Kt minimax (#1742)

[feat]: fp8 kernel and kt-cli support

kt-kernel/python/cli/commands/__init__.py

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+"""
+Command modules for kt-cli.
+"""

kt-kernel/python/cli/commands/bench.py

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+"""
+Bench commands for kt-cli.
+
+Runs benchmarks for performance testing.
+"""
+
+import subprocess
+import sys
+from enum import Enum
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import (
+    console,
+    print_error,
+    print_info,
+    print_step,
+    print_success,
+)
+
+
+class BenchType(str, Enum):
+    """Benchmark type."""
+
+    INFERENCE = "inference"
+    MLA = "mla"
+    MOE = "moe"
+    LINEAR = "linear"
+    ATTENTION = "attention"
+    ALL = "all"
+
+
+def bench(
+    type: BenchType = typer.Option(
+        BenchType.ALL,
+        "--type",
+        "-t",
+        help="Benchmark type",
+    ),
+    model: Optional[str] = typer.Option(
+        None,
+        "--model",
+        "-m",
+        help="Model to benchmark",
+    ),
+    output: Optional[Path] = typer.Option(
+        None,
+        "--output",
+        "-o",
+        help="Output file for results (JSON)",
+    ),
+    iterations: int = typer.Option(
+        10,
+        "--iterations",
+        "-n",
+        help="Number of iterations",
+    ),
+) -> None:
+    """Run full benchmark suite."""
+    console.print()
+    print_step(t("bench_starting"))
+    print_info(t("bench_type", type=type.value))
+    console.print()
+
+    if type == BenchType.ALL:
+        _run_all_benchmarks(model, output, iterations)
+    elif type == BenchType.INFERENCE:
+        _run_inference_benchmark(model, output, iterations)
+    elif type == BenchType.MLA:
+        _run_component_benchmark("mla", output, iterations)
+    elif type == BenchType.MOE:
+        _run_component_benchmark("moe", output, iterations)
+    elif type == BenchType.LINEAR:
+        _run_component_benchmark("linear", output, iterations)
+    elif type == BenchType.ATTENTION:
+        _run_component_benchmark("attention", output, iterations)
+
+    console.print()
+    print_success(t("bench_complete"))
+    if output:
+        console.print(f"  Results saved to: {output}")
+    console.print()
+
+
+def microbench(
+    component: str = typer.Argument(
+        "moe",
+        help="Component to benchmark (moe, mla, linear, attention)",
+    ),
+    batch_size: int = typer.Option(
+        1,
+        "--batch-size",
+        "-b",
+        help="Batch size",
+    ),
+    seq_len: int = typer.Option(
+        1,
+        "--seq-len",
+        "-s",
+        help="Sequence length",
+    ),
+    iterations: int = typer.Option(
+        100,
+        "--iterations",
+        "-n",
+        help="Number of iterations",
+    ),
+    warmup: int = typer.Option(
+        10,
+        "--warmup",
+        "-w",
+        help="Warmup iterations",
+    ),
+    output: Optional[Path] = typer.Option(
+        None,
+        "--output",
+        "-o",
+        help="Output file for results (JSON)",
+    ),
+) -> None:
+    """Run micro-benchmark for specific components."""
+    console.print()
+    console.print(f"[yellow]{t('feature_coming_soon')}[/yellow]")
+    console.print()
+    raise typer.Exit(0)
+
+    # Try to find the benchmark script
+    kt_kernel_path = _find_kt_kernel_path()
+
+    if kt_kernel_path is None:
+        print_error("kt-kernel not found. Install with: kt install inference")
+        raise typer.Exit(1)
+
+    bench_dir = kt_kernel_path / "bench"
+
+    # Map component to script
+    component_scripts = {
+        "moe": "bench_moe.py",
+        "mla": "bench_mla.py",
+        "linear": "bench_linear.py",
+        "attention": "bench_attention.py",
+        "mlp": "bench_mlp.py",
+    }
+
+    script_name = component_scripts.get(component.lower())
+    if script_name is None:
+        print_error(f"Unknown component: {component}")
+        console.print(f"Available: {', '.join(component_scripts.keys())}")
+        raise typer.Exit(1)
+
+    script_path = bench_dir / script_name
+    if not script_path.exists():
+        print_error(f"Benchmark script not found: {script_path}")
+        raise typer.Exit(1)
+
+    # Run benchmark
+    cmd = [
+        sys.executable,
+        str(script_path),
+        "--batch-size",
+        str(batch_size),
+        "--seq-len",
+        str(seq_len),
+        "--iterations",
+        str(iterations),
+        "--warmup",
+        str(warmup),
+    ]
+
+    if output:
+        cmd.extend(["--output", str(output)])
+
+    console.print(f"[dim]$ {' '.join(cmd)}[/dim]")
+    console.print()
+
+    try:
+        process = subprocess.run(cmd)
+
+        if process.returncode == 0:
+            console.print()
+            print_success(t("bench_complete"))
+            if output:
+                console.print(f"  Results saved to: {output}")
+        else:
+            print_error(f"Benchmark failed with exit code {process.returncode}")
+            raise typer.Exit(process.returncode)
+
+    except FileNotFoundError as e:
+        print_error(f"Failed to run benchmark: {e}")
+        raise typer.Exit(1)
+
+
+def _find_kt_kernel_path() -> Optional[Path]:
+    """Find the kt-kernel installation path."""
+    try:
+        import kt_kernel
+
+        return Path(kt_kernel.__file__).parent.parent
+    except ImportError:
+        pass
+
+    # Check common locations
+    possible_paths = [
+        Path.home() / "Projects" / "ktransformers" / "kt-kernel",
+        Path("/opt/ktransformers/kt-kernel"),
+        Path.cwd() / "kt-kernel",
+    ]
+
+    for path in possible_paths:
+        if path.exists() and (path / "bench").exists():
+            return path
+
+    return None
+
+
+def _run_all_benchmarks(model: Optional[str], output: Optional[Path], iterations: int) -> None:
+    """Run all benchmarks."""
+    components = ["moe", "mla", "linear", "attention"]
+
+    for component in components:
+        console.print(f"\n[bold]Running {component} benchmark...[/bold]")
+        _run_component_benchmark(component, None, iterations)
+
+
+def _run_inference_benchmark(model: Optional[str], output: Optional[Path], iterations: int) -> None:
+    """Run inference benchmark."""
+    if model is None:
+        print_error("Model required for inference benchmark. Use --model flag.")
+        raise typer.Exit(1)
+
+    print_info(f"Running inference benchmark on {model}...")
+    console.print()
+    console.print("[dim]This will start the server and run test requests.[/dim]")
+    console.print()
+
+    # TODO: Implement actual inference benchmarking
+    print_error("Inference benchmarking not yet implemented.")
+
+
+def _run_component_benchmark(component: str, output: Optional[Path], iterations: int) -> None:
+    """Run a component benchmark."""
+    kt_kernel_path = _find_kt_kernel_path()
+
+    if kt_kernel_path is None:
+        print_error("kt-kernel not found.")
+        return
+
+    bench_dir = kt_kernel_path / "bench"
+    script_map = {
+        "moe": "bench_moe.py",
+        "mla": "bench_mla.py",
+        "linear": "bench_linear.py",
+        "attention": "bench_attention.py",
+    }
+
+    script_name = script_map.get(component)
+    if script_name is None:
+        print_error(f"Unknown component: {component}")
+        return
+
+    script_path = bench_dir / script_name
+    if not script_path.exists():
+        print_error(f"Script not found: {script_path}")
+        return
+
+    cmd = [sys.executable, str(script_path), "--iterations", str(iterations)]
+
+    try:
+        subprocess.run(cmd)
+    except Exception as e:
+        print_error(f"Benchmark failed: {e}")

