apr-cli 0.4.15

/// Extended CLI commands (analysis, profiling, QA, benchmarks, and advanced tools).
///
/// Flattened into `Commands` via `#[command(flatten)]` so all subcommands remain
/// top-level from the user's perspective (e.g., `apr chat`, `apr profile`).
#[derive(Subcommand, Debug)]
pub enum ExtendedCommands {
    /// Interactive chat with language model
    Chat {
        /// Path to .apr model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Sampling temperature (0 = greedy, higher = more random)
        #[arg(long, default_value = "0.7")]
        temperature: f32,
        /// Nucleus sampling threshold
        #[arg(long, default_value = "0.9")]
        top_p: f32,
        /// Maximum tokens to generate per response
        #[arg(long, default_value = "512")]
        max_tokens: usize,
        /// System prompt to set model behavior
        #[arg(long)]
        system: Option<String>,
        /// Show inspection info (top-k probs, tokens/sec)
        #[arg(long)]
        inspect: bool,
        /// Disable GPU acceleration (use CPU)
        #[arg(long)]
        no_gpu: bool,
        /// Force GPU acceleration (requires CUDA)
        #[arg(long)]
        gpu: bool,
        /// Enable inference tracing (APR-TRACE-001)
        #[arg(long)]
        trace: bool,
        /// Trace specific steps only (comma-separated)
        #[arg(long, value_delimiter = ',')]
        trace_steps: Option<Vec<String>>,
        /// Verbose tracing
        #[arg(long)]
        trace_verbose: bool,
        /// Save trace output to JSON file
        #[arg(long, value_name = "FILE")]
        trace_output: Option<PathBuf>,
        /// Trace detail level (none, basic, layer, payload)
        #[arg(long, value_name = "LEVEL", default_value = "basic")]
        trace_level: String,
        /// Enable inline Roofline profiling (PMAT-SHOWCASE-METHODOLOGY-001)
        #[arg(long)]
        profile: bool,
        /// PMAT-488: Compute backend override (cuda, cpu, wgpu)
        #[arg(long, value_name = "BACKEND")]
        backend: Option<String>,
    },
    /// Benchmark throughput (spec H12: >= 10 tok/s)
    Bench {
        /// Path to model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Number of warmup iterations
        #[arg(long, default_value = "3")]
        warmup: usize,
        /// Number of measurement iterations
        #[arg(long, default_value = "5")]
        iterations: usize,
        /// Max tokens to generate per iteration
        #[arg(long, default_value = "32")]
        max_tokens: usize,
        /// Test prompt
        #[arg(long)]
        prompt: Option<String>,
        /// Use realizar for fast inference (vs aprender baseline)
        #[arg(long)]
        fast: bool,
        /// Benchmark specific brick
        #[arg(long)]
        brick: Option<String>,
    },
    /// Evaluate model perplexity (spec H13: PPL <= 20) or classification metrics
    Eval {
        /// Path to model file or checkpoint directory
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Dataset: wikitext-2, lambada, or custom
        #[arg(long, default_value = "wikitext-2")]
        dataset: String,
        /// Custom text (when dataset=custom)
        #[arg(long)]
        text: Option<String>,
        /// Maximum tokens to evaluate
        #[arg(long, default_value = "512")]
        max_tokens: usize,
        /// Perplexity threshold for pass/fail
        #[arg(long, default_value = "20.0")]
        threshold: f32,
        /// Task type: omit for perplexity, "classify" for classification eval
        #[arg(long)]
        task: Option<String>,
        /// Test data file (JSONL) for classification evaluation
        #[arg(long, value_name = "FILE")]
        data: Option<PathBuf>,
        /// Model size hint: "0.5B", "tiny" (for classification eval)
        #[arg(long)]
        model_size: Option<String>,
        /// Number of output classes (default: 5)
        #[arg(long, default_value = "5")]
        num_classes: usize,
        /// Generate HuggingFace model card (README.md) in checkpoint dir
        #[arg(long)]
        generate_card: bool,
        /// Device for inference: "cpu" (default) or "cuda" (GPU-accelerated, ALB-089)
        #[arg(long, default_value = "cpu")]
        device: String,
