Kt minimax (#1742)

[feat]: fp8 kernel and kt-cli support

kt-kernel/python/__init__.py

@@ -37,11 +37,13 @@ from __future__ import annotations
 
 # Detect CPU and load optimal extension variant
 from ._cpu_detect import initialize as _initialize_cpu
+
 _kt_kernel_ext, __cpu_variant__ = _initialize_cpu()
 
 # Make the extension module available to other modules in this package
 import sys
-sys.modules['kt_kernel_ext'] = _kt_kernel_ext
+
+sys.modules["kt_kernel_ext"] = _kt_kernel_ext
 
 # Also expose kt_kernel_ext as an attribute for backward compatibility
 kt_kernel_ext = _kt_kernel_ext
@@ -53,25 +55,28 @@ from .experts import KTMoEWrapper
 try:
     # Try to get version from installed package metadata (works in installed environment)
     from importlib.metadata import version, PackageNotFoundError
+
     try:
-        __version__ = version('kt-kernel')
+        __version__ = version("kt-kernel")
     except PackageNotFoundError:
         # Package not installed, try to read from source tree version.py
         import os
-        _root_version_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'version.py')
+
+        _root_version_file = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "version.py")
         if os.path.exists(_root_version_file):
             _version_ns = {}
-            with open(_root_version_file, 'r', encoding='utf-8') as f:
+            with open(_root_version_file, "r", encoding="utf-8") as f:
                 exec(f.read(), _version_ns)
-            __version__ = _version_ns.get('__version__', '0.4.3')
+            __version__ = _version_ns.get("__version__", "0.4.3")
         else:
             __version__ = "0.4.3"
 except ImportError:
     # Python < 3.8, fallback to pkg_resources or hardcoded version
     try:
         from pkg_resources import get_distribution, DistributionNotFound
+
         try:
-            __version__ = get_distribution('kt-kernel').version
+            __version__ = get_distribution("kt-kernel").version
         except DistributionNotFound:
             __version__ = "0.4.3"
     except ImportError:

kt-kernel/python/_cpu_detect.py

@@ -17,6 +17,7 @@ Example:
     >>> os.environ['KT_KERNEL_CPU_VARIANT'] = 'avx2'
     >>> import kt_kernel  # Will use AVX2 variant
 """
+
 import os
 import sys
 from pathlib import Path
@@ -35,82 +36,82 @@ def detect_cpu_features():
         str: 'amx', 'avx512', or 'avx2'
     """
     # Check environment override
-    variant = os.environ.get('KT_KERNEL_CPU_VARIANT', '').lower()
-    if variant in ['amx', 'avx512', 'avx2']:
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+    variant = os.environ.get("KT_KERNEL_CPU_VARIANT", "").lower()
+    if variant in ["amx", "avx512", "avx2"]:
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print(f"[kt-kernel] Using environment override: {variant}")
         return variant
 
     # Try to read /proc/cpuinfo on Linux
     try:
-        with open('/proc/cpuinfo', 'r') as f:
+        with open("/proc/cpuinfo", "r") as f:
             cpuinfo = f.read().lower()
 
         # Check for AMX support (Intel Sapphire Rapids+)
         # AMX requires amx_tile, amx_int8, and amx_bf16
-        amx_flags = ['amx_tile', 'amx_int8', 'amx_bf16']
+        amx_flags = ["amx_tile", "amx_int8", "amx_bf16"]
         has_amx = all(flag in cpuinfo for flag in amx_flags)
 
         if has_amx:
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Detected AMX support via /proc/cpuinfo")
-            return 'amx'
+            return "amx"
 
         # Check for AVX512 support
         # AVX512F is the foundation for all AVX512 variants
-        if 'avx512f' in cpuinfo:
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if "avx512f" in cpuinfo:
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Detected AVX512 support via /proc/cpuinfo")
-            return 'avx512'
+            return "avx512"
 
         # Check for AVX2 support
-        if 'avx2' in cpuinfo:
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if "avx2" in cpuinfo:
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Detected AVX2 support via /proc/cpuinfo")
-            return 'avx2'
+            return "avx2"
 
         # Fallback to AVX2 (should be rare on modern CPUs)
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print("[kt-kernel] No AVX2/AVX512/AMX detected, using AVX2 fallback")
-        return 'avx2'
+        return "avx2"
 
     except FileNotFoundError:
         # /proc/cpuinfo doesn't exist (not Linux or in container)
         # Try cpufeature package as fallback
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print("[kt-kernel] /proc/cpuinfo not found, trying cpufeature package")
 
         try:
             import cpufeature
 
             # Check for AMX
-            if cpufeature.CPUFeature.get('AMX_TILE', False):
-                if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            if cpufeature.CPUFeature.get("AMX_TILE", False):
+                if os.environ.get("KT_KERNEL_DEBUG") == "1":
                     print("[kt-kernel] Detected AMX support via cpufeature")
-                return 'amx'
+                return "amx"
 
             # Check for AVX512
-            if cpufeature.CPUFeature.get('AVX512F', False):
-                if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            if cpufeature.CPUFeature.get("AVX512F", False):
+                if os.environ.get("KT_KERNEL_DEBUG") == "1":
                     print("[kt-kernel] Detected AVX512 support via cpufeature")
-                return 'avx512'
+                return "avx512"
 
             # Fallback to AVX2
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Using AVX2 fallback via cpufeature")
-            return 'avx2'
+            return "avx2"
 
         except ImportError:
             # cpufeature not available - ultimate fallback
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] cpufeature not available, using AVX2 fallback")
-            return 'avx2'
+            return "avx2"
 
     except Exception as e:
         # Any other error - safe fallback
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print(f"[kt-kernel] Error during CPU detection: {e}, using AVX2 fallback")
-        return 'avx2'
+        return "avx2"
 
 
 def load_extension(variant):
@@ -148,51 +149,53 @@ def load_extension(variant):
         kt_kernel_dir = os.path.dirname(os.path.abspath(__file__))
 
         # Try multi-variant naming first
-        pattern = os.path.join(kt_kernel_dir, f'_kt_kernel_ext_{variant}.*.so')
+        pattern = os.path.join(kt_kernel_dir, f"_kt_kernel_ext_{variant}.*.so")
         so_files = glob.glob(pattern)
 
         if not so_files:
             # Try single-variant naming (fallback for builds without CPUINFER_BUILD_ALL_VARIANTS)
-            pattern = os.path.join(kt_kernel_dir, 'kt_kernel_ext.*.so')
+            pattern = os.path.join(kt_kernel_dir, "kt_kernel_ext.*.so")
             so_files = glob.glob(pattern)
 
             if so_files:
-                if os.environ.get('KT_KERNEL_DEBUG') == '1':
+                if os.environ.get("KT_KERNEL_DEBUG") == "1":
                     print(f"[kt-kernel] Multi-variant {variant} not found, using single-variant build")
             else:
-                raise ImportError(f"No .so file found for variant {variant} (tried patterns: {kt_kernel_dir}/_kt_kernel_ext_{variant}.*.so and {kt_kernel_dir}/kt_kernel_ext.*.so)")
+                raise ImportError(
+                    f"No .so file found for variant {variant} (tried patterns: {kt_kernel_dir}/_kt_kernel_ext_{variant}.*.so and {kt_kernel_dir}/kt_kernel_ext.*.so)"
+                )
 
         so_file = so_files[0]
 