kt-kernel/python/cli/commands/chat.py

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+"""
+Chat command for kt-cli.
+
+Provides interactive chat interface with running model server.
+"""
+
+import json
+import os
+import sys
+from datetime import datetime
+from pathlib import Path
+from typing import Optional
+
+import typer
+from rich.console import Console
+from rich.markdown import Markdown
+from rich.panel import Panel
+from rich.prompt import Prompt, Confirm
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import (
+    console,
+    print_error,
+    print_info,
+    print_success,
+    print_warning,
+)
+
+# Try to import OpenAI SDK
+try:
+    from openai import OpenAI
+
+    HAS_OPENAI = True
+except ImportError:
+    HAS_OPENAI = False
+
+
+def chat(
+    host: Optional[str] = typer.Option(
+        None,
+        "--host",
+        "-H",
+        help="Server host address",
+    ),
+    port: Optional[int] = typer.Option(
+        None,
+        "--port",
+        "-p",
+        help="Server port",
+    ),
+    model: Optional[str] = typer.Option(
+        None,
+        "--model",
+        "-m",
+        help="Model name (if server hosts multiple models)",
+    ),
+    temperature: float = typer.Option(
+        0.7,
+        "--temperature",
+        "-t",
+        help="Sampling temperature (0.0 to 2.0)",
+    ),
+    max_tokens: int = typer.Option(
+        2048,
+        "--max-tokens",
+        help="Maximum tokens to generate",
+    ),
+    system_prompt: Optional[str] = typer.Option(
+        None,
+        "--system",
+        "-s",
+        help="System prompt",
+    ),
+    save_history: bool = typer.Option(
+        True,
+        "--save-history/--no-save-history",
+        help="Save conversation history",
+    ),
+    history_file: Optional[Path] = typer.Option(
+        None,
+        "--history-file",
+        help="Path to save conversation history",
+    ),
+    stream: bool = typer.Option(
+        True,
+        "--stream/--no-stream",
+        help="Enable streaming output",
+    ),
+) -> None:
+    """Start interactive chat with a running model server.
+
+    Examples:
+        kt chat                          # Connect to default server
+        kt chat --host 127.0.0.1 -p 8080 # Connect to specific server
+        kt chat -t 0.9 --max-tokens 4096 # Adjust generation parameters
+    """
+    if not HAS_OPENAI:
+        print_error("OpenAI Python SDK is required for chat functionality.")
+        console.print()
+        console.print("Install it with:")
+        console.print("  pip install openai")
+        raise typer.Exit(1)
+
+    settings = get_settings()
+
+    # Resolve server connection
+    final_host = host or settings.get("server.host", "127.0.0.1")
+    final_port = port or settings.get("server.port", 30000)
+
+    # Construct base URL for OpenAI-compatible API
+    base_url = f"http://{final_host}:{final_port}/v1"
+
+    console.print()
+    console.print(
+        Panel.fit(
+            f"[bold cyan]KTransformers Chat[/bold cyan]\n\n"
+            f"Server: [yellow]{final_host}:{final_port}[/yellow]\n"
+            f"Temperature: [cyan]{temperature}[/cyan] | Max tokens: [cyan]{max_tokens}[/cyan]\n\n"
+            f"[dim]Type '/help' for commands, '/quit' to exit[/dim]",
+            border_style="cyan",
+        )
+    )
+    console.print()
+
+    # Check for proxy environment variables
+    proxy_vars = ["HTTP_PROXY", "HTTPS_PROXY", "http_proxy", "https_proxy", "ALL_PROXY", "all_proxy"]
+    detected_proxies = {var: os.environ.get(var) for var in proxy_vars if os.environ.get(var)}
+
+    if detected_proxies:
+        proxy_info = ", ".join(f"{k}={v}" for k, v in detected_proxies.items())
+        console.print()
+        print_warning(t("chat_proxy_detected"))
+        console.print(f"  [dim]{proxy_info}[/dim]")
+        console.print()
+
+        use_proxy = Confirm.ask(t("chat_proxy_confirm"), default=False)
+
+        if not use_proxy:
+            # Temporarily disable proxy for this connection
+            for var in proxy_vars:
+                if var in os.environ:
+                    del os.environ[var]
+            print_info(t("chat_proxy_disabled"))
+            console.print()
+
+    # Initialize OpenAI client
+    try:
+        client = OpenAI(
+            base_url=base_url,
+            api_key="EMPTY",  # SGLang doesn't require API key
+        )
+
+        # Test connection
+        print_info("Connecting to server...")
+        models = client.models.list()
+        available_models = [m.id for m in models.data]
+
+        if not available_models:
+            print_error("No models available on server")
+            raise typer.Exit(1)
+
+        # Select model
+        if model:
+            if model not in available_models:
+                print_warning(f"Model '{model}' not found. Available models: {', '.join(available_models)}")
+                selected_model = available_models[0]
+            else:
+                selected_model = model
+        else:
+            selected_model = available_models[0]
+
+        print_success(f"Connected to model: {selected_model}")
+        console.print()
+
+    except Exception as e:
+        print_error(f"Failed to connect to server: {e}")
+        console.print()
+        console.print("Make sure the model server is running:")
+        console.print("  kt run <model>")
+        raise typer.Exit(1)
+
+    # Initialize conversation history
+    messages = []
+
+    # Add system prompt if provided
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+
+    # Setup history file
+    if save_history:
+        if history_file is None:
+            history_dir = settings.config_dir / "chat_history"
+            history_dir.mkdir(parents=True, exist_ok=True)
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+            history_file = history_dir / f"chat_{timestamp}.json"
+        else:
+            history_file = Path(history_file)
+            history_file.parent.mkdir(parents=True, exist_ok=True)
+
+    # Main chat loop
+    try:
+        while True:
+            # Get user input
+            try:
+                user_input = Prompt.ask("[bold green]You[/bold green]")
+            except (EOFError, KeyboardInterrupt):
+                console.print()
+                print_info("Goodbye!")
+                break
+
+            if not user_input.strip():
+                continue
+
+            # Handle special commands
+            if user_input.startswith("/"):
+                if _handle_command(user_input, messages, temperature, max_tokens):
+                    continue
+                else:
+                    break  # Exit command
+
+            # Add user message to history
+            messages.append({"role": "user", "content": user_input})
+
+            # Generate response
+            console.print()
+            console.print("[bold cyan]Assistant[/bold cyan]")
+
+            try:
+                if stream:
+                    # Streaming response
+                    response_content = _stream_response(client, selected_model, messages, temperature, max_tokens)
+                else:
+                    # Non-streaming response
+                    response_content = _generate_response(client, selected_model, messages, temperature, max_tokens)
+
+                # Add assistant response to history
+                messages.append({"role": "assistant", "content": response_content})
+
+                console.print()
+
+            except Exception as e:
+                print_error(f"Error generating response: {e}")
+                # Remove the user message that caused the error
+                messages.pop()
+                continue
+
+            # Save history if enabled
+            if save_history:
+                _save_history(history_file, messages, selected_model)
+
+    except KeyboardInterrupt:
+        console.print()
+        console.print()
+        print_info("Chat interrupted. Goodbye!")
+
+    # Final history save
+    if save_history and messages:
+        _save_history(history_file, messages, selected_model)
+        console.print(f"[dim]History saved to: {history_file}[/dim]")
+        console.print()
+
+
+def _stream_response(
+    client: "OpenAI",
+    model: str,
+    messages: list,
+    temperature: float,
+    max_tokens: int,
+) -> str:
+    """Generate streaming response and display in real-time."""
+    response_content = ""
+
+    try:
+        stream = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            stream=True,
+        )
+
+        for chunk in stream:
+            if chunk.choices[0].delta.content:
+                content = chunk.choices[0].delta.content
+                response_content += content
+                console.print(content, end="")
+
+        console.print()  # Newline after streaming
+
+    except Exception as e:
+        raise Exception(f"Streaming error: {e}")
+
+    return response_content
+
+
+def _generate_response(
+    client: "OpenAI",
+    model: str,
+    messages: list,
+    temperature: float,
+    max_tokens: int,
+) -> str:
+    """Generate non-streaming response."""
+    try:
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            stream=False,
+        )
+
+        content = response.choices[0].message.content
+
+        # Display as markdown
+        md = Markdown(content)
+        console.print(md)
+
+        return content
+
+    except Exception as e:
+        raise Exception(f"Generation error: {e}")
+
+
+def _handle_command(command: str, messages: list, temperature: float, max_tokens: int) -> bool:
+    """Handle special commands. Returns True to continue chat, False to exit."""
+    cmd = command.lower().strip()
+
+    if cmd in ["/quit", "/exit", "/q"]:
+        console.print()
+        print_info("Goodbye!")
+        return False
+
+    elif cmd in ["/help", "/h"]:
+        console.print()
+        console.print(
+            Panel(
+                "[bold]Available Commands:[/bold]\n\n"
+                "/help, /h         - Show this help message\n"
+                "/quit, /exit, /q  - Exit chat\n"
+                "/clear, /c        - Clear conversation history\n"
+                "/history, /hist   - Show conversation history\n"
+                "/info, /i         - Show current settings\n"
+                "/retry, /r        - Regenerate last response",
+                title="Help",
+                border_style="cyan",
+            )
+        )
+        console.print()
+        return True
+
+    elif cmd in ["/clear", "/c"]:
+        messages.clear()
+        console.print()
+        print_success("Conversation history cleared")
+        console.print()
+        return True
+
+    elif cmd in ["/history", "/hist"]:
+        console.print()
+        if not messages:
+            print_info("No conversation history")
+        else:
+            console.print(
+                Panel(
+                    _format_history(messages),
+                    title=f"History ({len(messages)} messages)",
+                    border_style="cyan",
+                )
+            )
+        console.print()
+        return True
+
+    elif cmd in ["/info", "/i"]:
+        console.print()
+        console.print(
+            Panel(
+                f"[bold]Current Settings:[/bold]\n\n"
+                f"Temperature: [cyan]{temperature}[/cyan]\n"
+                f"Max tokens: [cyan]{max_tokens}[/cyan]\n"
+                f"Messages: [cyan]{len(messages)}[/cyan]",
+                title="Info",
+                border_style="cyan",
+            )
+        )
+        console.print()
+        return True
+
+    elif cmd in ["/retry", "/r"]:
+        if len(messages) >= 2 and messages[-1]["role"] == "assistant":
+            # Remove last assistant response
+            messages.pop()
+            print_info("Retrying last response...")
+            console.print()
+        else:
+            print_warning("No previous response to retry")
+            console.print()
+        return True
+
+    else:
+        print_warning(f"Unknown command: {command}")
+        console.print("[dim]Type /help for available commands[/dim]")
+        console.print()
+        return True
+
+
+def _format_history(messages: list) -> str:
+    """Format conversation history for display."""
+    lines = []
+    for i, msg in enumerate(messages, 1):
+        role = msg["role"].capitalize()
+        content = msg["content"]
+
+        # Truncate long messages
+        if len(content) > 200:
+            content = content[:200] + "..."
+
+        lines.append(f"[bold]{i}. {role}:[/bold] {content}")
+
+    return "\n\n".join(lines)
+
+
+def _save_history(file_path: Path, messages: list, model: str) -> None:
+    """Save conversation history to file."""
+    try:
+        history_data = {
+            "model": model,
+            "timestamp": datetime.now().isoformat(),
+            "messages": messages,
+        }
+
+        with open(file_path, "w", encoding="utf-8") as f:
+            json.dump(history_data, f, indent=2, ensure_ascii=False)
+
+    except Exception as e:
+        print_warning(f"Failed to save history: {e}")

kt-kernel/python/cli/commands/config.py

@@ -0,0 +1,167 @@
+"""
+Config command for kt-cli.
+
+Manages kt-cli configuration.
+"""
+
+from typing import Optional
+
+import typer
+import yaml
+from rich.syntax import Syntax
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import confirm, console, print_error, print_success
+
+app = typer.Typer(help="Manage kt-cli configuration")
+
+
+@app.command(name="init")
+def init() -> None:
+    """Initialize or re-run the first-time setup wizard."""
+    from kt_kernel.cli.main import _show_first_run_setup
+    from kt_kernel.cli.config.settings import get_settings
+
+    settings = get_settings()
+    _show_first_run_setup(settings)
+
+
+@app.command(name="show")
+def show(
+    key: Optional[str] = typer.Argument(None, help="Configuration key to show (e.g., server.port)"),
+) -> None:
+    """Show current configuration."""
+    settings = get_settings()
+
+    if key:
+        value = settings.get(key)
+        if value is not None:
+            if isinstance(value, (dict, list)):
+                console.print(yaml.dump({key: value}, default_flow_style=False, allow_unicode=True))
+            else:
+                console.print(t("config_get_value", key=key, value=value))
+        else:
+            print_error(t("config_get_not_found", key=key))
+            raise typer.Exit(1)
+    else:
+        console.print(f"\n[bold]{t('config_show_title')}[/bold]\n")
+        console.print(f"[dim]{t('config_file_location', path=str(settings.config_path))}[/dim]\n")
+
+        config_yaml = yaml.dump(settings.get_all(), default_flow_style=False, allow_unicode=True)
+        syntax = Syntax(config_yaml, "yaml", theme="monokai", line_numbers=False)
+        console.print(syntax)
+
+
+@app.command(name="set")
+def set_config(
+    key: str = typer.Argument(..., help="Configuration key (e.g., server.port)"),
+    value: str = typer.Argument(..., help="Value to set"),
+) -> None:
+    """Set a configuration value."""
+    settings = get_settings()
+
+    # Try to parse value as JSON/YAML for complex types
+    parsed_value = _parse_value(value)
+
+    settings.set(key, parsed_value)
+    print_success(t("config_set_success", key=key, value=parsed_value))
+
+
+@app.command(name="get")
+def get_config(
+    key: str = typer.Argument(..., help="Configuration key (e.g., server.port)"),
+) -> None:
+    """Get a configuration value."""
+    settings = get_settings()
+    value = settings.get(key)
+
+    if value is not None:
+        if isinstance(value, (dict, list)):
+            console.print(yaml.dump(value, default_flow_style=False, allow_unicode=True))
+        else:
+            console.print(str(value))
+    else:
+        print_error(t("config_get_not_found", key=key))
+        raise typer.Exit(1)
+
+
+@app.command(name="reset")
+def reset(
+    yes: bool = typer.Option(False, "--yes", "-y", help="Skip confirmation"),
+) -> None:
+    """Reset configuration to defaults."""
+    if not yes:
+        if not confirm(t("config_reset_confirm"), default=False):
+            raise typer.Abort()
+
+    settings = get_settings()
+    settings.reset()
+    print_success(t("config_reset_success"))
+
+
+@app.command(name="path")
+def path() -> None:
+    """Show configuration file path."""
+    settings = get_settings()
+    console.print(str(settings.config_path))
+
+
+@app.command(name="model-path-list", deprecated=True, hidden=True)
+def model_path_list() -> None:
+    """[Deprecated] Use 'kt model path-list' instead."""
+    console.print("[yellow]⚠ This command is deprecated. Use 'kt model path-list' instead.[/yellow]\n")
+    import subprocess
+    subprocess.run(["kt", "model", "path-list"])
+
+
+@app.command(name="model-path-add", deprecated=True, hidden=True)
+def model_path_add(
+    path: str = typer.Argument(..., help="Path to add"),
+) -> None:
+    """[Deprecated] Use 'kt model path-add' instead."""
+    console.print("[yellow]⚠ This command is deprecated. Use 'kt model path-add' instead.[/yellow]\n")
+    import subprocess
+    subprocess.run(["kt", "model", "path-add", path])
+
+
+@app.command(name="model-path-remove", deprecated=True, hidden=True)
+def model_path_remove(
+    path: str = typer.Argument(..., help="Path to remove"),
+) -> None:
+    """[Deprecated] Use 'kt model path-remove' instead."""
+    console.print("[yellow]⚠ This command is deprecated. Use 'kt model path-remove' instead.[/yellow]\n")
+    import subprocess
+    subprocess.run(["kt", "model", "path-remove", path])
+
+
+def _parse_value(value: str):
+    """Parse a string value into appropriate Python type."""
+    # Try boolean
+    if value.lower() in ("true", "yes", "on", "1"):
+        return True
+    if value.lower() in ("false", "no", "off", "0"):
+        return False
+
+    # Try integer
+    try:
+        return int(value)
+    except ValueError:
+        pass
+
+    # Try float
+    try:
+        return float(value)
+    except ValueError:
+        pass
+
+    # Try YAML/JSON parsing for lists/dicts
+    try:
+        parsed = yaml.safe_load(value)
+        if isinstance(parsed, (dict, list)):
+            return parsed
+    except yaml.YAMLError:
+        pass
+
+    # Return as string
+    return value

