",
        llama_dir / "llama-quantize.exe",
        llama_dir / "build" / "llama-quantize",
    ]
    # Multi-config generator layouts (MSVC on Windows, Xcode on macOS).
    for config in _CMAKE_CONFIG_DIRS:
        candidates.append(llama_dir / "build" / "bin" / config / "llama-quantize")
        candidates.append(llama_dir / "build" / "bin" / config / "llama-quantize.exe")
        candidates.append(llama_dir / "build" / config / "llama-quantize.exe")
    for candidate in candidates:
        if candidate.exists():
            return candidate

    # Check if it's in PATH
    which_result = shutil.which("llama-quantize")
    if which_result:
        return Path(which_result)

    return None


def _export_onnx(
    model_path: Path, output: Optional[str], base: Optional[str],
    task: str = "text-generation",
    trust_remote_code: bool = False,
):
    """Export model to ONNX format via optimum."""
    try:
        from optimum.exporters.onnx import main_export
    except ImportError:
        console.print(
            "[red]optimum not installed.[/]\n"
            "Install with: [bold]pip install \"soup-cli\\[onnx]\"[/]\n"
            "Or directly: [bold]pip install optimum[onnx][/]"
        )
        raise typer.Exit(1)

    # Check if LoRA adapter
    adapter_config_path = model_path / "adapter_config.json"
    is_adapter = adapter_config_path.exists()
    merge_dir = None
    source_path = model_path

    if is_adapter:
        console.print("[yellow]LoRA adapter detected - merging with base model first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model from adapter_config.json.[/]\n"
                "Please specify with [bold]--base[/] flag."
            )
            raise typer.Exit(1)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(str(model_path), base_model, str(merge_dir), trust_remote_code)
        source_path = merge_dir

    output_path = Path(output) if output else model_path.parent / f"{model_path.name}_onnx"

    console.print(
        Panel(
            f"Model:  [bold]{source_path}[/]\n"
            f"Format: [bold]ONNX[/]\n"
            f"Output: [bold]{output_path}[/]",
            title="Export Plan",
        )
    )

    try:
        console.print(
            "[yellow]Warning: ONNX export may execute custom model code "
            "if the model uses trust_remote_code.[/]"
        )
        console.print("[dim]Exporting to ONNX...[/]")
        main_export(
            model_name_or_path=str(source_path),
            output=str(output_path),
            task=task,
        )
    except Exception as exc:
        console.print(f"[red]ONNX export failed:[/] {exc}")
        raise typer.Exit(1)
    finally:
        if merge_dir and merge_dir.exists():
            console.print("[dim]Cleaning up temporary merge files...[/]")
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(
        Panel(
            f"Output: [bold]{output_path}[/]\n"
            f"Format: [bold]ONNX[/]\n\n"
            f"Use with ONNX Runtime:\n"
            f"  [bold]from optimum.onnxruntime import ORTModelForCausalLM[/]\n"
            f"  [bold]model = ORTModelForCausalLM.from_pretrained('{output_path}')[/]",
            title="[bold green]ONNX Export Complete![/]",
        )
    )


def _export_tensorrt(
    model_path: Path,
    output: Optional[str],
    base: Optional[str],
    trust_remote_code: bool = False,
):
    """Export model to TensorRT-LLM format."""
    # TensorRT-LLM uses trtllm-build CLI from the tensorrt_llm package
    trtllm_available = False
    try:
        import tensorrt_llm  # noqa: F401

        trtllm_available = True
    except ImportError:
        pass

    if not trtllm_available:
        console.print(
            "[red]tensorrt_llm not installed.[/]\n"
            "Install with: [bold]pip install \"soup-cli\\[tensorrt]\"[/]\n"
            "Or follow: https://github.com/NVIDIA/TensorRT-LLM#installation"
        )
        raise typer.Exit(1)