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print(f"[kt-kernel] Loading {variant} from: {so_file}")
 
         # Load the module manually
         # The module exports PyInit_kt_kernel_ext, so we use that as the module name
-        spec = importlib.util.spec_from_file_location('kt_kernel_ext', so_file)
+        spec = importlib.util.spec_from_file_location("kt_kernel_ext", so_file)
         if spec is None or spec.loader is None:
             raise ImportError(f"Failed to create spec for {so_file}")
 
         ext = importlib.util.module_from_spec(spec)
         spec.loader.exec_module(ext)
 
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print(f"[kt-kernel] Successfully loaded {variant.upper()} variant")
         return ext
 
     except (ImportError, ModuleNotFoundError, FileNotFoundError) as e:
-        if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if os.environ.get("KT_KERNEL_DEBUG") == "1":
             print(f"[kt-kernel] Failed to load {variant} variant: {e}")
 
         # Automatic fallback to next best variant
-        if variant == 'amx':
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+        if variant == "amx":
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Falling back from AMX to AVX512")
-            return load_extension('avx512')
-        elif variant == 'avx512':
-            if os.environ.get('KT_KERNEL_DEBUG') == '1':
+            return load_extension("avx512")
+        elif variant == "avx512":
+            if os.environ.get("KT_KERNEL_DEBUG") == "1":
                 print("[kt-kernel] Falling back from AVX512 to AVX2")
-            return load_extension('avx2')
+            return load_extension("avx2")
         else:
             # AVX2 is the last fallback - if this fails, we can't continue
             raise ImportError(
@@ -221,13 +224,13 @@ def initialize():
     # Detect CPU features
     variant = detect_cpu_features()
 
-    if os.environ.get('KT_KERNEL_DEBUG') == '1':
+    if os.environ.get("KT_KERNEL_DEBUG") == "1":
         print(f"[kt-kernel] Selected CPU variant: {variant}")
 
     # Load the appropriate extension
     ext = load_extension(variant)
 
-    if os.environ.get('KT_KERNEL_DEBUG') == '1':
+    if os.environ.get("KT_KERNEL_DEBUG") == "1":
         print(f"[kt-kernel] Extension module loaded: {ext.__name__}")
 
     return ext, variant

kt-kernel/python/cli/__init__.py

@@ -0,0 +1,8 @@
+"""
+KTransformers CLI - A unified command-line interface for KTransformers.
+
+This CLI provides a user-friendly interface to all KTransformers functionality,
+including model inference, fine-tuning, benchmarking, and more.
+"""
+
+__version__ = "0.1.0"

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

@@ -0,0 +1,3 @@
+"""
+Command modules for kt-cli.
+"""

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

@@ -0,0 +1,274 @@
+"""
+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

@@ -0,0 +1,437 @@
+"""
+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()

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

@@ -0,0 +1 @@
+"""Shell completion scripts for kt-cli."""

kt-kernel/python/cli/completions/_kt

@@ -0,0 +1,153 @@
+#compdef kt
+# Zsh completion for kt command
+# This is a static completion script that doesn't require Python startup
+
+_kt() {
+    local -a commands
+    commands=(
+        'version:Show version information'
+        'run:Start model inference server'
+        'chat:Interactive chat with running model'
+        'quant:Quantize model weights'
+        'bench:Run full benchmark'
+        'microbench:Run micro-benchmark'
+        'doctor:Diagnose environment issues'
+        'model:Manage models and storage paths'
+        'config:Manage configuration'
+        'sft:Fine-tuning with LlamaFactory'
+    )
+
+    local -a run_opts
+    run_opts=(
+        '--host[Server host]:host:'
+        '--port[Server port]:port:'
+        '--gpu-experts[Number of GPU experts]:count:'
+        '--cpu-threads[Number of CPU threads]:count:'
+        '--tensor-parallel-size[Tensor parallel size]:size:'
+        '--kt-method[KT method]:method:(AMXINT4 FP8 RAWINT4)'
+        '--attention-backend[Attention backend]:backend:(triton flashinfer)'
+        '--max-total-tokens[Maximum total tokens]:tokens:'
+        '--dry-run[Show command without executing]'
+        '--help[Show help message]'
+    )
+
+    local -a chat_opts
+    chat_opts=(
+        '--host[Server host]:host:'
+        '--port[Server port]:port:'
+        '--model[Model name]:model:'
+        '--temperature[Sampling temperature]:temp:'
+        '--max-tokens[Maximum tokens]:tokens:'
+        '--system[System prompt]:prompt:'
+        '--save-history[Save conversation history]'
+        '--no-save-history[Do not save history]'
+        '--history-file[History file path]:path:_files'
+        '--stream[Enable streaming output]'
+        '--no-stream[Disable streaming output]'
+        '--help[Show help message]'
+    )
+
+    local -a model_cmds
+    model_cmds=(
+        'download:Download a model from HuggingFace'
+        'list:List available models'
+        'path-list:List all model storage paths'
+        'path-add:Add a new model storage path'
+        'path-remove:Remove a model storage path'
+        'search:Search for models in the registry'
+    )
+
+    local -a config_cmds
+    config_cmds=(
+        'show:Show all configuration'
+        'get:Get configuration value'
+        'set:Set configuration value'
+        'reset:Reset to defaults'
+        'path:Show configuration file path'
+        'init:Re-run first-time setup wizard'
+    )
+
+    local -a sft_cmds
+    sft_cmds=(
+        'train:Train model'
+        'chat:Chat with model'
+        'export:Export model'
+    )
+
+    _arguments -C \
+        '1: :->command' \
+        '*::arg:->args'
+
+    case $state in
+        command)
+            _describe 'kt commands' commands
+            _arguments \
+                '--help[Show help message]' \
+                '--version[Show version]'
+            ;;
+        args)
+            case $words[1] in
+                run)
+                    _arguments $run_opts \
+                        '1:model:'
+                    ;;
+                chat)
+                    _arguments $chat_opts
+                    ;;
+                quant)
+                    _arguments \
+                        '--method[Quantization method]:method:' \
+                        '--output[Output directory]:path:_files -/' \
+                        '--help[Show help message]' \
+                        '1:model:_files -/'
+                    ;;
+                bench|microbench)
+                    _arguments \
+                        '--model[Model name or path]:model:' \
+                        '--config[Config file path]:path:_files' \
+                        '--help[Show help message]'
+                    ;;
+                doctor)
+                    _arguments \
+                        '--verbose[Verbose output]' \
+                        '--help[Show help message]'
+                    ;;
+                model)
+                    _arguments \
+                        '1: :->model_cmd' \
+                        '*::arg:->model_args'
+
+                    case $state in
+                        model_cmd)
+                            _describe 'model commands' model_cmds
+                            ;;
+                    esac
+                    ;;
+                config)
+                    _arguments \
+                        '1: :->config_cmd' \
+                        '*::arg:->config_args'
+
+                    case $state in
+                        config_cmd)
+                            _describe 'config commands' config_cmds
+                            ;;
+                    esac
+                    ;;
+                sft)
+                    _arguments \
+                        '1: :->sft_cmd' \
+                        '*::arg:->sft_args'
+
+                    case $state in
+                        sft_cmd)
+                            _describe 'sft commands' sft_cmds
+                            ;;
+                    esac
+                    ;;
+            esac
+            ;;
+    esac
+}
+
+_kt "$@"