kt-kernel/python/cli/commands/doctor.py

@@ -0,0 +1,394 @@
+"""
+Doctor command for kt-cli.
+
+Diagnoses environment issues and provides recommendations.
+"""
+
+import platform
+import shutil
+from pathlib import Path
+from typing import Optional
+
+import typer
+from rich.table import Table
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import console, print_error, print_info, print_success, print_warning
+from kt_kernel.cli.utils.environment import (
+    check_docker,
+    detect_available_ram_gb,
+    detect_cpu_info,
+    detect_cuda_version,
+    detect_disk_space_gb,
+    detect_env_managers,
+    detect_gpus,
+    detect_memory_info,
+    detect_ram_gb,
+    get_installed_package_version,
+)
+
+
+def doctor(
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Show detailed diagnostics"),
+) -> None:
+    """Diagnose environment issues."""
+    console.print(f"\n[bold]{t('doctor_title')}[/bold]\n")
+
+    issues_found = False
+    checks = []
+
+    # 1. Python version
+    python_version = platform.python_version()
+    python_ok = _check_python_version(python_version)
+    checks.append(
+        {
+            "name": t("doctor_check_python"),
+            "status": "ok" if python_ok else "error",
+            "value": python_version,
+            "hint": "Python 3.10+ required" if not python_ok else None,
+        }
+    )
+    if not python_ok:
+        issues_found = True
+
+    # 2. CUDA availability
+    cuda_version = detect_cuda_version()
+    checks.append(
+        {
+            "name": t("doctor_check_cuda"),
+            "status": "ok" if cuda_version else "warning",
+            "value": cuda_version or t("version_cuda_not_found"),
+            "hint": "CUDA is optional but recommended for GPU acceleration" if not cuda_version else None,
+        }
+    )
+
+    # 3. GPU detection
+    gpus = detect_gpus()
+    if gpus:
+        gpu_names = ", ".join(g.name for g in gpus)
+        total_vram = sum(g.vram_gb for g in gpus)
+        checks.append(
+            {
+                "name": t("doctor_check_gpu"),
+                "status": "ok",
+                "value": t("doctor_gpu_found", count=len(gpus), names=gpu_names),
+                "hint": f"Total VRAM: {total_vram}GB",
+            }
+        )
+    else:
+        checks.append(
+            {
+                "name": t("doctor_check_gpu"),
+                "status": "warning",
+                "value": t("doctor_gpu_not_found"),
+                "hint": "GPU recommended for best performance",
+            }
+        )
+
+    # 4. CPU information
+    cpu_info = detect_cpu_info()
+    checks.append(
+        {
+            "name": t("doctor_check_cpu"),
+            "status": "ok",
+            "value": t("doctor_cpu_info", name=cpu_info.name, cores=cpu_info.cores, threads=cpu_info.threads),
+            "hint": None,
+        }
+    )
+
+    # 5. CPU instruction sets (critical for kt-kernel)
+    isa_list = cpu_info.instruction_sets
+    # Check for recommended instruction sets
+    recommended_isa = {"AVX2", "AVX512F", "AMX-INT8"}
+    has_recommended = bool(set(isa_list) & recommended_isa)
+    has_avx2 = "AVX2" in isa_list
+    has_avx512 = any(isa.startswith("AVX512") for isa in isa_list)
+    has_amx = any(isa.startswith("AMX") for isa in isa_list)
+
+    # Determine status and build display string
+    if has_amx:
+        isa_status = "ok"
+        isa_hint = "AMX available - best performance for INT4/INT8"
+    elif has_avx512:
+        isa_status = "ok"
+        isa_hint = "AVX512 available - good performance"
+    elif has_avx2:
+        isa_status = "warning"
+        isa_hint = "AVX2 only - consider upgrading CPU for better performance"
+    else:
+        isa_status = "error"
+        isa_hint = "AVX2 required for kt-kernel"
+
+    # Show top instruction sets (prioritize important ones)
+    display_isa = isa_list[:8] if len(isa_list) > 8 else isa_list
+    isa_display = ", ".join(display_isa)
+    if len(isa_list) > 8:
+        isa_display += f" (+{len(isa_list) - 8} more)"
+
+    checks.append(
+        {
+            "name": t("doctor_check_cpu_isa"),
+            "status": isa_status,
+            "value": isa_display if isa_display else "None detected",
+            "hint": isa_hint,
+        }
+    )
+
+    # 6. NUMA topology
+    numa_detail = []
+    for node, cpus in sorted(cpu_info.numa_info.items()):
+        if len(cpus) > 6:
+            cpu_str = f"{cpus[0]}-{cpus[-1]}"
+        else:
+            cpu_str = ",".join(str(c) for c in cpus)
+        numa_detail.append(f"{node}: {cpu_str}")
+
+    numa_value = t("doctor_numa_info", nodes=cpu_info.numa_nodes)
+    if verbose and numa_detail:
+        numa_value += " (" + "; ".join(numa_detail) + ")"
+
+    checks.append(
+        {
+            "name": t("doctor_check_numa"),
+            "status": "ok",
+            "value": numa_value,
+            "hint": f"{cpu_info.threads // cpu_info.numa_nodes} threads per node" if cpu_info.numa_nodes > 1 else None,
+        }
+    )
+
+    # 7. System memory (with frequency if available)
+    mem_info = detect_memory_info()
+    if mem_info.frequency_mhz and mem_info.type:
+        mem_value = t(
+            "doctor_memory_freq",
+            available=f"{mem_info.available_gb}GB",
+            total=f"{mem_info.total_gb}GB",
+            freq=mem_info.frequency_mhz,
+            type=mem_info.type,
+        )
+    else:
+        mem_value = t("doctor_memory_info", available=f"{mem_info.available_gb}GB", total=f"{mem_info.total_gb}GB")
+
+    ram_ok = mem_info.total_gb >= 32
+    checks.append(
+        {
+            "name": t("doctor_check_memory"),
+            "status": "ok" if ram_ok else "warning",
+            "value": mem_value,
+            "hint": "32GB+ RAM recommended for large models" if not ram_ok else None,
+        }
+    )
+
+    # 8. Disk space - check all model paths
+    settings = get_settings()
+    model_paths = settings.get_model_paths()
+
+    # Check all configured model paths
+    for i, disk_path in enumerate(model_paths):
+        available_disk, total_disk = detect_disk_space_gb(str(disk_path))
+        disk_ok = available_disk >= 100
+
+        # For multiple paths, add index to name
+        path_label = f"Model Path {i+1}" if len(model_paths) > 1 else t("doctor_check_disk")
+
+        checks.append(
+            {
+                "name": path_label,
+                "status": "ok" if disk_ok else "warning",
+                "value": t("doctor_disk_info", available=f"{available_disk}GB", path=str(disk_path)),
+                "hint": "100GB+ free space recommended for model storage" if not disk_ok else None,
+            }
+        )
+
+    # 6. Required packages
+    packages = [
+        ("kt-kernel", ">=0.4.0", False),  # name, version_req, required
+        ("ktransformers", ">=0.4.0", False),
+        ("sglang", ">=0.4.0", False),
+        ("torch", ">=2.4.0", True),
+        ("transformers", ">=4.45.0", True),
+    ]
+
+    package_issues = []
+    for pkg_name, version_req, required in packages:
+        version = get_installed_package_version(pkg_name)
+        if version:
+            package_issues.append((pkg_name, version, "ok"))
+        elif required:
+            package_issues.append((pkg_name, t("version_not_installed"), "error"))
+            issues_found = True
+        else:
+            package_issues.append((pkg_name, t("version_not_installed"), "warning"))
+
+    if verbose:
+        checks.append(
+            {
+                "name": t("doctor_check_packages"),
+                "status": "ok" if not any(p[2] == "error" for p in package_issues) else "error",
+                "value": f"{sum(1 for p in package_issues if p[2] == 'ok')}/{len(package_issues)} installed",
+                "packages": package_issues,
+            }
+        )
+
+    # 7. SGLang installation source check
+    from kt_kernel.cli.utils.sglang_checker import check_sglang_installation, check_sglang_kt_kernel_support
+
+    sglang_info = check_sglang_installation()
+
+    if sglang_info["installed"]:
+        if sglang_info["from_source"]:
+            if sglang_info["git_info"]:
+                git_remote = sglang_info["git_info"].get("remote", "unknown")
+                git_branch = sglang_info["git_info"].get("branch", "unknown")
+                sglang_source_value = f"Source (GitHub: {git_remote}, branch: {git_branch})"
+                sglang_source_status = "ok"
+                sglang_source_hint = None
+            else:
+                sglang_source_value = "Source (editable)"
+                sglang_source_status = "ok"
+                sglang_source_hint = None
+        else:
+            sglang_source_value = "PyPI (not recommended)"
+            sglang_source_status = "warning"
+            sglang_source_hint = t("sglang_pypi_hint")
+    else:
+        sglang_source_value = "Not installed"
+        sglang_source_status = "warning"
+        sglang_source_hint = t("sglang_install_hint")
+
+    checks.append(
+        {
+            "name": "SGLang Source",
+            "status": sglang_source_status,
+            "value": sglang_source_value,
+            "hint": sglang_source_hint,
+        }
+    )
+
+    # 7b. SGLang kt-kernel support check (only if SGLang is installed)
+    kt_kernel_support = {"supported": True}  # Default to True if not checked
+    if sglang_info["installed"]:
+        # Use cache=False to force re-check in doctor, but silent=True since we show in table
+        kt_kernel_support = check_sglang_kt_kernel_support(use_cache=False, silent=True)
+
+        if kt_kernel_support["supported"]:
+            kt_kernel_value = t("sglang_kt_kernel_supported")
+            kt_kernel_status = "ok"
+            kt_kernel_hint = None
+        else:
+            kt_kernel_value = t("sglang_kt_kernel_not_supported")
+            kt_kernel_status = "error"
+            kt_kernel_hint = 'Reinstall SGLang from: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"'
+            issues_found = True
+
+        checks.append(
+            {
+                "name": "SGLang kt-kernel",
+                "status": kt_kernel_status,
+                "value": kt_kernel_value,
+                "hint": kt_kernel_hint,
+            }
+        )
+
+    # 8. Environment managers
+    env_managers = detect_env_managers()
+    docker = check_docker()
+    env_list = [f"{m.name} {m.version}" for m in env_managers]
+    if docker:
+        env_list.append(f"docker {docker.version}")
+
+    checks.append(
+        {
+            "name": "Environment Managers",
+            "status": "ok" if env_list else "warning",
+            "value": ", ".join(env_list) if env_list else "None found",
+            "hint": "conda or docker recommended for installation" if not env_list else None,
+        }
+    )
+
+    # Display results
+    _display_results(checks, verbose)
+
+    # Show SGLang installation instructions if not installed
+    if not sglang_info["installed"]:
+        from kt_kernel.cli.utils.sglang_checker import print_sglang_install_instructions
+
+        console.print()
+        print_sglang_install_instructions()
+    # Show kt-kernel installation instructions if SGLang is installed but doesn't support kt-kernel
+    elif sglang_info["installed"] and not kt_kernel_support.get("supported", True):
+        from kt_kernel.cli.utils.sglang_checker import print_sglang_kt_kernel_instructions
+
+        console.print()
+        print_sglang_kt_kernel_instructions()
+
+    # Summary
+    console.print()
+    if issues_found:
+        print_warning(t("doctor_has_issues"))
+    else:
+        print_success(t("doctor_all_ok"))
+    console.print()
+
+
+def _check_python_version(version: str) -> bool:
+    """Check if Python version meets requirements."""
+    parts = version.split(".")
+    try:
+        major, minor = int(parts[0]), int(parts[1])
+        return major >= 3 and minor >= 10
+    except (IndexError, ValueError):
+        return False
+
+
+def _display_results(checks: list[dict], verbose: bool) -> None:
+    """Display diagnostic results."""
+    table = Table(show_header=True, header_style="bold")
+    table.add_column("Check", style="bold")
+    table.add_column("Status", width=8)
+    table.add_column("Value")
+    if verbose:
+        table.add_column("Notes", style="dim")
+
+    for check in checks:
+        status = check["status"]
+        if status == "ok":
+            status_str = f"[green]{t('doctor_status_ok')}[/green]"
+        elif status == "warning":
+            status_str = f"[yellow]{t('doctor_status_warning')}[/yellow]"
+        else:
+            status_str = f"[red]{t('doctor_status_error')}[/red]"
+
+        if verbose:
+            table.add_row(
+                check["name"],
+                status_str,
+                check["value"],
+                check.get("hint", ""),
+            )
+        else:
+            table.add_row(
+                check["name"],
+                status_str,
+                check["value"],
+            )
+
+        # Show package details if verbose
+        if verbose and "packages" in check:
+            for pkg_name, pkg_version, pkg_status in check["packages"]:
+                if pkg_status == "ok":
+                    pkg_status_str = "[green]✓[/green]"
+                elif pkg_status == "warning":
+                    pkg_status_str = "[yellow]○[/yellow]"
+                else:
+                    pkg_status_str = "[red]✗[/red]"
+
+                table.add_row(
+                    f"  └─ {pkg_name}",
+                    pkg_status_str,
+                    pkg_version,
+                    "",
+                )
+
+    console.print(table)