        /// Number of samples per problem for pass@k (ALB-088, default: 1)
        #[arg(long, default_value = "1")]
        samples: usize,
        /// Sampling temperature (0.0 = greedy, 0.8 = standard for pass@k>1)
        #[arg(long, default_value = "0.0")]
        temperature: f32,
    },
    /// Deep profiling with Roofline analysis
    Profile {
        /// Path to model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Layer-by-layer granular analysis
        #[arg(long)]
        granular: bool,
        /// Output format (human, json, flamegraph)
        #[arg(long, default_value = "human")]
        format: String,
        /// Focus on specific operation
        #[arg(long)]
        focus: Option<String>,
        /// Detect naive implementations
        #[arg(long)]
        detect_naive: bool,
        /// GFLOPS threshold for naive detection
        #[arg(long, default_value = "10.0")]
        threshold: f64,
        /// Compare against HuggingFace baseline
        #[arg(long)]
        compare_hf: Option<String>,
        /// Measure energy consumption (requires RAPL)
        #[arg(long)]
        energy: bool,
        /// Compute performance grade (vs Ollama baseline)
        #[arg(long)]
        perf_grade: bool,
        /// Show call graph
        #[arg(long)]
        callgraph: bool,
        /// Exit non-zero if naive implementation detected
        #[arg(long)]
        fail_on_naive: bool,
        /// Output file path for flamegraph SVG (GH-174, PMAT-182)
        #[arg(long, short = 'o')]
        output: Option<PathBuf>,

        // PMAT-192: CI Assertion Mode (GH-180)
        /// Enable CI mode with assertion checks (exits 1 on failure)
        #[arg(long)]
        ci: bool,
        /// Minimum throughput in tok/s (CI assertion, exits 1 if below)
        #[arg(long)]
        assert_throughput: Option<f64>,
        /// Maximum p99 latency in ms (CI assertion, exits 1 if above)
        #[arg(long)]
        assert_p99: Option<f64>,
        /// Maximum p50 latency in ms (CI assertion, exits 1 if above)
        #[arg(long)]
        assert_p50: Option<f64>,
        /// Warmup passes before measurement (default: 3)
        #[arg(long, default_value = "3")]
        warmup: usize,
        /// Measurement passes (default: 10)
        #[arg(long, default_value = "10")]
        measure: usize,
        /// Number of tokens to generate per measurement pass (default: 32)
        #[arg(long, default_value = "32")]
        tokens: usize,
        /// Compare against Ollama baseline (runs ollama for comparison)
        #[arg(long)]
        ollama: bool,
        /// Disable GPU (force CPU-only profiling)
        #[arg(long)]
        no_gpu: bool,
        /// Compare against another model format (F-PROFILE-011)
        #[arg(long, value_name = "FILE")]
        compare: Option<PathBuf>,
    },
    /// Falsifiable QA checklist for model releases
    Qa {
        /// Path to model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Minimum throughput threshold in tok/s
        #[arg(long, value_name = "TPS")]
        assert_tps: Option<f64>,
        /// Minimum speedup vs Ollama
        #[arg(long, value_name = "SPEEDUP")]
        assert_speedup: Option<f64>,
        /// Minimum GPU vs CPU speedup (F-PERF-042)
        #[arg(long, value_name = "SPEEDUP")]
        assert_gpu_speedup: Option<f64>,
        /// Skip golden output test
        #[arg(long)]
        skip_golden: bool,
        /// Skip throughput benchmark
        #[arg(long)]
        skip_throughput: bool,
        /// Skip Ollama parity comparison
        #[arg(long)]
        skip_ollama: bool,
        /// Skip GPU vs CPU speedup test (F-PERF-042)
        #[arg(long)]
        skip_gpu_speedup: bool,
        /// Skip tensor contract validation (PMAT-235)
        #[arg(long)]
        skip_contract: bool,
        /// Skip cross-format parity test (F-QUAL-032)
        #[arg(long)]
        skip_format_parity: bool,
        /// Skip PTX parity validation (GH-219)
        #[arg(long)]
        skip_ptx_parity: bool,
        /// SafeTensors model path for cross-format parity test (F-QUAL-032)
        #[arg(long, value_name = "PATH")]
        safetensors_path: Option<PathBuf>,
        /// Number of benchmark iterations