kt-kernel/python/cli/completions/kt-completion.bash

@@ -0,0 +1,73 @@
+#!/bin/bash
+# Bash completion for kt command
+# This is a static completion script that doesn't require Python startup
+
+_kt_completion() {
+    local cur prev opts
+    COMPREPLY=()
+    cur="${COMP_WORDS[COMP_CWORD]}"
+    prev="${COMP_WORDS[COMP_CWORD-1]}"
+
+    # Main commands
+    local commands="version run chat quant bench microbench doctor model config sft"
+
+    # Global options
+    local global_opts="--help --version"
+
+    # Handle subcommands
+    case "${COMP_CWORD}" in
+        1)
+            # First argument: suggest commands and global options
+            COMPREPLY=( $(compgen -W "${commands} ${global_opts}" -- ${cur}) )
+            return 0
+            ;;
+        *)
+            # Handle specific command options
+            case "${COMP_WORDS[1]}" in
+                run)
+                    local run_opts="--host --port --gpu-experts --cpu-threads --tensor-parallel-size --kt-method --attention-backend --max-total-tokens --dry-run --help"
+                    COMPREPLY=( $(compgen -W "${run_opts}" -- ${cur}) )
+                    ;;
+                chat)
+                    local chat_opts="--host --port --model --temperature --max-tokens --system --save-history --no-save-history --history-file --stream --no-stream --help"
+                    COMPREPLY=( $(compgen -W "${chat_opts}" -- ${cur}) )
+                    ;;
+                quant)
+                    local quant_opts="--method --output --help"
+                    COMPREPLY=( $(compgen -W "${quant_opts}" -- ${cur}) )
+                    ;;
+                bench|microbench)
+                    local bench_opts="--model --config --help"
+                    COMPREPLY=( $(compgen -W "${bench_opts}" -- ${cur}) )
+                    ;;
+                doctor)
+                    local doctor_opts="--verbose --help"
+                    COMPREPLY=( $(compgen -W "${doctor_opts}" -- ${cur}) )
+                    ;;
+                model)
+                    local model_cmds="download list path-list path-add path-remove search"
+                    local model_opts="--help"
+                    COMPREPLY=( $(compgen -W "${model_cmds} ${model_opts}" -- ${cur}) )
+                    ;;
+                config)
+                    local config_cmds="show get set reset path init model-path-list model-path-add model-path-remove"
+                    local config_opts="--help"
+                    COMPREPLY=( $(compgen -W "${config_cmds} ${config_opts}" -- ${cur}) )
+                    ;;
+                sft)
+                    local sft_cmds="train chat export"
+                    local sft_opts="--help"
+                    COMPREPLY=( $(compgen -W "${sft_cmds} ${sft_opts}" -- ${cur}) )
+                    ;;
+                version)
+                    COMPREPLY=( $(compgen -W "--help" -- ${cur}) )
+                    ;;
+                *)
+                    COMPREPLY=()
+                    ;;
+            esac
+            ;;
+    esac
+}
+
+complete -F _kt_completion kt

kt-kernel/python/cli/completions/kt.fish

@@ -0,0 +1,74 @@
+# Fish completion for kt command
+# This is a static completion script that doesn't require Python startup
+
+# Main commands
+complete -c kt -f -n "__fish_use_subcommand" -a "version" -d "Show version information"
+complete -c kt -f -n "__fish_use_subcommand" -a "run" -d "Start model inference server"
+complete -c kt -f -n "__fish_use_subcommand" -a "chat" -d "Interactive chat with running model"
+complete -c kt -f -n "__fish_use_subcommand" -a "quant" -d "Quantize model weights"
+complete -c kt -f -n "__fish_use_subcommand" -a "bench" -d "Run full benchmark"
+complete -c kt -f -n "__fish_use_subcommand" -a "microbench" -d "Run micro-benchmark"
+complete -c kt -f -n "__fish_use_subcommand" -a "doctor" -d "Diagnose environment issues"
+complete -c kt -f -n "__fish_use_subcommand" -a "model" -d "Manage models and storage paths"
+complete -c kt -f -n "__fish_use_subcommand" -a "config" -d "Manage configuration"
+complete -c kt -f -n "__fish_use_subcommand" -a "sft" -d "Fine-tuning with LlamaFactory"
+
+# Global options
+complete -c kt -l help -d "Show help message"
+complete -c kt -l version -d "Show version"
+
+# Run command options
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l host -d "Server host"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l port -d "Server port"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l gpu-experts -d "Number of GPU experts"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l cpu-threads -d "Number of CPU threads"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l tensor-parallel-size -d "Tensor parallel size"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l kt-method -d "KT method"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l attention-backend -d "Attention backend"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l max-total-tokens -d "Maximum total tokens"
+complete -c kt -f -n "__fish_seen_subcommand_from run" -l dry-run -d "Show command without executing"
+
+# Chat command options
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l host -d "Server host"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l port -d "Server port"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l model -d "Model name"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l temperature -d "Sampling temperature"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l max-tokens -d "Maximum tokens"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l system -d "System prompt"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l save-history -d "Save conversation history"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l no-save-history -d "Do not save history"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l history-file -d "History file path"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l stream -d "Enable streaming output"
+complete -c kt -f -n "__fish_seen_subcommand_from chat" -l no-stream -d "Disable streaming output"
+
+# Quant command options
+complete -c kt -f -n "__fish_seen_subcommand_from quant" -l method -d "Quantization method"
+complete -c kt -f -n "__fish_seen_subcommand_from quant" -l output -d "Output directory"
+
+# Bench command options
+complete -c kt -f -n "__fish_seen_subcommand_from bench microbench" -l model -d "Model name or path"
+complete -c kt -f -n "__fish_seen_subcommand_from bench microbench" -l config -d "Config file path"
+
+# Doctor command options
+complete -c kt -f -n "__fish_seen_subcommand_from doctor" -l verbose -d "Verbose output"
+
+# Model subcommands
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "download" -d "Download a model from HuggingFace"
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "list" -d "List available models"
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "path-list" -d "List all model storage paths"
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "path-add" -d "Add a new model storage path"
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "path-remove" -d "Remove a model storage path"
+complete -c kt -f -n "__fish_seen_subcommand_from model; and not __fish_seen_subcommand_from download list path-list path-add path-remove search" -a "search" -d "Search for models in the registry"
+
+# Config subcommands
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "show" -d "Show all configuration"
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "get" -d "Get configuration value"
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "set" -d "Set configuration value"
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "reset" -d "Reset to defaults"
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "path" -d "Show configuration file path"
+complete -c kt -f -n "__fish_seen_subcommand_from config; and not __fish_seen_subcommand_from show get set reset path init" -a "init" -d "Re-run first-time setup wizard"
+
+# SFT subcommands
+complete -c kt -f -n "__fish_seen_subcommand_from sft; and not __fish_seen_subcommand_from train chat export" -a "train" -d "Train model"
+complete -c kt -f -n "__fish_seen_subcommand_from sft; and not __fish_seen_subcommand_from train chat export" -a "chat" -d "Chat with model"
+complete -c kt -f -n "__fish_seen_subcommand_from sft; and not __fish_seen_subcommand_from train chat export" -a "export" -d "Export model"