kt-kernel/python/cli/commands/model.py

@@ -0,0 +1,409 @@
+"""
+Model command for kt-cli.
+
+Manages models: download, list, and storage paths.
+"""
+
+import os
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import (
+    confirm,
+    console,
+    print_error,
+    print_info,
+    print_success,
+    print_warning,
+    prompt_choice,
+)
+
+app = typer.Typer(
+    help="Manage models and storage paths",
+    invoke_without_command=True,
+    no_args_is_help=False,
+)
+
+
+@app.callback()
+def callback(ctx: typer.Context) -> None:
+    """
+    Model management commands.
+
+    Run without arguments to see available models.
+    """
+    # If no subcommand is provided, show the model list
+    if ctx.invoked_subcommand is None:
+        show_model_list()
+
+
+def show_model_list() -> None:
+    """Display available models with their status and paths."""
+    from rich.table import Table
+    from kt_kernel.cli.utils.model_registry import get_registry
+    from kt_kernel.cli.i18n import get_lang
+
+    registry = get_registry()
+    settings = get_settings()
+
+    console.print()
+    console.print(f"[bold cyan]{t('model_supported_title')}[/bold cyan]\n")
+
+    # Get local models mapping
+    local_models = {m.name: p for m, p in registry.find_local_models()}
+
+    # Create table
+    table = Table(show_header=True, header_style="bold")
+    table.add_column(t("model_column_model"), style="cyan", no_wrap=True)
+    table.add_column(t("model_column_status"), justify="center")
+
+    all_models = registry.list_all()
+    for model in all_models:
+        if model.name in local_models:
+            status = f"[green]✓ {t('model_status_local')}[/green]"
+        else:
+            status = "[dim]-[/dim]"
+
+        table.add_row(model.name, status)
+
+    console.print(table)
+    console.print()
+
+    # Usage instructions
+    console.print(f"[bold]{t('model_usage_title')}:[/bold]")
+    console.print(f"  • {t('model_usage_download')}  [cyan]kt model download <model-name>[/cyan]")
+    console.print(f"  • {t('model_usage_list_local')} [cyan]kt model list --local[/cyan]")
+    console.print(f"  • {t('model_usage_search')}     [cyan]kt model search <query>[/cyan]")
+    console.print()
+
+    # Show model storage paths
+    model_paths = settings.get_model_paths()
+    console.print(f"[bold]{t('model_storage_paths_title')}:[/bold]")
+    for path in model_paths:
+        marker = "[green]✓[/green]" if path.exists() else "[dim]✗[/dim]"
+        console.print(f"  {marker} {path}")
+    console.print()
+
+
+@app.command(name="download")
+def download(
+    model: Optional[str] = typer.Argument(
+        None,
+        help="Model name or HuggingFace repo (e.g., deepseek-v3, Qwen/Qwen3-30B)",
+    ),
+    path: Optional[Path] = typer.Option(
+        None,
+        "--path",
+        "-p",
+        help="Custom download path",
+    ),
+    list_models: bool = typer.Option(
+        False,
+        "--list",
+        "-l",
+        help="List available models",
+    ),
+    resume: bool = typer.Option(
+        True,
+        "--resume/--no-resume",
+        help="Resume incomplete downloads",
+    ),
+    yes: bool = typer.Option(
+        False,
+        "--yes",
+        "-y",
+        help="Skip confirmation prompts",
+    ),
+) -> None:
+    """Download model weights from HuggingFace."""
+    import subprocess
+    from kt_kernel.cli.i18n import get_lang
+    from kt_kernel.cli.utils.console import print_model_table, print_step
+    from kt_kernel.cli.utils.model_registry import get_registry
+
+    settings = get_settings()
+    registry = get_registry()
+
+    console.print()
+
+    # List mode
+    if list_models or model is None:
+        print_step(t("download_list_title"))
+        console.print()
+
+        models = registry.list_all()
+        model_dicts = []
+        for m in models:
+            lang = get_lang()
+            desc = m.description_zh if lang == "zh" and m.description_zh else m.description
+            model_dicts.append(
+                {
+                    "name": m.name,
+                    "hf_repo": m.hf_repo,
+                    "type": m.type,
+                    "gpu_vram_gb": m.gpu_vram_gb,
+                    "cpu_ram_gb": m.cpu_ram_gb,
+                }
+            )
+
+        print_model_table(model_dicts)
+        console.print()
+
+        if model is None:
+            console.print(f"[dim]{t('model_download_usage_hint')}[/dim]")
+            console.print()
+            return
+
+    # Search for model
+    print_step(t("download_searching", name=model))
+
+    # Check if it's a direct HuggingFace repo path
+    if "/" in model:
+        hf_repo = model
+        model_info = None
+        model_name = model.split("/")[-1]
+    else:
+        matches = registry.search(model)
+
+        if not matches:
+            print_error(t("run_model_not_found", name=model))
+            console.print()
+            console.print(t("model_download_list_hint"))
+            console.print(t("model_download_hf_hint"))
+            raise typer.Exit(1)
+
+        if len(matches) == 1:
+            model_info = matches[0]
+        else:
+            console.print()
+            print_info(t("download_multiple_found"))
+            choices = [f"{m.name} ({m.hf_repo})" for m in matches]
+            selected = prompt_choice(t("download_select"), choices)
+            idx = choices.index(selected)
+            model_info = matches[idx]
+
+        hf_repo = model_info.hf_repo
+        model_name = model_info.name
+
+    print_success(t("download_found", name=hf_repo))
+
+    # Determine download path
+    if path is None:
+        download_path = settings.models_dir / model_name.replace(" ", "-")
+    else:
+        download_path = path
+
+    console.print()
+    print_info(t("download_destination", path=str(download_path)))
+
+    # Check if already exists
+    if download_path.exists() and (download_path / "config.json").exists():
+        print_warning(t("download_already_exists", path=str(download_path)))
+        if not yes:
+            if not confirm(t("download_overwrite_prompt"), default=False):
+                raise typer.Abort()
+
+    # Confirm download
+    if not yes:
+        console.print()
+        if not confirm(t("prompt_continue")):
+            raise typer.Abort()
+
+    # Download using huggingface-cli
+    console.print()
+    print_step(t("download_starting"))
+
+    cmd = [
+        "huggingface-cli",
+        "download",
+        hf_repo,
+        "--local-dir",
+        str(download_path),
+    ]
+
+    if resume:
+        cmd.append("--resume-download")
+
+    # Add mirror if configured
+    mirror = settings.get("download.mirror", "")
+    if mirror:
+        cmd.extend(["--endpoint", mirror])
+
+    try:
+        process = subprocess.run(cmd, check=True)
+
+        console.print()
+        print_success(t("download_complete"))
+        console.print()
+        console.print(f"  {t('model_saved_to', path=download_path)}")
+        console.print()
+        console.print(f"  {t('model_start_with', name=model_name)}")
+        console.print()
+
+    except subprocess.CalledProcessError as e:
+        print_error(t("model_download_failed", error=str(e)))
+        raise typer.Exit(1)
+    except FileNotFoundError:
+        print_error(t("model_hf_cli_not_found"))
+        raise typer.Exit(1)
+
+
+@app.command(name="list")
+def list_models(
+    local_only: bool = typer.Option(False, "--local", help="Show only locally downloaded models"),
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Show detailed info including paths"),
+) -> None:
+    """List available models."""
+    from rich.table import Table
+    from kt_kernel.cli.utils.model_registry import get_registry
+
+    registry = get_registry()
+    console.print()
+
+    if local_only:
+        # Show only local models
+        local_models = registry.find_local_models()
+
+        if not local_models:
+            print_warning(t("model_no_local_models"))
+            console.print()
+            console.print(f"  {t('model_download_hint')} [cyan]kt model download <model-name>[/cyan]")
+            console.print()
+            return
+
+        table = Table(title=t("model_local_models_title"), show_header=True, header_style="bold")
+        table.add_column(t("model_column_model"), style="cyan", no_wrap=True)
+        if verbose:
+            table.add_column(t("model_column_local_path"), style="dim")
+
+        for model_info, model_path in local_models:
+            if verbose:
+                table.add_row(model_info.name, str(model_path))
+            else:
+                table.add_row(model_info.name)
+
+        console.print(table)
+    else:
+        # Show all registered models
+        all_models = registry.list_all()
+        local_models_dict = {m.name: p for m, p in registry.find_local_models()}
+
+        table = Table(title=t("model_available_models_title"), show_header=True, header_style="bold")
+        table.add_column(t("model_column_model"), style="cyan", no_wrap=True)
+        table.add_column(t("model_column_status"), justify="center")
+        if verbose:
+            table.add_column(t("model_column_local_path"), style="dim")
+
+        for model in all_models:
+            if model.name in local_models_dict:
+                status = f"[green]✓ {t('model_status_local')}[/green]"
+                local_path = str(local_models_dict[model.name])
+            else:
+                status = "[dim]-[/dim]"
+                local_path = f"[dim]{t('model_status_not_downloaded')}[/dim]"
+
+            if verbose:
+                table.add_row(model.name, status, local_path)
+            else:
+                table.add_row(model.name, status)
+
+        console.print(table)
+
+    console.print()
+
+
+@app.command(name="path-list")
+def path_list() -> None:
+    """List all configured model storage paths."""
+    settings = get_settings()
+    model_paths = settings.get_model_paths()
+
+    console.print()
+    console.print(f"[bold]{t('model_storage_paths_title')}:[/bold]\n")
+
+    for i, path in enumerate(model_paths, 1):
+        marker = "[green]✓[/green]" if path.exists() else "[red]✗[/red]"
+        console.print(f"  {marker} [{i}] {path}")
+
+    console.print()
+
+
+@app.command(name="path-add")
+def path_add(
+    path: str = typer.Argument(..., help="Path to add"),
+) -> None:
+    """Add a new model storage path."""
+    # Expand user home directory
+    path = os.path.expanduser(path)
+
+    # Check if path exists or can be created
+    path_obj = Path(path)
+    if not path_obj.exists():
+        console.print(f"[yellow]{t('model_path_not_exist', path=path)}[/yellow]")
+        if confirm(t("model_create_directory", path=path), default=True):
+            try:
+                path_obj.mkdir(parents=True, exist_ok=True)
+                console.print(f"[green]✓[/green] {t('model_created_directory', path=path)}")
+            except (OSError, PermissionError) as e:
+                print_error(t("model_create_dir_failed", error=str(e)))
+                raise typer.Exit(1)
+        else:
+            raise typer.Abort()
+
+    # Add to configuration
+    settings = get_settings()
+    settings.add_model_path(path)
+    print_success(t("model_path_added", path=path))
+
+
+@app.command(name="path-remove")
+def path_remove(
+    path: str = typer.Argument(..., help="Path to remove"),
+) -> None:
+    """Remove a model storage path from configuration."""
+    # Expand user home directory
+    path = os.path.expanduser(path)
+
+    settings = get_settings()
+    if settings.remove_model_path(path):
+        print_success(t("model_path_removed", path=path))
+    else:
+        print_error(t("model_path_not_found", path=path))
+        raise typer.Exit(1)
+
+
+@app.command(name="search")
+def search(
+    query: str = typer.Argument(..., help="Search query (model name or keyword)"),
+) -> None:
+    """Search for models in the registry."""
+    from rich.table import Table
+    from kt_kernel.cli.utils.model_registry import get_registry
+
+    registry = get_registry()
+    matches = registry.search(query)
+
+    console.print()
+
+    if not matches:
+        print_warning(t("model_search_no_results", query=query))
+        console.print()
+        return
+
+    table = Table(title=t("model_search_results_title", query=query), show_header=True)
+    table.add_column(t("model_column_name"), style="cyan")
+    table.add_column(t("model_column_hf_repo"), style="dim")
+    table.add_column(t("model_column_aliases"), style="yellow")
+
+    for model in matches:
+        aliases = ", ".join(model.aliases[:3])
+        if len(model.aliases) > 3:
+            aliases += f" +{len(model.aliases) - 3} more"
+        table.add_row(model.name, model.hf_repo, aliases)
+
+    console.print(table)
+    console.print()