    # Check if LoRA adapter
    adapter_config_path = model_path / "adapter_config.json"
    is_adapter = adapter_config_path.exists()
    merge_dir = None
    source_path = model_path

    if is_adapter:
        console.print("[yellow]LoRA adapter detected - merging with base model first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model from adapter_config.json.[/]\n"
                "Please specify with [bold]--base[/] flag."
            )
            raise typer.Exit(1)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(str(model_path), base_model, str(merge_dir), trust_remote_code)
        source_path = merge_dir

    output_path = Path(output) if output else model_path.parent / f"{model_path.name}_trt"

    console.print(
        Panel(
            f"Model:  [bold]{source_path}[/]\n"
            f"Format: [bold]TensorRT-LLM[/]\n"
            f"Output: [bold]{output_path}[/]",
            title="Export Plan",
        )
    )

    try:
        # Step 1: Convert HF model to TensorRT-LLM checkpoint
        console.print("[dim]Converting to TensorRT-LLM checkpoint...[/]")
        ckpt_dir = output_path / "checkpoint"
        ckpt_dir.mkdir(parents=True, exist_ok=True)

        try:
            result = subprocess.run(
                [
                    sys.executable, "-m",
                    "tensorrt_llm.commands.convert_checkpoint",
                    "--model_dir", str(source_path),
                    "--output_dir", str(ckpt_dir),
                    "--dtype", "float16",
                ],
                capture_output=True,
                text=True,
            )
        except FileNotFoundError:
            console.print("[red]Python executable not found.[/]")
            raise typer.Exit(1)
        if result.returncode != 0:
            console.print(
                f"[red]Checkpoint conversion failed:[/]\n{result.stderr}"
            )
            raise typer.Exit(1)

        # Step 2: Build TensorRT engine
        console.print("[dim]Building TensorRT engine...[/]")
        engine_dir = output_path / "engine"
        engine_dir.mkdir(parents=True, exist_ok=True)

        try:
            result = subprocess.run(
                [
                    "trtllm-build",
                    "--checkpoint_dir", str(ckpt_dir),
                    "--output_dir", str(engine_dir),
                    "--gemm_plugin", "float16",
                ],
                capture_output=True,
                text=True,
            )
        except FileNotFoundError:
            console.print(
                "[red]trtllm-build not found in PATH.[/]\n"
                "Ensure tensorrt_llm is installed and "
                "trtllm-build is available."
            )
            raise typer.Exit(1)
        if result.returncode != 0:
            console.print(
                f"[red]TensorRT engine build failed:[/]\n{result.stderr}"
            )
            raise typer.Exit(1)

    finally:
        if merge_dir and merge_dir.exists():
            console.print("[dim]Cleaning up temporary merge files...[/]")
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(
        Panel(
            f"Output: [bold]{output_path}[/]\n"
            f"Format: [bold]TensorRT-LLM[/]\n\n"
            f"Use with TensorRT-LLM:\n"
            f"  [bold]import tensorrt_llm[/]\n"
            f"  [bold]runner = tensorrt_llm.ModelRunner.from_dir('{engine_dir}')[/]",
            title="[bold green]TensorRT-LLM Export Complete![/]",
        )
    )


def _validate_output_path(output: Optional[str]) -> Optional[Path]:
    """Validate output path stays under cwd (path traversal protection)."""
    if output is None:
        return None
    # realpath + commonpath containment (is_under_cwd) — Path.resolve() +
    # relative_to() breaks on Windows 8.3 short names.
    from soup_cli.utils.paths import is_under_cwd

    out_path = Path(output).resolve()
    if not is_under_cwd(output):
        console.print("[red]Output path must be under the current working directory.[/]")
        raise typer.Exit(1)
    return out_path


def _validate_calibration_path(calibration_data: Optional[str]) -> Optional[Path]:
    """Validate calibration data path stays under cwd."""
    if calibration_data is None:
        return None
    from soup_cli.utils.paths import is_under_cwd

    cal_path = Path(calibration_data).resolve()
    if not is_under_cwd(calibration_data):
        console.print("[red]Calibration data path must be under the current working directory.[/]")
        raise typer.Exit(1)
    if not cal_path.exists():
        console.print(f"[red]Calibration data not found: {cal_path}[/]")
        raise typer.Exit(1)
    return cal_path


def _load_calibration_texts(cal_path: Optional[Path], max_samples: int = 128) -> list:
    """Load calibration texts from JSONL file."""
    if cal_path is None:
        return []
    texts = []
    with open(cal_path, encoding="utf-8") as fh:
        for line in fh:
            line = line.strip()
            if not line:
                continue
            try:
                row = json.loads(line)
                # Support "text" field or concatenate all string values
                if "text" in row:
                    texts.append(str(row["text"]))
                else:
                    texts.append(" ".join(str(v) for v in row.values() if v))
            except json.JSONDecodeError:
                continue
            if len(texts) >= max_samples:
                break
    return texts