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

@@ -0,0 +1,7 @@
+"""
+Configuration management for kt-cli.
+"""
+
+from kt_kernel.cli.config.settings import Settings, get_settings
+
+__all__ = ["Settings", "get_settings"]

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

@@ -0,0 +1,311 @@
+"""
+Configuration management for kt-cli.
+
+Handles reading and writing configuration from ~/.ktransformers/config.yaml
+"""
+
+import os
+from pathlib import Path
+from typing import Any, Optional
+
+import yaml
+
+# Default configuration directory
+DEFAULT_CONFIG_DIR = Path.home() / ".ktransformers"
+DEFAULT_CONFIG_FILE = DEFAULT_CONFIG_DIR / "config.yaml"
+DEFAULT_MODELS_DIR = DEFAULT_CONFIG_DIR / "models"
+DEFAULT_CACHE_DIR = DEFAULT_CONFIG_DIR / "cache"
+
+# Default configuration values
+DEFAULT_CONFIG = {
+    "general": {
+        "language": "auto",  # auto, en, zh
+        "color": True,
+        "verbose": False,
+    },
+    "paths": {
+        "models": str(DEFAULT_MODELS_DIR),
+        "cache": str(DEFAULT_CACHE_DIR),
+        "weights": "",  # Custom quantized weights path
+    },
+    "server": {
+        "host": "0.0.0.0",
+        "port": 30000,
+    },
+    "inference": {
+        # Inference parameters are model-specific and should not have defaults
+        # They will be auto-detected or use model-specific optimizations
+        # Environment variables (general optimizations)
+        "env": {
+            "PYTORCH_ALLOC_CONF": "expandable_segments:True",
+            "SGLANG_ENABLE_JIT_DEEPGEMM": "0",
+        },
+    },
+    "download": {
+        "mirror": "",  # HuggingFace mirror URL
+        "resume": True,
+        "verify": True,
+    },
+    "advanced": {
+        # Environment variables to set when running
+        "env": {},
+        # Extra arguments to pass to sglang
+        "sglang_args": [],
+        # Extra arguments to pass to llamafactory
+        "llamafactory_args": [],
+    },
+    "dependencies": {
+        # SGLang installation source configuration
+        "sglang": {
+            "source": "github",  # "pypi" or "github"
+            "repo": "https://github.com/kvcache-ai/sglang",
+            "branch": "main",
+        },
+    },
+}
+
+
+class Settings:
+    """Configuration manager for kt-cli."""
+
+    def __init__(self, config_path: Optional[Path] = None):
+        """Initialize settings manager.
+
+        Args:
+            config_path: Path to config file. Defaults to ~/.ktransformers/config.yaml
+        """
+        self.config_path = config_path or DEFAULT_CONFIG_FILE
+        self.config_dir = self.config_path.parent
+        self._config: dict[str, Any] = {}
+        self._load()
+
+    def _ensure_dirs(self) -> None:
+        """Ensure configuration directories exist."""
+        self.config_dir.mkdir(parents=True, exist_ok=True)
+
+        # Ensure all model paths exist
+        model_paths = self.get_model_paths()
+        for path in model_paths:
+            path.mkdir(parents=True, exist_ok=True)
+
+        Path(self.get("paths.cache", DEFAULT_CACHE_DIR)).mkdir(parents=True, exist_ok=True)
+
+    def _load(self) -> None:
+        """Load configuration from file."""
+        self._config = self._deep_copy(DEFAULT_CONFIG)
+
+        if self.config_path.exists():
+            try:
+                with open(self.config_path, "r", encoding="utf-8") as f:
+                    user_config = yaml.safe_load(f) or {}
+                self._deep_merge(self._config, user_config)
+            except (yaml.YAMLError, OSError) as e:
+                # Log warning but continue with defaults
+                print(f"Warning: Failed to load config: {e}")
+
+        self._ensure_dirs()
+
+    def _save(self) -> None:
+        """Save configuration to file."""
+        self._ensure_dirs()
+        try:
+            with open(self.config_path, "w", encoding="utf-8") as f:
+                yaml.dump(self._config, f, default_flow_style=False, allow_unicode=True)
+        except OSError as e:
+            raise RuntimeError(f"Failed to save config: {e}")
+
+    def _deep_copy(self, obj: Any) -> Any:
+        """Create a deep copy of a nested dict."""
+        if isinstance(obj, dict):
+            return {k: self._deep_copy(v) for k, v in obj.items()}
+        if isinstance(obj, list):
+            return [self._deep_copy(item) for item in obj]
+        return obj
+
+    def _deep_merge(self, base: dict, override: dict) -> None:
+        """Deep merge override into base."""
+        for key, value in override.items():
+            if key in base and isinstance(base[key], dict) and isinstance(value, dict):
+                self._deep_merge(base[key], value)
+            else:
+                base[key] = value
+
+    def get(self, key: str, default: Any = None) -> Any:
+        """Get a configuration value by dot-separated key.
+
+        Args:
+            key: Dot-separated key path (e.g., "server.port")
+            default: Default value if key not found
+
+        Returns:
+            Configuration value or default
+        """
+        parts = key.split(".")
+        value = self._config
+
+        for part in parts:
+            if isinstance(value, dict) and part in value:
+                value = value[part]
+            else:
+                return default
+
+        return value
+
+    def set(self, key: str, value: Any) -> None:
+        """Set a configuration value by dot-separated key.
+
+        Args:
+            key: Dot-separated key path (e.g., "server.port")
+            value: Value to set
+        """
+        parts = key.split(".")
+        config = self._config
+
+        # Navigate to parent
+        for part in parts[:-1]:
+            if part not in config:
+                config[part] = {}
+            config = config[part]
+
+        # Set value
+        config[parts[-1]] = value
+        self._save()
+
+    def delete(self, key: str) -> bool:
+        """Delete a configuration value.
+
+        Args:
+            key: Dot-separated key path
+
+        Returns:
+            True if key was deleted, False if not found
+        """
+        parts = key.split(".")
+        config = self._config
+
+        # Navigate to parent
+        for part in parts[:-1]:
+            if part not in config:
+                return False
+            config = config[part]
+
+        # Delete key
+        if parts[-1] in config:
+            del config[parts[-1]]
+            self._save()
+            return True
+        return False
+
+    def reset(self) -> None:
+        """Reset configuration to defaults."""
+        self._config = self._deep_copy(DEFAULT_CONFIG)
+        self._save()
+
+    def get_all(self) -> dict[str, Any]:
+        """Get all configuration values."""
+        return self._deep_copy(self._config)
+
+    def get_env_vars(self) -> dict[str, str]:
+        """Get environment variables to set."""
+        env_vars = {}
+
+        # Get from advanced.env
+        advanced_env = self.get("advanced.env", {})
+        if isinstance(advanced_env, dict):
+            env_vars.update({k: str(v) for k, v in advanced_env.items()})
+
+        return env_vars
+
+    @property
+    def models_dir(self) -> Path:
+        """Get the primary models directory path (for backward compatibility)."""
+        paths = self.get_model_paths()
+        return paths[0] if paths else Path(DEFAULT_MODELS_DIR)
+
+    def get_model_paths(self) -> list[Path]:
+        """Get all model directory paths.
+
+        Returns a list of Path objects. Supports both:
+        - Single path: paths.models = "/path/to/models"
+        - Multiple paths: paths.models = ["/path/1", "/path/2"]
+        """
+        models_config = self.get("paths.models", DEFAULT_MODELS_DIR)
+
+        # Handle both string and list
+        if isinstance(models_config, str):
+            return [Path(models_config)]
+        elif isinstance(models_config, list):
+            return [Path(p) for p in models_config]
+        else:
+            return [Path(DEFAULT_MODELS_DIR)]
+
+    def add_model_path(self, path: str) -> None:
+        """Add a new model path to the configuration."""
+        models_config = self.get("paths.models", DEFAULT_MODELS_DIR)
+
+        # Convert to list if it's a string
+        if isinstance(models_config, str):
+            paths = [models_config]
+        elif isinstance(models_config, list):
+            paths = list(models_config)
+        else:
+            paths = []
+
+        # Add new path if not already present
+        if path not in paths:
+            paths.append(path)
+            self.set("paths.models", paths)
+
+    def remove_model_path(self, path: str) -> bool:
+        """Remove a model path from the configuration.
+
+        Returns True if path was removed, False if not found.
+        """
+        models_config = self.get("paths.models", DEFAULT_MODELS_DIR)
+
+        if isinstance(models_config, str):
+            # Can't remove if it's a single string
+            if models_config == path:
+                # Don't remove the last path
+                return False
+            return False
+        elif isinstance(models_config, list):
+            if path in models_config:
+                paths = list(models_config)
+                paths.remove(path)
+                # Don't allow removing all paths
+                if not paths:
+                    return False
+                self.set("paths.models", paths if len(paths) > 1 else paths[0])
+                return True
+
+        return False
+
+    @property
+    def cache_dir(self) -> Path:
+        """Get the cache directory path."""
+        return Path(self.get("paths.cache", DEFAULT_CACHE_DIR))
+
+    @property
+    def weights_dir(self) -> Optional[Path]:
+        """Get the custom weights directory path."""
+        weights = self.get("paths.weights", "")
+        return Path(weights) if weights else None
+
+
+# Global settings instance
+_settings: Optional[Settings] = None
+
+
+def get_settings() -> Settings:
+    """Get the global settings instance."""
+    global _settings
+    if _settings is None:
+        _settings = Settings()
+    return _settings
+
+
+def reset_settings() -> None:
+    """Reset the global settings instance."""
+    global _settings
+    _settings = None