        #[arg(long, default_value = "10")]
        iterations: usize,
        /// Number of warmup iterations
        #[arg(long, default_value = "3")]
        warmup: usize,
        /// Maximum tokens to generate
        #[arg(long, default_value = "32")]
        max_tokens: usize,
        /// Output as JSON (for CI integration)
        #[arg(long)]
        json: bool,
        /// Verbose output
        #[arg(short, long)]
        verbose: bool,
        /// Minimum number of gates that must execute (fail if fewer)
        #[arg(long, value_name = "N")]
        min_executed: Option<usize>,
        /// Previous QA report for regression detection
        #[arg(long, value_name = "FILE")]
        previous_report: Option<PathBuf>,
        /// Maximum allowed performance regression ratio (default: 0.10 = 10%)
        #[arg(long, value_name = "RATIO")]
        regression_threshold: Option<f64>,
        /// Skip GPU state isolation test
        #[arg(long)]
        skip_gpu_state: bool,
        /// Skip metadata plausibility validation (Bug 210, GH-222)
        #[arg(long)]
        skip_metadata: bool,
        /// Skip GPU capability match gate (GH-280)
        #[arg(long)]
        skip_capability: bool,
        /// Assert classifier head presence and shape (F-CLASS-004)
        #[arg(long)]
        assert_classifier_head: bool,
    },
    /// GPU/CPU parity check (PMAT-232: genchi genbutsu — see where GPU diverges)
    Parity {
        /// Path to GGUF model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Prompt text (default: "What is 2+2?")
        #[arg(short, long, default_value = "What is 2+2?")]
        prompt: String,
        /// Assert parity (exit non-zero on divergence)
        #[arg(long)]
        assert: bool,
    },
    /// Model-to-PTX source mapping (Mieruka: make GPU kernel dispatch visible)
    #[command(name = "ptx-map")]
    PtxMap {
        /// Path to GGUF model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Filter to specific kernel (e.g., --kernel Q4KGemv)
        #[arg(long)]
        kernel: Option<String>,
        /// Reverse lookup: kernel name -> which layers/steps use it
        #[arg(long)]
        reverse: Option<String>,
        /// Output as JSON
        #[arg(long)]
        json: bool,
        /// Full PTX snippets and detailed analysis
        #[arg(short, long)]
        verbose: bool,
        /// Show batched prefill kernel variants instead of decode
        #[arg(long)]
        prefill: bool,
    },
    /// PTX analysis and bug detection (register pressure, roofline)
    #[command(name = "ptx")]
    Ptx {
        /// Path to a PTX source file
        #[arg(value_name = "FILE")]
        file: Option<PathBuf>,
        /// Analyze a named kernel from trueno-gpu
        #[arg(long, short)]
        kernel: Option<String>,
        /// Strict mode (no performance whitelist)
        #[arg(long)]
        strict: bool,
        /// Show only bug analysis (skip register/memory/roofline)
        #[arg(long)]
        bugs: bool,
        /// Output as JSON
        #[arg(long)]
        json: bool,
        /// Verbose output (include PTX source listing)
        #[arg(short, long)]
        verbose: bool,
    },
    /// ML tuning: LoRA/QLoRA configuration, memory planning, and HPO (GH-176, SPEC-TUNE-2026-001)
    #[cfg(feature = "training")]
    Tune {
        /// Path to model file (optional if using --model)
        #[arg(value_name = "FILE")]
        file: Option<PathBuf>,
        /// Tuning method: auto, full, lora, qlora
        #[arg(long, short = 'm', default_value = "auto")]
        method: String,
        /// LoRA rank (default: auto-selected)
        #[arg(long, short = 'r')]
        rank: Option<u32>,
        /// Available VRAM in GB
        #[arg(long, default_value = "16.0")]
        vram: f64,
        /// Only plan configuration, don't train
        #[arg(long)]
        plan: bool,
        /// Model size for planning (e.g., "7B", "1.5B")
        #[arg(long, value_name = "SIZE")]
        model: Option<String>,
        /// Freeze base model weights
        #[arg(long)]
        freeze_base: bool,
        /// Training data file (JSONL format)
        #[arg(long, value_name = "FILE")]
        train_data: Option<PathBuf>,
        /// Output as JSON (for CI integration)