def _export_awq(
    model_path: Path,
    output: Optional[str],
    base: Optional[str],
    bits: int = 4,
    group_size: int = 128,
    calibration_data: Optional[str] = None,
    calibration_samples: int = 128,
    trust_remote_code: bool = False,
) -> None:
    """Export model to AWQ format via autoawq."""
    # Validate bits
    valid_bits = {4, 8}
    if bits not in valid_bits:
        console.print(
            f"[red]Invalid --bits {bits}. Must be one of: {sorted(valid_bits)}[/]"
        )
        raise typer.Exit(1)

    # Validate output path (security: path traversal protection)
    validated_output = _validate_output_path(output)

    # Validate calibration path (security: path traversal protection)
    cal_path = _validate_calibration_path(calibration_data)

    try:
        from awq import AutoAWQForCausalLM
    except ImportError:
        console.print(
            "[red]autoawq not installed.[/]\n"
            "Install with: [bold]pip install \"soup-cli\\[awq]\"[/]\n"
            "Or directly: [bold]pip install autoawq[/]"
        )
        raise typer.Exit(1)

    from transformers import AutoTokenizer

    # Check if LoRA adapter — merge first
    adapter_config_path = model_path / "adapter_config.json"
    is_adapter = adapter_config_path.exists()
    merge_dir = None
    source_path = model_path

    if is_adapter:
        console.print("[yellow]LoRA adapter detected - merging with base model first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model from adapter_config.json.[/]\n"
                "Please specify with [bold]--base[/] flag."
            )
            raise typer.Exit(1)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(str(model_path), base_model, str(merge_dir), trust_remote_code)
        source_path = merge_dir

    default_out = model_path.parent / f"{model_path.name}_awq"
    output_path = validated_output if validated_output else default_out

    console.print(
        Panel(
            f"Model:      [bold]{source_path}[/]\n"
            f"Format:     [bold]AWQ[/]\n"
            f"Bits:       [bold]{bits}[/]\n"
            f"Group size: [bold]{group_size}[/]\n"
            f"Output:     [bold]{output_path}[/]",
            title="Export Plan",
        )
    )

    try:
        console.print(
            Panel(
                "[yellow]Warning:[/] Loading model with trust_remote_code=True.\n"
                "This may execute custom code from the model directory.",
                title="Security Notice",
            )
        )
        console.print("[dim]Loading model for AWQ quantization...[/]")
        model = AutoAWQForCausalLM.from_pretrained(str(source_path))
        from soup_cli.utils.trust_remote import (
            model_requires_trust_remote_code,
            resolve_trust_remote_code,
        )

        requires_tok = model_requires_trust_remote_code(str(source_path)) or False
        trc_tok = resolve_trust_remote_code(
            str(source_path),
            requested=trust_remote_code,
            console=console,
            requires_remote_code=requires_tok,
        )
        tokenizer = AutoTokenizer.from_pretrained(str(source_path), trust_remote_code=trc_tok)

        quant_config = {"zero_point": True, "q_group_size": group_size, "w_bit": bits}

        # Load calibration data if provided
        calib_data = (
            _load_calibration_texts(cal_path, max_samples=calibration_samples)
            if cal_path else None
        )

        console.print(f"[dim]Quantizing to AWQ {bits}-bit (group_size={group_size})...[/]")
        if calib_data:
            model.quantize(tokenizer, quant_config=quant_config, calib_data=calib_data)
        else:
            model.quantize(tokenizer, quant_config=quant_config)

        console.print("[dim]Saving quantized model...[/]")
        model.save_quantized(str(output_path))
        tokenizer.save_pretrained(str(output_path))

    except Exception as exc:
        console.print(f"[red]AWQ export failed:[/] {exc}")
        raise typer.Exit(1)
    finally:
        if merge_dir and merge_dir.exists():
            console.print("[dim]Cleaning up temporary merge files...[/]")
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(
        Panel(
            f"Output: [bold]{output_path}[/]\n"
            f"Format: [bold]AWQ {bits}-bit[/]\n\n"
            f"Use with vLLM:\n"
            f"  [bold]from vllm import LLM[/]\n"
            f"  [bold]llm = LLM(model='{output_path}', quantization='awq')[/]",
            title="[bold green]AWQ Export Complete![/]",
        )
    )