kt-kernel/python/cli/i18n.py

@@ -0,0 +1,655 @@
+"""
+Internationalization (i18n) module for kt-cli.
+
+Supports English and Chinese languages, with automatic detection based on
+system locale or KT_LANG environment variable.
+"""
+
+import os
+from typing import Any
+
+# Message definitions for all supported languages
+MESSAGES: dict[str, dict[str, str]] = {
+    "en": {
+        # General
+        "welcome": "Welcome to KTransformers!",
+        "goodbye": "Goodbye!",
+        "error": "Error",
+        "warning": "Warning",
+        "success": "Success",
+        "info": "Info",
+        "yes": "Yes",
+        "no": "No",
+        "cancel": "Cancel",
+        "confirm": "Confirm",
+        "done": "Done",
+        "failed": "Failed",
+        "skip": "Skip",
+        "back": "Back",
+        "next": "Next",
+        "retry": "Retry",
+        "abort": "Abort",
+        # Version command
+        "version_info": "KTransformers CLI",
+        "version_python": "Python",
+        "version_platform": "Platform",
+        "version_cuda": "CUDA",
+        "version_cuda_not_found": "Not found",
+        "version_kt_kernel": "kt-kernel",
+        "version_ktransformers": "ktransformers",
+        "version_sglang": "sglang",
+        "version_llamafactory": "llamafactory",
+        "version_not_installed": "Not installed",
+        # Install command
+        "install_detecting_env": "Detecting environment managers...",
+        "install_found": "Found {name} (version {version})",
+        "install_not_found": "Not found: {name}",
+        "install_checking_env": "Checking existing environments...",
+        "install_env_exists": "Found existing 'kt' environment",
+        "install_env_not_exists": "No 'kt' environment found",
+        "install_no_env_manager": "No virtual environment manager detected",
+        "install_select_method": "Please select installation method:",
+        "install_method_conda": "Create new conda environment 'kt' (Recommended)",
+        "install_method_venv": "Create new venv environment",
+        "install_method_uv": "Create new uv environment (Fast)",
+        "install_method_docker": "Use Docker container",
+        "install_method_system": "Install to system Python (Not recommended)",
+        "install_select_mode": "Please select installation mode:",
+        "install_mode_inference": "Inference - Install kt-kernel + SGLang",
+        "install_mode_sft": "Training - Install kt-sft + LlamaFactory",
+        "install_mode_full": "Full - Install all components",
+        "install_creating_env": "Creating {type} environment '{name}'...",
+        "install_env_created": "Environment created successfully",
+        "install_installing_deps": "Installing dependencies...",
+        "install_checking_deps": "Checking dependency versions...",
+        "install_dep_ok": "OK",
+        "install_dep_outdated": "Needs update",
+        "install_dep_missing": "Missing",
+        "install_installing_pytorch": "Installing PyTorch...",
+        "install_installing_from_requirements": "Installing from requirements file...",
+        "install_deps_outdated": "Found {count} package(s) that need updating. Continue?",
+        "install_updating": "Updating packages...",
+        "install_complete": "Installation complete!",
+        "install_activate_hint": "Activate environment: {command}",
+        "install_start_hint": "Get started: kt run --help",
+        "install_docker_pulling": "Pulling Docker image...",
+        "install_docker_complete": "Docker image ready!",
+        "install_docker_run_hint": "Run with: docker run --gpus all -p 30000:30000 {image} kt run {model}",
+        "install_in_venv": "Running in virtual environment: {name}",
+        "install_continue_without_venv": "Continue installing to system Python?",
+        "install_already_installed": "All dependencies are already installed!",
+        "install_confirm": "Install {count} package(s)?",
+        # Install - System dependencies
+        "install_checking_system_deps": "Checking system dependencies...",
+        "install_dep_name": "Dependency",
+        "install_dep_status": "Status",
+        "install_deps_all_installed": "All system dependencies are installed",
+        "install_deps_install_prompt": "Install missing dependencies?",
+        "install_installing_system_deps": "Installing system dependencies...",
+        "install_installing_dep": "Installing {name}",
+        "install_dep_no_install_cmd": "No install command available for {name} on {os}",
+        "install_dep_install_failed": "Failed to install {name}",
+        "install_deps_skipped": "Skipping dependency installation",
+        "install_deps_failed": "Failed to install system dependencies",
+        # Install - CPU detection
+        "install_auto_detect_cpu": "Auto-detecting CPU capabilities...",
+        "install_cpu_features": "Detected CPU features: {features}",
+        "install_cpu_no_features": "No advanced CPU features detected",
+        # Install - Build configuration
+        "install_build_config": "Build Configuration:",
+        "install_native_warning": "Note: Binary optimized for THIS CPU only (not portable)",
+        "install_building_from_source": "Building kt-kernel from source...",
+        "install_build_failed": "Build failed",
+        "install_build_success": "Build completed successfully",
+        # Install - Verification
+        "install_verifying": "Verifying installation...",
+        "install_verify_success": "kt-kernel {version} ({variant} variant) installed successfully",
+        "install_verify_failed": "Verification failed: {error}",
+        # Install - Docker
+        "install_docker_guide_title": "Docker Installation",
+        "install_docker_guide_desc": "For Docker installation, please refer to the official guide:",
+        # Config command
+        "config_show_title": "Current Configuration",
+        "config_set_success": "Configuration updated: {key} = {value}",
+        "config_get_value": "{key} = {value}",
+        "config_get_not_found": "Configuration key '{key}' not found",
+        "config_reset_confirm": "This will reset all configurations to default. Continue?",
+        "config_reset_success": "Configuration reset to default",
+        "config_file_location": "Configuration file: {path}",
+        # Doctor command
+        "doctor_title": "KTransformers Environment Diagnostics",
+        "doctor_checking": "Running diagnostics...",
+        "doctor_check_python": "Python version",