kt-kernel/python/cli/commands/quant.py

@@ -0,0 +1,239 @@
+"""
+Quant command for kt-cli.
+
+Quantizes model weights for CPU inference.
+"""
+
+import subprocess
+import sys
+from enum import Enum
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import (
+    confirm,
+    console,
+    create_progress,
+    print_error,
+    print_info,
+    print_step,
+    print_success,
+    print_warning,
+)
+from kt_kernel.cli.utils.environment import detect_cpu_info
+
+
+class QuantMethod(str, Enum):
+    """Quantization method."""
+
+    INT4 = "int4"
+    INT8 = "int8"
+
+
+def quant(
+    model: str = typer.Argument(
+        ...,
+        help="Model name or path to quantize",
+    ),
+    method: QuantMethod = typer.Option(
+        QuantMethod.INT4,
+        "--method",
+        "-m",
+        help="Quantization method",
+    ),
+    output: Optional[Path] = typer.Option(
+        None,
+        "--output",
+        "-o",
+        help="Output path for quantized weights",
+    ),
+    input_type: str = typer.Option(
+        "fp8",
+        "--input-type",
+        "-i",
+        help="Input weight type (fp8, fp16, bf16)",
+    ),
+    cpu_threads: Optional[int] = typer.Option(
+        None,
+        "--cpu-threads",
+        help="Number of CPU threads for quantization",
+    ),
+    numa_nodes: Optional[int] = typer.Option(
+        None,
+        "--numa-nodes",
+        help="Number of NUMA nodes",
+    ),
+    no_merge: bool = typer.Option(
+        False,
+        "--no-merge",
+        help="Don't merge safetensor files",
+    ),
+    yes: bool = typer.Option(
+        False,
+        "--yes",
+        "-y",
+        help="Skip confirmation prompts",
+    ),
+) -> None:
+    """Quantize model weights for CPU inference."""
+    settings = get_settings()
+    console.print()
+
+    # Resolve input path
+    input_path = _resolve_input_path(model, settings)
+    if input_path is None:
+        print_error(t("quant_input_not_found", path=model))
+        raise typer.Exit(1)
+
+    print_info(t("quant_input_path", path=str(input_path)))
+
+    # Resolve output path
+    if output is None:
+        output = input_path.parent / f"{input_path.name}-{method.value.upper()}"
+
+    print_info(t("quant_output_path", path=str(output)))
+    print_info(t("quant_method", method=method.value.upper()))
+
+    # Detect CPU configuration
+    cpu = detect_cpu_info()
+    final_cpu_threads = cpu_threads or cpu.cores
+    final_numa_nodes = numa_nodes or cpu.numa_nodes
+
+    print_info(f"CPU threads: {final_cpu_threads}")
+    print_info(f"NUMA nodes: {final_numa_nodes}")
+
+    # Check if output exists
+    if output.exists():
+        print_warning(f"Output path already exists: {output}")
+        if not yes:
+            if not confirm("Overwrite?", default=False):
+                raise typer.Abort()
+
+    # Confirm
+    if not yes:
+        console.print()
+        console.print("[bold]Quantization Settings:[/bold]")
+        console.print(f"  Input: {input_path}")
+        console.print(f"  Output: {output}")
+        console.print(f"  Method: {method.value.upper()}")
+        console.print(f"  Input type: {input_type}")
+        console.print()
+        print_warning("Quantization may take 30-60 minutes depending on model size.")
+        console.print()
+
+        if not confirm(t("prompt_continue")):
+            raise typer.Abort()
+
+    # Find conversion script
+    kt_kernel_path = _find_kt_kernel_path()
+    if kt_kernel_path is None:
+        print_error("kt-kernel not found. Install with: kt install inference")
+        raise typer.Exit(1)
+
+    script_path = kt_kernel_path / "scripts" / "convert_cpu_weights.py"
+    if not script_path.exists():
+        print_error(f"Conversion script not found: {script_path}")
+        raise typer.Exit(1)
+
+    # Build command
+    cmd = [
+        sys.executable, str(script_path),
+        "--input-path", str(input_path),
+        "--input-type", input_type,
+        "--output", str(output),
+        "--quant-method", method.value,
+        "--cpuinfer-threads", str(final_cpu_threads),
+        "--threadpool-count", str(final_numa_nodes),
+    ]
+
+    if no_merge:
+        cmd.append("--no-merge-safetensor")
+
+    # Run quantization
+    console.print()
+    print_step(t("quant_starting"))
+    console.print()
+    console.print(f"[dim]$ {' '.join(cmd)}[/dim]")
+    console.print()
+
+    try:
+        process = subprocess.run(cmd)
+
+        if process.returncode == 0:
+            console.print()
+            print_success(t("quant_complete"))
+            console.print()
+            console.print(f"  Quantized weights saved to: {output}")
+            console.print()
+            console.print("  Use with:")
+            console.print(f"    kt run {model} --weights-path {output}")
+            console.print()
+        else:
+            print_error(f"Quantization failed with exit code {process.returncode}")
+            raise typer.Exit(process.returncode)
+
+    except FileNotFoundError as e:
+        print_error(f"Failed to run quantization: {e}")
+        raise typer.Exit(1)
+    except KeyboardInterrupt:
+        console.print()
+        print_warning("Quantization interrupted.")
+        raise typer.Exit(130)
+
+
+def _resolve_input_path(model: str, settings) -> Optional[Path]:
+    """Resolve the input model path."""
+    # Check if it's already a path
+    path = Path(model)
+    if path.exists() and (path / "config.json").exists():
+        return path
+
+    # Search in models directory
+    from kt_kernel.cli.utils.model_registry import get_registry
+
+    registry = get_registry()
+    matches = registry.search(model)
+
+    if matches:
+        model_info = matches[0]
+        # Try to find in all configured model directories
+        model_paths = settings.get_model_paths()
+
+        for models_dir in model_paths:
+            possible_paths = [
+                models_dir / model_info.name,
+                models_dir / model_info.name.lower(),
+                models_dir / model_info.hf_repo.split("/")[-1],
+            ]
+
+            for p in possible_paths:
+                if p.exists() and (p / "config.json").exists():
+                    return p
+
+    return None
+
+
+def _find_kt_kernel_path() -> Optional[Path]:
+    """Find the kt-kernel installation path."""
+    try:
+        import kt_kernel
+        return Path(kt_kernel.__file__).parent.parent
+    except ImportError:
+        pass
+
+    # Check common locations
+    possible_paths = [
+        Path.home() / "Projects" / "ktransformers" / "kt-kernel",
+        Path.cwd().parent / "kt-kernel",
+        Path.cwd() / "kt-kernel",
+    ]
+
+    for path in possible_paths:
+        if path.exists() and (path / "scripts").exists():
+            return path
+
+    return None