def _export_gptq(
    model_path: Path,
    output: Optional[str],
    base: Optional[str],
    bits: int = 4,
    group_size: int = 128,
    calibration_data: Optional[str] = None,
    calibration_samples: int = 128,
    trust_remote_code: bool = False,
) -> None:
    """Export model to GPTQ format via auto-gptq."""
    # Validate bits
    valid_bits = {4, 8}
    if bits not in valid_bits:
        console.print(
            f"[red]Invalid --bits {bits}. Must be one of: {sorted(valid_bits)}[/]"
        )
        raise typer.Exit(1)

    # Validate output path (security: path traversal protection)
    validated_output = _validate_output_path(output)

    # Validate calibration path (security: path traversal protection)
    cal_path = _validate_calibration_path(calibration_data)

    try:
        from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
    except ImportError:
        console.print(
            "[red]auto-gptq not installed.[/]\n"
            "Install with: [bold]pip install \"soup-cli\\[gptq]\"[/]\n"
            "Or directly: [bold]pip install auto-gptq[/]"
        )
        raise typer.Exit(1)

    from transformers import AutoTokenizer

    # Check if LoRA adapter — merge first
    adapter_config_path = model_path / "adapter_config.json"
    is_adapter = adapter_config_path.exists()
    merge_dir = None
    source_path = model_path

    if is_adapter:
        console.print("[yellow]LoRA adapter detected - merging with base model first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model from adapter_config.json.[/]\n"
                "Please specify with [bold]--base[/] flag."
            )
            raise typer.Exit(1)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(str(model_path), base_model, str(merge_dir), trust_remote_code)
        source_path = merge_dir

    default_out = model_path.parent / f"{model_path.name}_gptq"
    output_path = validated_output if validated_output else default_out

    console.print(
        Panel(
            f"Model:      [bold]{source_path}[/]\n"
            f"Format:     [bold]GPTQ[/]\n"
            f"Bits:       [bold]{bits}[/]\n"
            f"Group size: [bold]{group_size}[/]\n"
            f"Output:     [bold]{output_path}[/]",
            title="Export Plan",
        )
    )

    try:
        console.print(
            Panel(
                "[yellow]Warning:[/] Loading model with trust_remote_code=True.\n"
                "This may execute custom code from the model directory.",
                title="Security Notice",
            )
        )
        console.print("[dim]Loading model for GPTQ quantization...[/]")
        quantize_config = BaseQuantizeConfig(
            bits=bits,
            group_size=group_size,
            desc_act=False,
        )
        model = AutoGPTQForCausalLM.from_pretrained(
            str(source_path), quantize_config=quantize_config
        )
        from soup_cli.utils.trust_remote import (
            model_requires_trust_remote_code,
            resolve_trust_remote_code,
        )

        requires_tok = model_requires_trust_remote_code(str(source_path)) or False
        trc_tok = resolve_trust_remote_code(
            str(source_path),
            requested=trust_remote_code,
            console=console,
            requires_remote_code=requires_tok,
        )
        tokenizer = AutoTokenizer.from_pretrained(str(source_path), trust_remote_code=trc_tok)

        # Load calibration data if provided
        calib_data = None
        if cal_path:
            texts = _load_calibration_texts(
                cal_path, max_samples=calibration_samples,
            )
            calib_data = [tokenizer(t, return_tensors="pt") for t in texts]

        console.print(f"[dim]Quantizing to GPTQ {bits}-bit (group_size={group_size})...[/]")
        if calib_data:
            model.quantize(calib_data)
        else:
            model.quantize(tokenizer)

        console.print("[dim]Saving quantized model...[/]")
        model.save_quantized(str(output_path))
        tokenizer.save_pretrained(str(output_path))

    except Exception as exc:
        console.print(f"[red]GPTQ export failed:[/] {exc}")
        raise typer.Exit(1)
    finally:
        if merge_dir and merge_dir.exists():
            console.print("[dim]Cleaning up temporary merge files...[/]")
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(
        Panel(
            f"Output: [bold]{output_path}[/]\n"
            f"Format: [bold]GPTQ {bits}-bit[/]\n\n"
            f"Use with vLLM:\n"
            f"  [bold]from vllm import LLM[/]\n"
            f"  [bold]llm = LLM(model='{output_path}', quantization='gptq')[/]",
            title="[bold green]GPTQ Export Complete![/]",
        )
    )