+        "doctor_check_cuda": "CUDA availability",
+        "doctor_check_gpu": "GPU detection",
+        "doctor_check_cpu": "CPU",
+        "doctor_check_cpu_isa": "CPU Instructions",
+        "doctor_check_numa": "NUMA Topology",
+        "doctor_check_memory": "System memory",
+        "doctor_check_disk": "Disk space",
+        "doctor_check_packages": "Required packages",
+        "doctor_check_env": "Environment variables",
+        "doctor_status_ok": "OK",
+        "doctor_status_warning": "Warning",
+        "doctor_status_error": "Error",
+        "doctor_gpu_found": "Found {count} GPU(s): {names}",
+        "doctor_gpu_not_found": "No GPU detected",
+        "doctor_cpu_info": "{name} ({cores} cores / {threads} threads)",
+        "doctor_cpu_isa_info": "{isa_list}",
+        "doctor_cpu_isa_missing": "Missing recommended: {missing}",
+        "doctor_numa_info": "{nodes} node(s)",
+        "doctor_numa_detail": "{node}: CPUs {cpus}",
+        "doctor_memory_info": "{available} available / {total} total",
+        "doctor_memory_freq": "{available} available / {total} total ({freq}MHz {type})",
+        "doctor_disk_info": "{available} available at {path}",
+        "doctor_all_ok": "All checks passed! Your environment is ready.",
+        "doctor_has_issues": "Some issues were found. Please review the warnings/errors above.",
+        # Run command
+        "run_detecting_hardware": "Detecting hardware configuration...",
+        "run_gpu_info": "GPU: {name} ({vram}GB VRAM)",
+        "run_cpu_info": "CPU: {name} ({cores} cores, {numa} NUMA nodes)",
+        "run_ram_info": "RAM: {total}GB",
+        "run_checking_model": "Checking model status...",
+        "run_model_path": "Model path: {path}",
+        "run_weights_not_found": "Quantized weights not found",
+        "run_quant_prompt": "Quantize model now? (This may take a while)",
+        "run_quantizing": "Quantizing model...",
+        "run_starting_server": "Starting server...",
+        "run_server_mode": "Mode: SGLang + kt-kernel",
+        "run_server_port": "Port: {port}",
+        "run_gpu_experts": "GPU experts: {count}/layer",
+        "run_cpu_threads": "CPU threads: {count}",
+        "run_server_started": "Server started!",
+        "run_api_url": "API URL: http://{host}:{port}",
+        "run_docs_url": "Docs URL: http://{host}:{port}/docs",
+        "run_stop_hint": "Press Ctrl+C to stop the server",
+        "run_model_not_found": "Model '{name}' not found. Run 'kt download' first.",
+        "run_multiple_matches": "Multiple models found. Please select:",
+        "run_select_model": "Select model",
+        "run_select_model_title": "Select a model to run",
+        "run_select_model_prompt": "Enter number",
+        "run_local_models": "Local Models (Downloaded)",
+        "run_registered_models": "Registered Models",
+        # Download command
+        "download_list_title": "Available Models",
+        "download_searching": "Searching for model '{name}'...",
+        "download_found": "Found: {name}",
+        "download_multiple_found": "Multiple matches found:",
+        "download_select": "Select model to download:",
+        "download_destination": "Destination: {path}",
+        "download_starting": "Starting download...",
+        "download_progress": "Downloading {name}...",
+        "download_complete": "Download complete!",
+        "download_already_exists": "Model already exists at {path}",
+        "download_overwrite_prompt": "Overwrite existing files?",
+        # Quant command
+        "quant_input_path": "Input path: {path}",
+        "quant_output_path": "Output path: {path}",
+        "quant_method": "Quantization method: {method}",
+        "quant_starting": "Starting quantization...",
+        "quant_progress": "Quantizing...",
+        "quant_complete": "Quantization complete!",
+        "quant_input_not_found": "Input model not found at {path}",
+        # SFT command
+        "sft_mode_train": "Training mode",
+        "sft_mode_chat": "Chat mode",
+        "sft_mode_export": "Export mode",
+        "sft_config_path": "Config file: {path}",
+        "sft_starting": "Starting {mode}...",
+        "sft_complete": "{mode} complete!",
+        "sft_config_not_found": "Config file not found: {path}",
+        # Bench command
+        "bench_starting": "Starting benchmark...",
+        "bench_type": "Benchmark type: {type}",
+        "bench_complete": "Benchmark complete!",
+        "bench_results_title": "Benchmark Results",
+        # Common prompts
+        "prompt_continue": "Continue?",
+        "prompt_select": "Please select:",
+        "prompt_enter_value": "Enter value:",
+        "prompt_confirm_action": "Confirm this action?",
+        # First-run setup - Model path selection
+        "setup_model_path_title": "Model Storage Location",
+        "setup_model_path_desc": "LLM models are large (50-200GB+). Please select a storage location with sufficient space:",
+        "setup_scanning_disks": "Scanning available storage locations...",
+        "setup_disk_option": "{path} ({available} available / {total} total)",
+        "setup_disk_option_recommended": "{path} ({available} available / {total} total) [Recommended]",
+        "setup_custom_path": "Enter custom path",
+        "setup_enter_custom_path": "Enter the path for model storage",
+        "setup_path_not_exist": "Path does not exist. Create it?",
+        "setup_path_no_write": "No write permission for this path. Please choose another.",
+        "setup_path_low_space": "Warning: Less than 100GB available. Large models may not fit.",
+        "setup_model_path_set": "Model storage path set to: {path}",
+        "setup_no_large_disk": "No large storage locations found. Using default path.",
+        "setup_scanning_models": "Scanning for existing models...",
+        "setup_found_models": "Found {count} model(s):",
+        "setup_model_info": "{name} ({size}, {type})",
+        "setup_no_models_found": "No existing models found in this location.",
+        "setup_location_has_models": "{count} model(s) found",
+        "setup_installing_completion": "Installing shell completion for {shell}...",
+        "setup_completion_installed": "Shell completion installed! Restart terminal to enable.",
+        "setup_completion_failed": "Failed to install shell completion. Run 'kt --install-completion' manually.",
+        # Auto completion
+        "completion_installed_title": "Tab Completion",