kt-kernel/python/cli/commands/run.py

@@ -0,0 +1,831 @@
+"""
+Run command for kt-cli.
+
+Starts the model inference server using SGLang + kt-kernel.
+"""
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import Optional
+
+import typer
+
+from kt_kernel.cli.config.settings import get_settings
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import (
+    confirm,
+    console,
+    print_api_info,
+    print_error,
+    print_info,
+    print_server_info,
+    print_step,
+    print_success,
+    print_warning,
+    prompt_choice,
+)
+from kt_kernel.cli.utils.environment import detect_cpu_info, detect_gpus, detect_ram_gb
+from kt_kernel.cli.utils.model_registry import MODEL_COMPUTE_FUNCTIONS, ModelInfo, get_registry
+
+
+def run(
+    model: Optional[str] = typer.Argument(
+        None,
+        help="Model name or path (e.g., deepseek-v3, qwen3-30b). If not specified, shows interactive selection.",
+    ),
+    host: str = typer.Option(
+        None,
+        "--host",
+        "-H",
+        help="Server host address",
+    ),
+    port: int = typer.Option(
+        None,
+        "--port",
+        "-p",
+        help="Server port",
+    ),
+    # CPU/GPU configuration
+    gpu_experts: Optional[int] = typer.Option(
+        None,
+        "--gpu-experts",
+        help="Number of GPU experts per layer",
+    ),
+    cpu_threads: Optional[int] = typer.Option(
+        None,
+        "--cpu-threads",
+        help="Number of CPU inference threads (kt-cpuinfer, defaults to 80% of CPU cores)",
+    ),
+    numa_nodes: Optional[int] = typer.Option(
+        None,
+        "--numa-nodes",
+        help="Number of NUMA nodes",
+    ),
+    tensor_parallel_size: Optional[int] = typer.Option(
+        None,
+        "--tensor-parallel-size",
+        "--tp",
+        help="Tensor parallel size (number of GPUs)",
+    ),
+    # Model paths
+    model_path: Optional[Path] = typer.Option(
+        None,
+        "--model-path",
+        help="Custom model path",
+    ),
+    weights_path: Optional[Path] = typer.Option(
+        None,
+        "--weights-path",
+        help="Custom quantized weights path",
+    ),
+    # KT-kernel options
+    kt_method: Optional[str] = typer.Option(
+        None,
+        "--kt-method",
+        help="KT quantization method (AMXINT4, RAWFP8, etc.)",
+    ),
+    kt_gpu_prefill_token_threshold: Optional[int] = typer.Option(
+        None,
+        "--kt-gpu-prefill-threshold",
+        help="GPU prefill token threshold for kt-kernel",
+    ),
+    # SGLang options
+    attention_backend: Optional[str] = typer.Option(
+        None,
+        "--attention-backend",
+        help="Attention backend (triton, flashinfer)",
+    ),
+    max_total_tokens: Optional[int] = typer.Option(
+        None,
+        "--max-total-tokens",
+        help="Maximum total tokens",
+    ),
+    max_running_requests: Optional[int] = typer.Option(
+        None,
+        "--max-running-requests",
+        help="Maximum running requests",
+    ),
+    chunked_prefill_size: Optional[int] = typer.Option(
+        None,
+        "--chunked-prefill-size",
+        help="Chunked prefill size",
+    ),
+    mem_fraction_static: Optional[float] = typer.Option(
+        None,
+        "--mem-fraction-static",
+        help="Memory fraction for static allocation",
+    ),
+    watchdog_timeout: Optional[int] = typer.Option(
+        None,
+        "--watchdog-timeout",
+        help="Watchdog timeout in seconds",
+    ),
+    served_model_name: Optional[str] = typer.Option(
+        None,
+        "--served-model-name",
+        help="Custom model name for API responses",
+    ),
+    # Performance flags
+    disable_shared_experts_fusion: Optional[bool] = typer.Option(
+        None,
+        "--disable-shared-experts-fusion/--enable-shared-experts-fusion",
+        help="Disable/enable shared experts fusion",
+    ),
+    # Other options
+    quantize: bool = typer.Option(
+        False,
+        "--quantize",
+        "-q",
+        help="Quantize model if weights not found",
+    ),
+    advanced: bool = typer.Option(
+        False,
+        "--advanced",
+        help="Show advanced options",
+    ),
+    dry_run: bool = typer.Option(
+        False,
+        "--dry-run",
+        help="Show command without executing",
+    ),
+) -> None:
+    """Start model inference server."""
+    # Check if SGLang is installed before proceeding
+    from kt_kernel.cli.utils.sglang_checker import (
+        check_sglang_installation,
+        check_sglang_kt_kernel_support,
+        print_sglang_install_instructions,
+        print_sglang_kt_kernel_instructions,
+    )
+
+    sglang_info = check_sglang_installation()
+    if not sglang_info["installed"]:
+        console.print()
+        print_error(t("sglang_not_found"))
+        console.print()
+        print_sglang_install_instructions()
+        raise typer.Exit(1)
+
+    # Check if SGLang supports kt-kernel (has --kt-gpu-prefill-token-threshold parameter)
+    kt_kernel_support = check_sglang_kt_kernel_support()
+    if not kt_kernel_support["supported"]:
+        console.print()
+        print_error(t("sglang_kt_kernel_not_supported"))
+        console.print()
+        print_sglang_kt_kernel_instructions()
+        raise typer.Exit(1)
+
+    settings = get_settings()
+    registry = get_registry()
+
+    console.print()
+
+    # If no model specified, show interactive selection
+    if model is None:
+        model = _interactive_model_selection(registry, settings)
+        if model is None:
+            raise typer.Exit(0)
+
+    # Step 1: Detect hardware
+    print_step(t("run_detecting_hardware"))
+    gpus = detect_gpus()
+    cpu = detect_cpu_info()
+    ram = detect_ram_gb()
+
+    if gpus:
+        gpu_info = f"{gpus[0].name} ({gpus[0].vram_gb}GB VRAM)"
+        if len(gpus) > 1:
+            gpu_info += f" + {len(gpus) - 1} more"
+        print_info(t("run_gpu_info", name=gpus[0].name, vram=gpus[0].vram_gb))
+    else:
+        print_warning(t("doctor_gpu_not_found"))
+        gpu_info = "None"
+
+    print_info(t("run_cpu_info", name=cpu.name, cores=cpu.cores, numa=cpu.numa_nodes))
+    print_info(t("run_ram_info", total=int(ram)))
+
+    # Step 2: Resolve model
+    console.print()
+    print_step(t("run_checking_model"))
+
+    model_info = None
+    resolved_model_path = model_path
+
+    # Check if model is a path
+    if Path(model).exists():
+        resolved_model_path = Path(model)
+        print_info(t("run_model_path", path=str(resolved_model_path)))
+
+        # Try to infer model type from path to use default configurations
+        # Check directory name against known models
+        dir_name = resolved_model_path.name.lower()
+        for registered_model in registry.list_all():
+            # Check if directory name matches model name or aliases
+            if dir_name == registered_model.name.lower():
+                model_info = registered_model
+                print_info(f"Detected model type: {registered_model.name}")
+                break
+            for alias in registered_model.aliases:
+                if dir_name == alias.lower() or alias.lower() in dir_name:
+                    model_info = registered_model
+                    print_info(f"Detected model type: {registered_model.name}")
+                    break
+            if model_info:
+                break
+
+        # Also check HuggingFace repo format (org--model)
+        if not model_info:
+            for registered_model in registry.list_all():
+                repo_slug = registered_model.hf_repo.replace("/", "--").lower()
+                if repo_slug in dir_name or dir_name in repo_slug:
+                    model_info = registered_model
+                    print_info(f"Detected model type: {registered_model.name}")
+                    break
+
+        if not model_info:
+            print_warning("Could not detect model type from path. Using default parameters.")
+            console.print("  [dim]Tip: Use model name (e.g., 'kt run m2') to apply optimized configurations[/dim]")
+    else:
+        # Search in registry
+        matches = registry.search(model)
+
+        if not matches:
+            print_error(t("run_model_not_found", name=model))
+            console.print()
+            console.print("Available models:")
+            for m in registry.list_all()[:5]:
+                console.print(f"  - {m.name} ({', '.join(m.aliases[:2])})")
+            raise typer.Exit(1)
+
+        if len(matches) == 1:
+            model_info = matches[0]
+        else:
+            # Multiple matches - prompt user
+            console.print()
+            print_info(t("run_multiple_matches"))
+            choices = [f"{m.name} ({m.hf_repo})" for m in matches]
+            selected = prompt_choice(t("run_select_model"), choices)
+            idx = choices.index(selected)
+            model_info = matches[idx]
+
+        # Find model path
+        if model_path is None:
+            resolved_model_path = _find_model_path(model_info, settings)
+            if resolved_model_path is None:
+                print_error(t("run_model_not_found", name=model_info.name))
+                console.print()
+                console.print(
+                    f"  Download with: kt download {model_info.aliases[0] if model_info.aliases else model_info.name}"
+                )
+                raise typer.Exit(1)
+
+        print_info(t("run_model_path", path=str(resolved_model_path)))
+
+    # Step 3: Check quantized weights (only if explicitly requested)
+    resolved_weights_path = None
+
+    # Only use quantized weights if explicitly specified by user
+    if weights_path is not None:
+        # User explicitly specified weights path
+        resolved_weights_path = weights_path
+        if not resolved_weights_path.exists():
+            print_error(t("run_weights_not_found"))
+            console.print(f"  Path: {resolved_weights_path}")
+            raise typer.Exit(1)
+        print_info(f"Using quantized weights: {resolved_weights_path}")
+    elif quantize:
+        # User requested quantization
+        console.print()
+        print_step(t("run_quantizing"))
+        # TODO: Implement quantization
+        print_warning("Quantization not yet implemented. Please run 'kt quant' manually.")
+        raise typer.Exit(1)
+    else:
+        # Default: use original precision model without quantization
+        console.print()
+        print_info("Using original precision model (no quantization)")
+
+    # Step 4: Build command
+    # Resolve all parameters (CLI > model defaults > config > auto-detect)
+    final_host = host or settings.get("server.host", "0.0.0.0")
+    final_port = port or settings.get("server.port", 30000)
+
+    # Get defaults from model info if available
+    model_defaults = model_info.default_params if model_info else {}
+
+    # Determine tensor parallel size first (needed for GPU expert calculation)
+    # Priority: CLI > model defaults > config > auto-detect (with model constraints)
+
+    # Check if explicitly specified by user or configuration
+    explicitly_specified = (
+        tensor_parallel_size  # CLI argument (highest priority)
+        or model_defaults.get("tensor-parallel-size")  # Model defaults
+        or settings.get("inference.tensor_parallel_size")  # Config file
+    )
+
+    if explicitly_specified:
+        # Use explicitly specified value
+        requested_tensor_parallel_size = explicitly_specified
+    else:
+        # Auto-detect from GPUs, considering model's max constraint
+        detected_gpu_count = len(gpus) if gpus else 1
+        if model_info and model_info.max_tensor_parallel_size is not None:
+            # Automatically limit to model's maximum to use as many GPUs as possible
+            requested_tensor_parallel_size = min(detected_gpu_count, model_info.max_tensor_parallel_size)
+        else:
+            requested_tensor_parallel_size = detected_gpu_count
+
+    # Apply model's max_tensor_parallel_size constraint if explicitly specified value exceeds it
+    final_tensor_parallel_size = requested_tensor_parallel_size
+    if model_info and model_info.max_tensor_parallel_size is not None:
+        if requested_tensor_parallel_size > model_info.max_tensor_parallel_size:
+            console.print()
+            print_warning(
+                f"Model {model_info.name} only supports up to {model_info.max_tensor_parallel_size}-way "
+                f"tensor parallelism, but {requested_tensor_parallel_size} was requested. "
+                f"Reducing to {model_info.max_tensor_parallel_size}."
+            )
+            final_tensor_parallel_size = model_info.max_tensor_parallel_size
+
+    # CPU/GPU configuration with smart defaults
+    # kt-cpuinfer: default to 80% of total CPU threads (cores * NUMA nodes)
+    total_threads = cpu.cores * cpu.numa_nodes
+    final_cpu_threads = (
+        cpu_threads
+        or model_defaults.get("kt-cpuinfer")
+        or settings.get("inference.cpu_threads")
+        or int(total_threads * 0.8)
+    )
+
+    # kt-threadpool-count: default to NUMA node count
+    final_numa_nodes = (
+        numa_nodes
+        or model_defaults.get("kt-threadpool-count")
+        or settings.get("inference.numa_nodes")
+        or cpu.numa_nodes
+    )
+
+    # kt-num-gpu-experts: use model-specific computation if available and not explicitly set
+    if gpu_experts is not None:
+        # User explicitly set it
+        final_gpu_experts = gpu_experts
+    elif model_info and model_info.name in MODEL_COMPUTE_FUNCTIONS and gpus:
+        # Use model-specific computation function (only if GPUs detected)
+        vram_per_gpu = gpus[0].vram_gb
+        compute_func = MODEL_COMPUTE_FUNCTIONS[model_info.name]
+        final_gpu_experts = compute_func(final_tensor_parallel_size, vram_per_gpu)
+        console.print()
+        print_info(
+            f"Auto-computed kt-num-gpu-experts: {final_gpu_experts} (TP={final_tensor_parallel_size}, VRAM={vram_per_gpu}GB per GPU)"
+        )
+    else:
+        # Fall back to defaults
+        final_gpu_experts = model_defaults.get("kt-num-gpu-experts") or settings.get("inference.gpu_experts", 1)
+
+    # KT-kernel options
+    final_kt_method = kt_method or model_defaults.get("kt-method") or settings.get("inference.kt_method", "AMXINT4")
+    final_kt_gpu_prefill_threshold = (
+        kt_gpu_prefill_token_threshold
+        or model_defaults.get("kt-gpu-prefill-token-threshold")
+        or settings.get("inference.kt_gpu_prefill_token_threshold", 4096)
+    )
+
+    # SGLang options
+    final_attention_backend = (
+        attention_backend
+        or model_defaults.get("attention-backend")
+        or settings.get("inference.attention_backend", "triton")
+    )
+    final_max_total_tokens = (
+        max_total_tokens or model_defaults.get("max-total-tokens") or settings.get("inference.max_total_tokens", 40000)