        #[arg(long)]
        json: bool,
        /// Task type for HPO: classify (SPEC-TUNE-2026-001)
        #[arg(long)]
        task: Option<String>,
        /// Number of HPO trials (default: 10)
        #[arg(long, default_value = "10")]
        budget: usize,
        /// HPO search strategy: tpe, grid, random
        #[arg(long, default_value = "tpe")]
        strategy: String,
        /// HPO scheduler: asha, median, none
        #[arg(long, default_value = "asha")]
        scheduler: String,
        /// Scout mode: 1 epoch per trial for fast exploration
        #[arg(long)]
        scout: bool,
        /// Training data file for HPO (JSONL format)
        #[arg(long, value_name = "FILE")]
        data: Option<PathBuf>,
        /// Number of output classes for classification
        #[arg(long, default_value = "5")]
        num_classes: usize,
        /// Model size hint for HPO (e.g., "0.5B", "1.5B")
        #[arg(long)]
        model_size: Option<String>,
        /// Warm-start from scout phase results directory
        #[arg(long, value_name = "DIR")]
        from_scout: Option<PathBuf>,
        /// Maximum epochs per trial (full mode, default: 20)
        #[arg(long, default_value = "20")]
        max_epochs: usize,
        /// Maximum wall-clock time (e.g., "8h", "30m")
        #[arg(long)]
        time_limit: Option<String>,
    },
    /// Attach live TUI to a running training session
    #[cfg(feature = "training")]
    Monitor {
        /// Experiment output directory (same as finetune -o)
        #[arg(value_name = "DIR")]
        dir: Option<PathBuf>,
        /// Refresh interval in milliseconds
        #[arg(long, default_value = "500")]
        refresh_ms: u64,
        /// Compact display mode
        #[arg(long)]
        compact: bool,
        /// Output JSON lines instead of TUI (for LLM agents and CI)
        #[arg(long)]
        json: bool,
        /// Output format: tui (default), json, text
        #[arg(long, default_value = "tui")]
        format: String,
    },
    /// List, show, and compare training experiment runs
    #[cfg(feature = "training")]
    Runs {
        #[command(subcommand)]
        command: RunsCommands,
    },
    /// Interactive experiment browser (TUI with loss curves)
    #[cfg(feature = "training")]
    Experiment {
        #[command(subcommand)]
        command: ExperimentCommands,
    },
    /// ComputeBrick pipeline monitor (cbtop)
    Cbtop {
        /// Model name (e.g., qwen2.5-coder-1.5b)
        #[arg(long)]
        model: Option<String>,
        /// Attach to running realizar process
        #[arg(long)]
        attach: Option<String>,
        /// Path to GGUF model file for real profiling
        #[arg(long, value_name = "MODEL")]
        model_path: Option<PathBuf>,
        /// Run in headless mode (no TUI, for CI/automation)
        #[arg(long)]
        headless: bool,
        /// Output JSON format (requires --headless)
        #[arg(long)]
        json: bool,
        /// Output file path (requires --headless)
        #[arg(long, value_name = "FILE")]
        output: Option<PathBuf>,
        /// CI mode: exit with code 1 if thresholds not met
        #[arg(long)]
        ci: bool,
        /// Minimum throughput threshold in tok/s (for --ci)
        #[arg(long, value_name = "TOK_S")]
        throughput: Option<f64>,
        /// Minimum brick score threshold 0-100 (for --ci)
        #[arg(long, value_name = "SCORE")]
        brick_score: Option<u32>,
        /// Number of warmup iterations before measurement
        #[arg(long, default_value = "10")]
        warmup: usize,
        /// Number of measurement iterations
        #[arg(long, default_value = "100")]
        iterations: usize,
        /// PAR-100: Enable speculative decoding benchmark
        #[arg(long)]
        speculative: bool,
        /// PAR-100: Number of tokens to draft speculatively (default: 4)
        #[arg(long, default_value = "4")]
        speculation_k: usize,
        /// PAR-099: Path to draft model for speculative decoding
        #[arg(long, value_name = "DRAFT_MODEL")]
        draft_model: Option<PathBuf>,
        /// PAR-102: Number of concurrent requests
        #[arg(long, default_value = "1")]
        concurrent: usize,