def _auto_deploy_ollama(
    output_path: Path, model_name: str, deploy_target: str, deploy_name: Optional[str]
):
    """Auto-deploy a GGUF file to Ollama after export."""
    if deploy_target != "ollama":
        console.print(
            f"[red]Unsupported deploy target: {deploy_target}[/]\n"
            "Supported: ollama"
        )
        raise typer.Exit(1)

    from soup_cli.utils.ollama import (
        create_modelfile,
        deploy_to_ollama,
        detect_ollama,
        validate_model_name,
    )

    ollama_name = deploy_name or f"soup-{model_name}"

    valid, err = validate_model_name(ollama_name)
    if not valid:
        console.print(f"[red]Invalid deploy name:[/] {err}")
        raise typer.Exit(1)

    version = detect_ollama()
    if not version:
        console.print(
            "[red]Ollama not found -- skipping deploy.[/]\n"
            "Install from: [bold]https://ollama.com[/]"
        )
        raise typer.Exit(1)

    console.print(
        f"\n[green]OK[/] Ollama v{version} detected"
        f" -- deploying as [bold]{ollama_name}[/]"
    )
    console.print(
        "[yellow]Warning:[/] This will overwrite any existing Ollama model "
        f"named '{ollama_name}'."
    )

    # Auto-detect template from soup.yaml, fall back to chatml
    from soup_cli.commands.deploy import _auto_detect_template

    resolved_template = _auto_detect_template() or "chatml"
    modelfile = create_modelfile(gguf_path=output_path, template=resolved_template)
    success, message = deploy_to_ollama(ollama_name, modelfile)
    if not success:
        console.print(f"[red]Deploy failed:[/] {message}")
        raise typer.Exit(1)

    console.print(f"[green]OK[/] Deployed to Ollama: [bold]{ollama_name}[/]")
    console.print(f"Run: [bold]ollama run {ollama_name}[/]")


def _format_size(size_bytes: int) -> str:
    """Format bytes into human-readable string."""
    value: float = float(size_bytes)
    for unit in ("B", "KB", "MB", "GB"):
        if value < 1024.0:
            return f"{value:.1f} {unit}"
        value /= 1024.0
    return f"{value:.1f} TB"


def _maybe_attach_export(
    *, artifact_path: str, kind: str,
    explicit_id: Optional[str], source_model: str,
) -> None:
    """Attach an exported artifact to a registry entry.

    Resolution order:
      1. ``--registry-id`` (explicit override)
      2. Auto-match by source model output dir
    Silent no-op if no match is found and no explicit id was given. Failures
    are surfaced as warnings, never as a hard CLI exit (the export itself
    succeeded).
    """
    from soup_cli.registry.attach import attach_artifact, lookup_entry_by_output_dir

    entry_id = explicit_id
    if entry_id is None:
        entry_id = lookup_entry_by_output_dir(source_model)
    if entry_id is None:
        return
    try:
        attach_artifact(entry_id, path=artifact_path, kind=kind)
    except (ValueError, FileNotFoundError) as exc:
        console.print(
            f"[yellow]Could not attach export to registry "
            f"'{entry_id}':[/] {exc}"
        )
        return
    console.print(
        f"[green]Attached export to registry entry '{entry_id}' as {kind}.[/]"
    )


# --- v0.53.1 #142 — TorchAO PTQ export CLI dispatch -------------------------


def _export_torchao_cli(
    model_path: Path,
    output: Optional[str],
    quant_config: Optional[str],
    trust_remote_code: bool,
) -> None:
    """Dispatch ``soup export --format torchao``.