+    )
+    final_max_running_requests = (
+        max_running_requests
+        or model_defaults.get("max-running-requests")
+        or settings.get("inference.max_running_requests", 32)
+    )
+    final_chunked_prefill_size = (
+        chunked_prefill_size
+        or model_defaults.get("chunked-prefill-size")
+        or settings.get("inference.chunked_prefill_size", 4096)
+    )
+    final_mem_fraction_static = (
+        mem_fraction_static
+        or model_defaults.get("mem-fraction-static")
+        or settings.get("inference.mem_fraction_static", 0.98)
+    )
+    final_watchdog_timeout = (
+        watchdog_timeout or model_defaults.get("watchdog-timeout") or settings.get("inference.watchdog_timeout", 3000)
+    )
+    final_served_model_name = (
+        served_model_name or model_defaults.get("served-model-name") or settings.get("inference.served_model_name", "")
+    )
+
+    # Performance flags
+    if disable_shared_experts_fusion is not None:
+        final_disable_shared_experts_fusion = disable_shared_experts_fusion
+    elif "disable-shared-experts-fusion" in model_defaults:
+        final_disable_shared_experts_fusion = model_defaults["disable-shared-experts-fusion"]
+    else:
+        final_disable_shared_experts_fusion = settings.get("inference.disable_shared_experts_fusion", False)
+
+    # Pass all model default params to handle any extra parameters
+    extra_params = model_defaults if model_info else {}
+
+    cmd = _build_sglang_command(
+        model_path=resolved_model_path,
+        weights_path=resolved_weights_path,
+        model_info=model_info,
+        host=final_host,
+        port=final_port,
+        gpu_experts=final_gpu_experts,
+        cpu_threads=final_cpu_threads,
+        numa_nodes=final_numa_nodes,
+        tensor_parallel_size=final_tensor_parallel_size,
+        kt_method=final_kt_method,
+        kt_gpu_prefill_threshold=final_kt_gpu_prefill_threshold,
+        attention_backend=final_attention_backend,
+        max_total_tokens=final_max_total_tokens,
+        max_running_requests=final_max_running_requests,
+        chunked_prefill_size=final_chunked_prefill_size,
+        mem_fraction_static=final_mem_fraction_static,
+        watchdog_timeout=final_watchdog_timeout,
+        served_model_name=final_served_model_name,
+        disable_shared_experts_fusion=final_disable_shared_experts_fusion,
+        settings=settings,
+        extra_model_params=extra_params,
+    )
+
+    # Prepare environment variables
+    env = os.environ.copy()
+    # Add environment variables from advanced.env
+    env.update(settings.get_env_vars())
+    # Add environment variables from inference.env
+    inference_env = settings.get("inference.env", {})
+    if isinstance(inference_env, dict):
+        env.update({k: str(v) for k, v in inference_env.items()})
+
+    # Step 5: Show configuration summary
+    console.print()
+    print_step("Configuration")
+
+    # Model info
+    if model_info:
+        console.print(f"  Model: [bold]{model_info.name}[/bold]")
+    else:
+        console.print(f"  Model: [bold]{resolved_model_path.name}[/bold]")
+
+    console.print(f"  Path: [dim]{resolved_model_path}[/dim]")
+
+    # Key parameters
+    console.print()
+    console.print(f"  GPU Experts: [cyan]{final_gpu_experts}[/cyan] per layer")
+    console.print(f"  CPU Threads (kt-cpuinfer): [cyan]{final_cpu_threads}[/cyan]")
+    console.print(f"  NUMA Nodes (kt-threadpool-count): [cyan]{final_numa_nodes}[/cyan]")
+    console.print(f"  Tensor Parallel: [cyan]{final_tensor_parallel_size}[/cyan]")
+    console.print(f"  Method: [cyan]{final_kt_method}[/cyan]")
+    console.print(f"  Attention: [cyan]{final_attention_backend}[/cyan]")
+
+    # Weights info
+    if resolved_weights_path:
+        console.print()
+        console.print(f"  Quantized weights: [yellow]{resolved_weights_path}[/yellow]")
+
+    console.print()
+    console.print(f"  Server: [green]http://{final_host}:{final_port}[/green]")
+    console.print()
+
+    # Step 6: Show or execute
+    if dry_run:
+        console.print()
+        console.print("[bold]Command:[/bold]")
+        console.print()
+        console.print(f"  [dim]{' '.join(cmd)}[/dim]")
+        console.print()
+        return
+
+    # Execute with prepared environment variables
+    # Don't print "Server started" or API info here - let sglang's logs speak for themselves
+    # The actual startup takes time and these messages are misleading
+
+    # Print the command being executed
+    console.print()
+    console.print("[bold]Launching server with command:[/bold]")
+    console.print()
+    console.print(f"  [dim]{' '.join(cmd)}[/dim]")
+    console.print()
+
+    try:
+        # Execute directly without intercepting output or signals
+        # This allows direct output to terminal and Ctrl+C to work naturally
+        process = subprocess.run(cmd, env=env)
+        sys.exit(process.returncode)
+
+    except FileNotFoundError:
+        from kt_kernel.cli.utils.sglang_checker import print_sglang_install_instructions
+
+        print_error(t("sglang_not_found"))
+        console.print()
+        print_sglang_install_instructions()
+        raise typer.Exit(1)
+    except Exception as e:
+        print_error(f"Failed to start server: {e}")
+        raise typer.Exit(1)
+
+
+def _find_model_path(model_info: ModelInfo, settings) -> Optional[Path]:
+    """Find the model path on disk by searching all configured model paths."""
+    model_paths = settings.get_model_paths()
+
+    # Search in all configured model directories
+    for models_dir in model_paths:
+        # Check common path patterns
+        possible_paths = [
+            models_dir / model_info.name,
+            models_dir / model_info.name.lower(),
+            models_dir / model_info.name.replace(" ", "-"),
+            models_dir / model_info.hf_repo.split("/")[-1],
+            models_dir / model_info.hf_repo.replace("/", "--"),
+        ]
+
+        # Add alias-based paths
+        for alias in model_info.aliases:
+            possible_paths.append(models_dir / alias)
+            possible_paths.append(models_dir / alias.lower())
+
+        for path in possible_paths:
+            if path.exists() and (path / "config.json").exists():
+                return path
+
+    return None
+
+
+def _find_weights_path(model_info: ModelInfo, settings) -> Optional[Path]:
+    """Find the quantized weights path on disk by searching all configured paths."""
+    model_paths = settings.get_model_paths()
+    weights_dir = settings.weights_dir
+
+    # Check common patterns
+    base_names = [
+        model_info.name,
+        model_info.name.lower(),
+        model_info.hf_repo.split("/")[-1],
+    ]
+
+    suffixes = ["-INT4", "-int4", "_INT4", "_int4", "-quant", "-quantized"]
+
+    # Prepare search directories
+    search_dirs = [weights_dir] if weights_dir else []
+    search_dirs.extend(model_paths)
+
+    for base in base_names:
+        for suffix in suffixes:
+            for dir_path in search_dirs:
+                if dir_path:
+                    path = dir_path / f"{base}{suffix}"
+                    if path.exists():
+                        return path
+
+    return None
+
+
+def _build_sglang_command(
+    model_path: Path,
+    weights_path: Optional[Path],
+    model_info: Optional[ModelInfo],
+    host: str,
+    port: int,
+    gpu_experts: int,
+    cpu_threads: int,
+    numa_nodes: int,
+    tensor_parallel_size: int,
+    kt_method: str,
+    kt_gpu_prefill_threshold: int,
+    attention_backend: str,
+    max_total_tokens: int,
+    max_running_requests: int,
+    chunked_prefill_size: int,
+    mem_fraction_static: float,
+    watchdog_timeout: int,
+    served_model_name: str,
+    disable_shared_experts_fusion: bool,
+    settings,
+    extra_model_params: Optional[dict] = None,  # New parameter for additional params
+) -> list[str]:
+    """Build the SGLang launch command."""
+    cmd = [
+        sys.executable,
+        "-m",
+        "sglang.launch_server",
+        "--host",
+        host,
+        "--port",
+        str(port),
+        "--model",
+        str(model_path),
+    ]
+
+    # Add kt-kernel options
+    # kt-kernel is needed for:
+    # 1. Quantized models (when weights_path is provided)
+    # 2. MoE models with CPU offloading (when kt-cpuinfer > 0 or kt-num-gpu-experts is configured)
+    use_kt_kernel = False
+
+    # Check if we should use kt-kernel
+    if weights_path:
+        # Quantized model - always use kt-kernel
+        use_kt_kernel = True
+    elif cpu_threads > 0 or gpu_experts > 1:
+        # CPU offloading configured - use kt-kernel
+        use_kt_kernel = True
+    elif model_info and model_info.type == "moe":
+        # MoE model - likely needs kt-kernel for expert offloading
+        use_kt_kernel = True
+
+    if use_kt_kernel:
+        # Add kt-weight-path: use quantized weights if available, otherwise use model path
+        weight_path_to_use = weights_path if weights_path else model_path
+
+        # Add kt-kernel configuration
+        cmd.extend(
+            [
+                "--kt-weight-path",
+                str(weight_path_to_use),
+                "--kt-cpuinfer",
+                str(cpu_threads),
+                "--kt-threadpool-count",
+                str(numa_nodes),
+                "--kt-num-gpu-experts",
+                str(gpu_experts),
+                "--kt-method",
+                kt_method,
+                "--kt-gpu-prefill-token-threshold",
+                str(kt_gpu_prefill_threshold),
+            ]
+        )
+
+    # Add SGLang options
+    cmd.extend(
+        [
+            "--attention-backend",
+            attention_backend,
+            "--trust-remote-code",
+            "--mem-fraction-static",
+            str(mem_fraction_static),
+            "--chunked-prefill-size",
+            str(chunked_prefill_size),
+            "--max-running-requests",
+            str(max_running_requests),
+            "--max-total-tokens",
+            str(max_total_tokens),
+            "--watchdog-timeout",
+            str(watchdog_timeout),
+            "--enable-mixed-chunk",
+            "--tensor-parallel-size",
+            str(tensor_parallel_size),
+            "--enable-p2p-check",
+        ]
+    )
+
+    # Add served model name if specified
+    if served_model_name:
+        cmd.extend(["--served-model-name", served_model_name])
+
+    # Add performance flags
+    if disable_shared_experts_fusion:
+        cmd.append("--disable-shared-experts-fusion")
+
+    # Add any extra parameters from model defaults that weren't explicitly handled
+    if extra_model_params:
+        # List of parameters already handled above
+        handled_params = {
+            "kt-num-gpu-experts",
+            "kt-cpuinfer",
+            "kt-threadpool-count",
+            "kt-method",
+            "kt-gpu-prefill-token-threshold",
+            "attention-backend",
+            "tensor-parallel-size",
+            "max-total-tokens",
+            "max-running-requests",
+            "chunked-prefill-size",
+            "mem-fraction-static",
+            "watchdog-timeout",
+            "served-model-name",
+            "disable-shared-experts-fusion",
+        }
+
+        for key, value in extra_model_params.items():
+            if key not in handled_params:
+                # Add unhandled parameters dynamically
+                cmd.append(f"--{key}")
+                if isinstance(value, bool):
+                    # Boolean flags don't need a value
+                    if not value:
+                        # For False boolean, skip the flag entirely
+                        cmd.pop()  # Remove the flag we just added
+                else:
+                    cmd.append(str(value))
+
+    # Add extra args from settings
+    extra_args = settings.get("advanced.sglang_args", [])
+    if extra_args:
+        cmd.extend(extra_args)
+
+    return cmd
+
+
+def _interactive_model_selection(registry, settings) -> Optional[str]:
+    """Show interactive model selection interface.
+
+    Returns:
+        Selected model name or None if cancelled.
+    """
+    from rich.panel import Panel
+    from rich.table import Table
+    from rich.prompt import Prompt
+
+    from kt_kernel.cli.i18n import get_lang
+
+    lang = get_lang()
+
+    # Find local models first
+    local_models = registry.find_local_models()
+
+    # Get all registered models
+    all_models = registry.list_all()
+
+    console.print()
+    console.print(
+        Panel.fit(
+            t("run_select_model_title"),
+            border_style="cyan",
+        )
+    )
+    console.print()
+
+    # Build choices list
+    choices = []
+    choice_map = {}  # index -> model name
+
+    # Section 1: Local models (downloaded)
+    if local_models:
+        console.print(f"[bold green]{t('run_local_models')}[/bold green]")
+        console.print()
+
+        for i, (model_info, path) in enumerate(local_models, 1):
+            desc = model_info.description_zh if lang == "zh" else model_info.description
+            short_desc = desc[:50] + "..." if len(desc) > 50 else desc
+            console.print(f"  [cyan][{i}][/cyan] [bold]{model_info.name}[/bold]")
+            console.print(f"      [dim]{short_desc}[/dim]")
+            console.print(f"      [dim]{path}[/dim]")
+            choices.append(str(i))
+            choice_map[str(i)] = model_info.name
+
+        console.print()
+
+    # Section 2: All registered models (for reference)
+    start_idx = len(local_models) + 1
+    console.print(f"[bold yellow]{t('run_registered_models')}[/bold yellow]")
+    console.print()
+
+    # Filter out already shown local models
+    local_model_names = {m.name for m, _ in local_models}
+
+    for i, model_info in enumerate(all_models, start_idx):
+        if model_info.name in local_model_names:
+            continue
+
+        desc = model_info.description_zh if lang == "zh" else model_info.description
+        short_desc = desc[:50] + "..." if len(desc) > 50 else desc
+        console.print(f"  [cyan][{i}][/cyan] [bold]{model_info.name}[/bold]")
+        console.print(f"      [dim]{short_desc}[/dim]")
+        console.print(f"      [dim]{model_info.hf_repo}[/dim]")
+        choices.append(str(i))
+        choice_map[str(i)] = model_info.name
+
+    console.print()
+
+    # Add cancel option
+    cancel_idx = str(len(choices) + 1)
+    console.print(f"  [cyan][{cancel_idx}][/cyan] [dim]{t('cancel')}[/dim]")
+    choices.append(cancel_idx)
+    console.print()
+
+    # Prompt for selection
+    try:
+        selection = Prompt.ask(
+            t("run_select_model_prompt"),
+            choices=choices,
+            default="1" if choices else cancel_idx,
+        )
+    except KeyboardInterrupt:
+        console.print()
+        return None
+
+    if selection == cancel_idx:
+        return None
+
+    return choice_map.get(selection)