+        "completion_installed_for": "Shell completion installed for {shell}",
+        "completion_activate_now": "To enable completion in this terminal session, run:",
+        "completion_next_session": "Completion will be automatically enabled in new terminal sessions.",
+        # SGLang
+        "sglang_not_found": "SGLang not found",
+        "sglang_pypi_warning": "SGLang from PyPI may not be compatible with kt-kernel",
+        "sglang_pypi_hint": 'SGLang from PyPI may not be compatible. Install from source: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_install_hint": 'Install SGLang: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_recommend_source": 'Recommend reinstalling from source: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_kt_kernel_not_supported": "SGLang does not support kt-kernel (missing --kt-gpu-prefill-token-threshold parameter)",
+        "sglang_checking_kt_kernel_support": "Checking SGLang kt-kernel support...",
+        "sglang_kt_kernel_supported": "SGLang kt-kernel support verified",
+        # Chat
+        "chat_proxy_detected": "Proxy detected in environment",
+        "chat_proxy_confirm": "Use proxy for connection?",
+        "chat_proxy_disabled": "Proxy disabled for this session",
+        # Model command
+        "model_supported_title": "KTransformers Supported Models",
+        "model_column_model": "Model",
+        "model_column_status": "Status",
+        "model_column_local_path": "Local Path",
+        "model_status_local": "Local",
+        "model_status_not_downloaded": "Not downloaded",
+        "model_usage_title": "Usage",
+        "model_usage_download": "Download a model:",
+        "model_usage_list_local": "List local models:",
+        "model_usage_search": "Search models:",
+        "model_storage_paths_title": "Model Storage Paths",
+        "model_local_models_title": "Locally Downloaded Models",
+        "model_available_models_title": "Available Models",
+        "model_no_local_models": "No locally downloaded models found",
+        "model_download_hint": "Download a model with:",
+        "model_download_usage_hint": "Usage: kt model download <model-name>",
+        "model_download_list_hint": "Use 'kt model download --list' to see available models.",
+        "model_download_hf_hint": "Or specify a HuggingFace repo directly: kt model download org/model-name",
+        "model_saved_to": "Model saved to: {path}",
+        "model_start_with": "Start with: kt run {name}",
+        "model_download_failed": "Download failed: {error}",
+        "model_hf_cli_not_found": "huggingface-cli not found. Install with: pip install huggingface-hub",
+        "model_path_not_exist": "Path does not exist: {path}",
+        "model_create_directory": "Create directory {path}?",
+        "model_created_directory": "Created directory: {path}",
+        "model_create_dir_failed": "Failed to create directory: {error}",
+        "model_path_added": "Added model path: {path}",
+        "model_path_removed": "Removed model path: {path}",
+        "model_path_not_found": "Path not found in configuration or cannot remove last path: {path}",
+        "model_search_no_results": "No models found matching '{query}'",
+        "model_search_results_title": "Search Results for '{query}'",
+        "model_column_name": "Name",
+        "model_column_hf_repo": "HuggingFace Repo",
+        "model_column_aliases": "Aliases",
+        # Coming soon
+        "feature_coming_soon": "This feature is coming soon...",
+    },
+    "zh": {
+        # General
+        "welcome": "欢迎使用 KTransformers！",
+        "goodbye": "再见！",
+        "error": "错误",
+        "warning": "警告",
+        "success": "成功",
+        "info": "信息",
+        "yes": "是",
+        "no": "否",
+        "cancel": "取消",
+        "confirm": "确认",
+        "done": "完成",
+        "failed": "失败",
+        "skip": "跳过",
+        "back": "返回",
+        "next": "下一步",
+        "retry": "重试",
+        "abort": "中止",
+        # Version command
+        "version_info": "KTransformers CLI",
+        "version_python": "Python",
+        "version_platform": "平台",
+        "version_cuda": "CUDA",
+        "version_cuda_not_found": "未找到",
+        "version_kt_kernel": "kt-kernel",
+        "version_ktransformers": "ktransformers",
+        "version_sglang": "sglang",
+        "version_llamafactory": "llamafactory",
+        "version_not_installed": "未安装",
+        # Install command
+        "install_detecting_env": "检测环境管理工具...",
+        "install_found": "发现 {name} (版本 {version})",
+        "install_not_found": "未找到: {name}",
+        "install_checking_env": "检查现有环境...",
+        "install_env_exists": "发现现有 'kt' 环境",
+        "install_env_not_exists": "未发现 'kt' 环境",
+        "install_no_env_manager": "未检测到虚拟环境管理工具",
+        "install_select_method": "请选择安装方式:",
+        "install_method_conda": "创建新的 conda 环境 'kt' (推荐)",
+        "install_method_venv": "创建新的 venv 环境",
+        "install_method_uv": "创建新的 uv 环境 (快速)",
+        "install_method_docker": "使用 Docker 容器",
+        "install_method_system": "安装到系统 Python (不推荐)",
+        "install_select_mode": "请选择安装模式:",
+        "install_mode_inference": "推理模式 - 安装 kt-kernel + SGLang",
+        "install_mode_sft": "训练模式 - 安装 kt-sft + LlamaFactory",
+        "install_mode_full": "完整安装 - 安装所有组件",
+        "install_creating_env": "正在创建 {type} 环境 '{name}'...",
+        "install_env_created": "环境创建成功",
+        "install_installing_deps": "正在安装依赖...",
+        "install_checking_deps": "检查依赖版本...",
+        "install_dep_ok": "正常",
+        "install_dep_outdated": "需更新",
+        "install_dep_missing": "缺失",
+        "install_installing_pytorch": "正在安装 PyTorch...",
+        "install_installing_from_requirements": "从依赖文件安装...",
+        "install_deps_outdated": "发现 {count} 个包需要更新，是否继续？",
+        "install_updating": "正在更新包...",
+        "install_complete": "安装完成！",
+        "install_activate_hint": "激活环境: {command}",
+        "install_start_hint": "开始使用: kt run --help",
+        "install_docker_pulling": "正在拉取 Docker 镜像...",
+        "install_docker_complete": "Docker 镜像已就绪！",
+        "install_docker_run_hint": "运行: docker run --gpus all -p 30000:30000 {image} kt run {model}",
+        "install_in_venv": "当前在虚拟环境中: {name}",
+        "install_continue_without_venv": "继续安装到系统 Python？",
+        "install_already_installed": "所有依赖已安装！",
+        "install_confirm": "安装 {count} 个包？",
+        # Install - System dependencies
+        "install_checking_system_deps": "检查系统依赖...",