        /// Use simulated data (for CI testing only)
        #[arg(long)]
        simulated: bool,
    },
    /// Export for probar visual regression testing (PMAT-481)
    Probar {
        /// Path to .apr model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Output directory for test artifacts
        #[arg(short, long, default_value = "./probar-export")]
        output: PathBuf,
        /// Export format: json, png, or both
        #[arg(long, default_value = "both")]
        format: String,
        /// Golden reference directory for comparison
        #[arg(long)]
        golden: Option<PathBuf>,
        /// Filter layers by name pattern
        #[arg(long)]
        layer: Option<String>,
        /// Exit non-zero on golden divergence (CI mode, PMAT-481)
        #[arg(long)]
        assert: bool,
        /// Cosine similarity threshold for golden comparison (default: 0.98)
        #[arg(long, default_value = "0.98")]
        tolerance: f32,
    },
    /// Compare APR model against HuggingFace source
    #[command(name = "compare-hf")]
    CompareHf {
        /// Path to .apr model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// HuggingFace repo ID (e.g., openai/whisper-tiny)
        #[arg(long)]
        hf: String,
        /// Filter tensors by name pattern
        #[arg(long)]
        tensor: Option<String>,
        /// Comparison threshold (default: 1e-5)
        #[arg(long, default_value = "1e-5")]
        threshold: f64,
        /// Output as JSON
        #[arg(long)]
        json: bool,
    },
    /// Format-aware binary forensics (10X better than xxd)
    Hex {
        /// Path to model file (APR, GGUF, or SafeTensors)
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Filter tensors by name pattern
        #[arg(long)]
        tensor: Option<String>,
        /// Limit bytes/values to display
        #[arg(long, default_value = "64")]
        limit: usize,
        /// Show tensor statistics
        #[arg(long)]
        stats: bool,
        /// List tensor names only
        #[arg(long)]
        list: bool,
        /// Output as JSON
        #[arg(long)]
        json: bool,
        /// Annotated file header (magic, version, tensor count, metadata)
        #[arg(long)]
        header: bool,
        /// Q4K/Q6K/Q8_0 super-block structure with field annotations
        #[arg(long)]
        blocks: bool,
        /// Value histogram + entropy + kurtosis analysis
        #[arg(long)]
        distribution: bool,
        /// Layout contract verification overlay per tensor
        #[arg(long)]
        contract: bool,
        /// Per-region byte entropy analysis
        #[arg(long)]
        entropy: bool,
        /// Raw bytes (like xxd but format-aware, with ASCII column)
        #[arg(long)]
        raw: bool,
        /// Start at byte offset (supports 0x prefix for hex)
        #[arg(long, default_value = "0")]
        offset: String,
        /// Bytes per row for raw output (default: 16)
        #[arg(long, default_value = "16")]
        width: usize,
        /// Slice range for partial tensor reads (e.g., 0:3 for first 3 elements)
        #[arg(long)]
        slice: Option<String>,
    },
    /// Model architecture tree view
    Tree {
        /// Path to .apr model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Filter by component pattern
        #[arg(long)]
        filter: Option<String>,
        /// Output format: ascii, dot, mermaid, json
        #[arg(long, default_value = "ascii")]
        format: String,
        /// Show tensor sizes
        #[arg(long)]
        sizes: bool,
        /// Maximum tree depth
        #[arg(long)]
        depth: Option<usize>,
    },
    /// Data flow visualization
    Flow {
        /// Path to .apr model file
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Filter by layer pattern
        #[arg(long)]
        layer: Option<String>,
        /// Component to visualize: full, encoder, decoder, etc.
        #[arg(long, default_value = "full")]
        component: String,
        /// Verbose output with statistics
        #[arg(short, long)]
        verbose: bool,
        /// Output as JSON
        #[arg(long)]
        json: bool,
    },
    /// Cross-subcommand smoke test (does every tool handle this model?)