kt-kernel/python/cli/commands/sft.py

@@ -0,0 +1,52 @@
+"""
+SFT command for kt-cli.
+
+Fine-tuning with LlamaFactory integration.
+"""
+
+import typer
+
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import console
+
+app = typer.Typer(help="Fine-tuning with LlamaFactory (coming soon)")
+
+
+@app.callback(invoke_without_command=True)
+def callback(ctx: typer.Context) -> None:
+    """Fine-tuning commands (coming soon)."""
+    if ctx.invoked_subcommand is None:
+        console.print()
+        console.print(f"[yellow]{t('feature_coming_soon')}[/yellow]")
+        console.print()
+        console.print("[dim]kt sft train   - Train a model[/dim]")
+        console.print("[dim]kt sft chat    - Chat with a trained model[/dim]")
+        console.print("[dim]kt sft export  - Export a trained model[/dim]")
+        console.print()
+
+
+@app.command(name="train")
+def train() -> None:
+    """Train a model using LlamaFactory (coming soon)."""
+    console.print()
+    console.print(f"[yellow]{t('feature_coming_soon')}[/yellow]")
+    console.print()
+    raise typer.Exit(0)
+
+
+@app.command(name="chat")
+def chat() -> None:
+    """Chat with a trained model using LlamaFactory (coming soon)."""
+    console.print()
+    console.print(f"[yellow]{t('feature_coming_soon')}[/yellow]")
+    console.print()
+    raise typer.Exit(0)
+
+
+@app.command(name="export")
+def export() -> None:
+    """Export a trained model using LlamaFactory (coming soon)."""
+    console.print()
+    console.print(f"[yellow]{t('feature_coming_soon')}[/yellow]")
+    console.print()
+    raise typer.Exit(0)

kt-kernel/python/cli/commands/version.py

@@ -0,0 +1,118 @@
+"""
+Version command for kt-cli.
+
+Displays version information for kt-cli and related packages.
+"""
+
+import platform
+from typing import Optional
+
+import typer
+
+from kt_kernel.cli import __version__
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import console, print_version_table
+from kt_kernel.cli.utils.environment import detect_cuda_version, get_installed_package_version
+
+
+def _get_sglang_info() -> str:
+    """Get sglang version and installation source information."""
+    try:
+        import sglang
+
+        version = getattr(sglang, "__version__", None)
+
+        if not version:
+            version = get_installed_package_version("sglang")
+
+        if not version:
+            return t("version_not_installed")
+
+        # Try to detect installation source
+        from pathlib import Path
+        import subprocess
+
+        if hasattr(sglang, "__file__") and sglang.__file__:
+            location = Path(sglang.__file__).parent.parent
+            git_dir = location / ".git"
+
+            if git_dir.exists():
+                # Installed from git (editable install)
+                try:
+                    # Get remote URL
+                    result = subprocess.run(
+                        ["git", "remote", "get-url", "origin"],
+                        cwd=location,
+                        capture_output=True,
+                        text=True,
+                        timeout=2,
+                    )
+                    if result.returncode == 0:
+                        remote_url = result.stdout.strip()
+                        # Simplify GitHub URLs
+                        if "github.com" in remote_url:
+                            repo_name = remote_url.split("/")[-1].replace(".git", "")
+                            owner = remote_url.split("/")[-2]
+                            return f"{version} [dim](GitHub: {owner}/{repo_name})[/dim]"
+                        return f"{version} [dim](Git: {remote_url})[/dim]"
+                except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
+                    pass
+
+        # Default: installed from PyPI
+        return f"{version} [dim](PyPI)[/dim]"
+
+    except ImportError:
+        return t("version_not_installed")
+
+
+def version(
+    verbose: bool = typer.Option(False, "--verbose", "-v", help="Show detailed version info"),
+) -> None:
+    """Show version information."""
+    console.print(f"\n[bold]{t('version_info')}[/bold] v{__version__}\n")
+
+    # Basic info
+    versions = {
+        t("version_python"): platform.python_version(),
+        t("version_platform"): f"{platform.system()} {platform.release()}",
+    }
+
+    # CUDA version
+    cuda_version = detect_cuda_version()
+    versions[t("version_cuda")] = cuda_version or t("version_cuda_not_found")
+
+    print_version_table(versions)
+
+    # Always show key packages with installation source
+    console.print("\n[bold]Packages:[/bold]\n")
+
+    sglang_info = _get_sglang_info()
+    key_packages = {
+        t("version_kt_kernel"): get_installed_package_version("kt-kernel") or t("version_not_installed"),
+        t("version_sglang"): sglang_info,
+    }
+
+    print_version_table(key_packages)
+
+    # Show SGLang installation hint if not installed
+    if sglang_info == t("version_not_installed"):
+        from kt_kernel.cli.utils.sglang_checker import print_sglang_install_instructions
+
+        console.print()
+        print_sglang_install_instructions()
+
+    if verbose:
+        console.print("\n[bold]Additional Packages:[/bold]\n")
+
+        package_versions = {
+            t("version_ktransformers"): get_installed_package_version("ktransformers") or t("version_not_installed"),
+            t("version_llamafactory"): get_installed_package_version("llamafactory") or t("version_not_installed"),
+            "typer": get_installed_package_version("typer") or t("version_not_installed"),
+            "rich": get_installed_package_version("rich") or t("version_not_installed"),
+            "torch": get_installed_package_version("torch") or t("version_not_installed"),
+            "transformers": get_installed_package_version("transformers") or t("version_not_installed"),
+        }
+
+        print_version_table(package_versions)
+
+    console.print()