    Per v0.53.0 ``validate_quant_config_path`` docstring contract:
    enforce cwd containment + ``os.lstat + S_ISLNK`` rejection at CLI
    dispatch time, not in the schema validator.
    """
    if quant_config is None:
        console.print(
            "[red]--format torchao requires --quant-config <yaml>[/]\n"
            "Example: [bold]soup export --format torchao "
            "--quant-config q.yaml --model ./merged[/]"
        )
        raise typer.Exit(2)

    from soup_cli.utils.save_formats import (
        export_torchao,
        load_quant_config,
        validate_torchao_scheme,
    )

    try:
        cfg_data = load_quant_config(quant_config)
    except (TypeError, ValueError, FileNotFoundError) as exc:
        console.print(f"[red]{exc}[/]")
        raise typer.Exit(2)

    scheme_raw = cfg_data.get("scheme")
    if not isinstance(scheme_raw, str):
        console.print(
            "[red]quant_config must declare a top-level 'scheme: <name>' field.[/]"
        )
        raise typer.Exit(2)
    try:
        scheme = validate_torchao_scheme(scheme_raw)
    except (TypeError, ValueError) as exc:
        console.print(f"[red]{exc}[/]")
        raise typer.Exit(2)

    if output is None:
        output_path = model_path.parent / f"{model_path.name}.torchao.{scheme}"
    else:
        output_path = Path(output)

    console.print(Panel(
        f"Model:  [bold]{model_path}[/]\n"
        f"Scheme: [bold]{scheme}[/]\n"
        f"Output: [bold]{output_path}[/]",
        title="TorchAO PTQ Export",
    ))

    try:
        export_torchao(
            model_dir=str(model_path),
            output_dir=str(output_path),
            scheme=scheme,
            quant_config_data={k: v for k, v in cfg_data.items() if k != "scheme"},
            trust_remote_code=trust_remote_code,
        )
    except (ImportError, RuntimeError) as exc:
        console.print(f"[red]TorchAO export failed: {exc}[/]")
        console.print(
            "Try: [bold]pip install torchao[/] "
            "(NVFP4 requires torchao>=0.5)"
        )
        raise typer.Exit(1)
    except (TypeError, ValueError, FileNotFoundError) as exc:
        console.print(f"[red]{exc}[/]")
        raise typer.Exit(2)

    console.print(Panel(
        f"Output: [bold]{output_path}[/]\n"
        f"Scheme: [bold]{scheme}[/]",
        title="[bold green]TorchAO Export Complete[/]",
    ))


# --- v0.53.1 #139 — Advanced GGUF export CLI dispatch -----------------------


def _export_bitnet_gguf(
    *,
    model_path: Path,
    output: Optional[str],
    base: Optional[str],
    export_format: str,
    llama_cpp_path: Optional[str],
    trust_remote_code: bool,
) -> None:
    """Dispatch ``soup export --format bitnet | tq1_0`` (v0.71.20 #134).

    Reuses the v0.53.1 gguf convert→quantize pipeline with the TQ1_0 ternary
    flavour. Pre-merges a LoRA adapter when one is detected. Requires a built
    llama.cpp toolchain.
    """
    from soup_cli.utils.bitnet import export_bitnet_gguf, validate_bitnet_export

    try:
        canonical = validate_bitnet_export(export_format)
    except (TypeError, ValueError) as exc:
        console.print(f"[red]{exc}[/]")
        raise typer.Exit(2)

    if output is None:
        output_path = model_path.parent / f"{model_path.name}.{canonical}.gguf"
    else:
        output_path = Path(output)

    adapter_config_path = model_path / "adapter_config.json"
    merge_dir = None
    if adapter_config_path.exists():
        console.print("[yellow]LoRA adapter detected — merging first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print("[red]Cannot detect base model. Pass --base.[/]")
            raise typer.Exit(2)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(
            str(model_path), base_model, str(merge_dir), trust_remote_code,
        )
        source_model_dir = merge_dir
    else:
        source_model_dir = model_path

    llama_dir = _find_llama_cpp(llama_cpp_path)

    console.print(Panel(
        f"Model:   [bold]{source_model_dir}[/]\n"
        f"Format:  [bold]{canonical}[/] (TQ1_0 ternary)\n"
        f"Output:  [bold]{output_path}[/]",
        title="BitNet GGUF Export",
    ))

    try:
        export_bitnet_gguf(
            model_dir=str(source_model_dir),
            output_path=str(output_path),
            export_format=canonical,
            llama_cpp_dir=str(llama_dir),
        )
    except (FileNotFoundError, RuntimeError, ValueError) as exc:
        console.print(f"[red]BitNet GGUF export failed: {exc}[/]")
        raise typer.Exit(1)
    finally:
        if merge_dir and merge_dir.exists():
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(Panel(
        f"Output: [bold]{output_path}[/]\n"
        f"Format: [bold]{canonical}[/]",
        title="[bold green]BitNet GGUF Export Complete[/]",
    ))