    Qualify {
        /// Path to model file (APR, GGUF, or SafeTensors)
        #[arg(value_name = "FILE")]
        file: PathBuf,
        /// Testing tier: smoke (Phase 1), standard (+contracts), full (+playbook)
        #[arg(long, default_value = "smoke")]
        tier: String,
        /// Timeout per gate in seconds
        #[arg(long, default_value = "120")]
        timeout: u64,
        /// Output as JSON
        #[arg(long)]
        json: bool,
        /// Show subcommand output (disable stdout suppression)
        #[arg(short, long)]
        verbose: bool,
        /// Skip specific gates (comma-separated)
        #[arg(long, value_delimiter = ',')]
        skip: Option<Vec<String>>,
    },
    /// Training pipeline (plan/apply) — forjar-style pre-flight validation
    #[cfg(feature = "training")]
    Train {
        #[command(subcommand)]
        command: TrainCommands,
    },
    /// Tokenizer training pipeline (plan/apply) — BPE vocabulary learning
    Tokenize {
        #[command(subcommand)]
        command: TokenizeCommands,
    },
    /// Data quality pipeline (audit, split, balance) — powered by alimentar
    Data {
        #[command(subcommand)]
        command: DataCommands,
    },
    /// Pipeline orchestration (plan/apply/status) — wraps forjar DAG engine
    Pipeline {
        #[command(subcommand)]
        command: PipelineCommands,
    },
    /// Automated Five Whys diagnosis on a training checkpoint
    Diagnose {
        /// Path to checkpoint directory
        #[arg(value_name = "CHECKPOINT_DIR")]
        checkpoint_dir: PathBuf,
        /// Test data file (JSONL) for evaluation
        #[arg(long, value_name = "FILE")]
        data: Option<PathBuf>,
        /// Model size hint: "0.5B", "tiny"
        #[arg(long)]
        model_size: Option<String>,
        /// Number of output classes (default: 5)
        #[arg(long, default_value = "5")]
        num_classes: usize,
    },
    /// Publishing, conversion, and analysis tools
    #[command(flatten)]
    Tools(ToolCommands),
}

#[cfg(feature = "training")]
/// Subcommands for `apr runs` — experiment run management (ALB-050/051)
#[derive(Subcommand, Debug)]
pub enum RunsCommands {
    /// List all training experiment runs (with inline loss sparklines)
    Ls {
        /// Directory to scan for experiments (default: current dir)
        #[arg(long, value_name = "DIR")]
        dir: Option<PathBuf>,
        /// Read from global experiment registry (~/.entrenar/experiments.db)
        #[arg(long)]
        global: bool,
        /// Filter by status: running, completed, failed, all
        #[arg(long, default_value = "all")]
        status: String,
        /// Output as JSON
        #[arg(long)]
        json: bool,
        /// Maximum number of runs to show
        #[arg(long, default_value = "50")]
        limit: usize,
    },
    /// Show detailed metrics for a specific run (with braille loss curve)
    Show {
        /// Run ID
        #[arg(value_name = "RUN_ID")]
        run_id: String,
        /// Directory containing experiment DB
        #[arg(long, value_name = "DIR")]
        dir: Option<PathBuf>,
        /// Read from global registry
        #[arg(long)]
        global: bool,
        /// Output as JSON
        #[arg(long)]
        json: bool,
    },
    /// Compare two runs side-by-side (loss curves, config diff, metrics)
    Diff {
        /// First run ID
        #[arg(value_name = "RUN_A")]
        run_a: String,
        /// Second run ID
        #[arg(value_name = "RUN_B")]
        run_b: String,
        /// Directory containing experiment DB
        #[arg(long, value_name = "DIR")]
        dir: Option<PathBuf>,
        /// Read from global registry
        #[arg(long)]
        global: bool,
        /// Output as JSON
        #[arg(long)]
        json: bool,
    },
}

#[cfg(feature = "training")]
/// Subcommands for `apr experiment` — interactive experiment browser (ALB-024)
#[derive(Subcommand, Debug)]
pub enum ExperimentCommands {
    /// Browse experiment history with interactive TUI (loss curves, params)
    View {
        /// Path to experiment database file
        #[arg(long, value_name = "FILE")]
        db: Option<PathBuf>,
        /// Read from global experiment registry (~/.entrenar/experiments.db)
        #[arg(long)]
        global: bool,
        /// Output as JSON (non-interactive)
        #[arg(long)]
        json: bool,
    },
}