def _export_gguf_advanced(
    *,
    model_path: Path,
    output: Optional[str],
    base: Optional[str],
    gguf_flavour: Optional[str],
    calibration_data: Optional[str],
    llama_cpp_path: Optional[str],
    trust_remote_code: bool,
) -> None:
    """Dispatch ``soup export --format gguf-ud --gguf-flavour <...>``.

    Routes through llama.cpp's ``imatrix`` + ``quantize`` binaries. Supports
    UD-Q*_K_XL ladder, IQ*_M family, Apple/ARM Q4_0_4_4 / Q4_NL etc.
    """
    if gguf_flavour is None:
        console.print(
            "[red]--format gguf-ud requires --gguf-flavour <UD-Q4_K_XL | IQ2_M | "
            "Q4_0_4_4 | ...>[/]"
        )
        raise typer.Exit(2)

    from soup_cli.utils.gguf_quant import (
        export_advanced_gguf,
        is_advanced_gguf_format,
    )

    if not is_advanced_gguf_format(gguf_flavour):
        console.print(
            f"[red]Unknown gguf_flavour {gguf_flavour!r}. "
            "See soup_cli.utils.gguf_quant.ALL_ADVANCED_GGUF_FORMATS.[/]"
        )
        raise typer.Exit(2)

    # Calibration data path (UD / IQ require it; Apple/ARM Q4_0_4_4 doesn't).
    from soup_cli.utils.paths import enforce_under_cwd_and_no_symlink
    if calibration_data is not None:
        try:
            enforce_under_cwd_and_no_symlink(
                calibration_data, "calibration_data",
            )
        except (TypeError, ValueError) as exc:
            console.print(f"[red]{exc}[/]")
            raise typer.Exit(2)
        if not Path(calibration_data).is_file():
            console.print(
                f"[red]Calibration data file not found: {calibration_data}[/]"
            )
            raise typer.Exit(2)

    if output is None:
        output_path = (
            model_path.parent / f"{model_path.name}.{gguf_flavour}.gguf"
        )
    else:
        output_path = Path(output)

    # Pre-merge LoRA adapter if needed
    adapter_config_path = model_path / "adapter_config.json"
    merge_dir = None
    if adapter_config_path.exists():
        console.print("[yellow]LoRA adapter detected — merging first...[/]")
        base_model = base or _detect_base_model(adapter_config_path)
        if not base_model:
            console.print(
                "[red]Cannot detect base model. Pass --base.[/]"
            )
            raise typer.Exit(2)
        merge_dir = model_path.parent / f".soup_merge_tmp_{model_path.name}"
        _merge_adapter(
            str(model_path), base_model, str(merge_dir), trust_remote_code,
        )
        source_model_dir = merge_dir
    else:
        source_model_dir = model_path

    llama_dir = _find_llama_cpp(llama_cpp_path)

    console.print(Panel(
        f"Model:    [bold]{source_model_dir}[/]\n"
        f"Flavour:  [bold]{gguf_flavour}[/]\n"
        f"Calib:    [bold]{calibration_data or '(none — Apple/ARM)'}[/]\n"
        f"Output:   [bold]{output_path}[/]",
        title="Advanced GGUF Export",
    ))

    try:
        export_advanced_gguf(
            model_dir=str(source_model_dir),
            output_path=str(output_path),
            flavour=gguf_flavour,
            calibration_data=calibration_data,
            llama_cpp_dir=str(llama_dir),
        )
    except (FileNotFoundError, RuntimeError, ValueError) as exc:
        console.print(f"[red]Advanced GGUF export failed: {exc}[/]")
        raise typer.Exit(1)
    finally:
        if merge_dir and merge_dir.exists():
            shutil.rmtree(merge_dir, ignore_errors=True)

    console.print(Panel(
        f"Output: [bold]{output_path}[/]\n"
        f"Flavour: [bold]{gguf_flavour}[/]",
        title="[bold green]Advanced GGUF Export Complete[/]",
    ))