+        "install_dep_name": "依赖项",
+        "install_dep_status": "状态",
+        "install_deps_all_installed": "所有系统依赖已安装",
+        "install_deps_install_prompt": "是否安装缺失的依赖？",
+        "install_installing_system_deps": "正在安装系统依赖...",
+        "install_installing_dep": "正在安装 {name}",
+        "install_dep_no_install_cmd": "{os} 系统上没有 {name} 的安装命令",
+        "install_dep_install_failed": "安装 {name} 失败",
+        "install_deps_skipped": "跳过依赖安装",
+        "install_deps_failed": "系统依赖安装失败",
+        # Install - CPU detection
+        "install_auto_detect_cpu": "正在自动检测 CPU 能力...",
+        "install_cpu_features": "检测到的 CPU 特性: {features}",
+        "install_cpu_no_features": "未检测到高级 CPU 特性",
+        # Install - Build configuration
+        "install_build_config": "构建配置:",
+        "install_native_warning": "注意: 二进制文件仅针对当前 CPU 优化（不可移植）",
+        "install_building_from_source": "正在从源码构建 kt-kernel...",
+        "install_build_failed": "构建失败",
+        "install_build_success": "构建成功",
+        # Install - Verification
+        "install_verifying": "正在验证安装...",
+        "install_verify_success": "kt-kernel {version} ({variant} 变体) 安装成功",
+        "install_verify_failed": "验证失败: {error}",
+        # Install - Docker
+        "install_docker_guide_title": "Docker 安装",
+        "install_docker_guide_desc": "有关 Docker 安装，请参阅官方指南:",
+        # Config command
+        "config_show_title": "当前配置",
+        "config_set_success": "配置已更新: {key} = {value}",
+        "config_get_value": "{key} = {value}",
+        "config_get_not_found": "未找到配置项 '{key}'",
+        "config_reset_confirm": "这将重置所有配置为默认值。是否继续？",
+        "config_reset_success": "配置已重置为默认值",
+        "config_file_location": "配置文件: {path}",
+        # Doctor command
+        "doctor_title": "KTransformers 环境诊断",
+        "doctor_checking": "正在运行诊断...",
+        "doctor_check_python": "Python 版本",
+        "doctor_check_cuda": "CUDA 可用性",
+        "doctor_check_gpu": "GPU 检测",
+        "doctor_check_cpu": "CPU",
+        "doctor_check_cpu_isa": "CPU 指令集",
+        "doctor_check_numa": "NUMA 拓扑",
+        "doctor_check_memory": "系统内存",
+        "doctor_check_disk": "磁盘空间",
+        "doctor_check_packages": "必需的包",
+        "doctor_check_env": "环境变量",
+        "doctor_status_ok": "正常",
+        "doctor_status_warning": "警告",
+        "doctor_status_error": "错误",
+        "doctor_gpu_found": "发现 {count} 个 GPU: {names}",
+        "doctor_gpu_not_found": "未检测到 GPU",
+        "doctor_cpu_info": "{name} ({cores} 核心 / {threads} 线程)",
+        "doctor_cpu_isa_info": "{isa_list}",
+        "doctor_cpu_isa_missing": "缺少推荐指令集: {missing}",
+        "doctor_numa_info": "{nodes} 个节点",
+        "doctor_numa_detail": "{node}: CPU {cpus}",
+        "doctor_memory_info": "{available} 可用 / {total} 总计",
+        "doctor_memory_freq": "{available} 可用 / {total} 总计 ({freq}MHz {type})",
+        "doctor_disk_info": "{path} 有 {available} 可用空间",
+        "doctor_all_ok": "所有检查通过！您的环境已就绪。",
+        "doctor_has_issues": "发现一些问题，请查看上方的警告/错误信息。",
+        # Run command
+        "run_detecting_hardware": "检测硬件配置...",
+        "run_gpu_info": "GPU: {name} ({vram}GB 显存)",
+        "run_cpu_info": "CPU: {name} ({cores} 核心, {numa} NUMA 节点)",
+        "run_ram_info": "内存: {total}GB",
+        "run_checking_model": "检查模型状态...",
+        "run_model_path": "模型路径: {path}",
+        "run_weights_not_found": "未找到量化权重",
+        "run_quant_prompt": "是否现在量化模型？(这可能需要一些时间)",
+        "run_quantizing": "正在量化模型...",
+        "run_starting_server": "正在启动服务器...",
+        "run_server_mode": "模式: SGLang + kt-kernel",
+        "run_server_port": "端口: {port}",
+        "run_gpu_experts": "GPU 专家: {count}/层",
+        "run_cpu_threads": "CPU 线程: {count}",
+        "run_server_started": "服务器已启动！",
+        "run_api_url": "API 地址: http://{host}:{port}",
+        "run_docs_url": "文档地址: http://{host}:{port}/docs",
+        "run_stop_hint": "按 Ctrl+C 停止服务器",
+        "run_model_not_found": "未找到模型 '{name}'。请先运行 'kt download'。",
+        "run_multiple_matches": "找到多个匹配的模型，请选择:",
+        "run_select_model": "选择模型",
+        "run_select_model_title": "选择要运行的模型",
+        "run_select_model_prompt": "输入编号",
+        "run_local_models": "本地模型 (已下载)",
+        "run_registered_models": "注册模型",
+        # Download command
+        "download_list_title": "可用模型",
+        "download_searching": "正在搜索模型 '{name}'...",
+        "download_found": "找到: {name}",
+        "download_multiple_found": "找到多个匹配:",
+        "download_select": "选择要下载的模型:",
+        "download_destination": "目标路径: {path}",
+        "download_starting": "开始下载...",
+        "download_progress": "正在下载 {name}...",
+        "download_complete": "下载完成！",
+        "download_already_exists": "模型已存在于 {path}",
+        "download_overwrite_prompt": "是否覆盖现有文件？",
+        # Quant command
+        "quant_input_path": "输入路径: {path}",
+        "quant_output_path": "输出路径: {path}",
+        "quant_method": "量化方法: {method}",
+        "quant_starting": "开始量化...",
+        "quant_progress": "正在量化...",
+        "quant_complete": "量化完成！",
+        "quant_input_not_found": "未找到输入模型: {path}",
+        # SFT command
+        "sft_mode_train": "训练模式",
+        "sft_mode_chat": "聊天模式",
+        "sft_mode_export": "导出模式",
+        "sft_config_path": "配置文件: {path}",
+        "sft_starting": "正在启动 {mode}...",
+        "sft_complete": "{mode} 完成！",
+        "sft_config_not_found": "未找到配置文件: {path}",
+        # Bench command
+        "bench_starting": "开始基准测试...",
+        "bench_type": "测试类型: {type}",
+        "bench_complete": "基准测试完成！",
+        "bench_results_title": "基准测试结果",
+        # Common prompts
+        "prompt_continue": "是否继续？",
+        "prompt_select": "请选择:",
+        "prompt_enter_value": "请输入:",
+        "prompt_confirm_action": "确认此操作？",
+        # First-run setup - Model path selection
+        "setup_model_path_title": "模型存储位置",
+        "setup_model_path_desc": "大语言模型体积较大（50-200GB+）。请选择一个有足够空间的存储位置：",
+        "setup_scanning_disks": "正在扫描可用存储位置...",
+        "setup_disk_option": "{path} (可用 {available} / 总共 {total})",
+        "setup_disk_option_recommended": "{path} (可用 {available} / 总共 {total}) [推荐]",
+        "setup_custom_path": "输入自定义路径",
+        "setup_enter_custom_path": "请输入模型存储路径",
+        "setup_path_not_exist": "路径不存在，是否创建？",
+        "setup_path_no_write": "没有该路径的写入权限，请选择其他路径。",
+        "setup_path_low_space": "警告：可用空间不足 100GB，可能无法存储大型模型。",
+        "setup_model_path_set": "模型存储路径已设置为: {path}",
+        "setup_no_large_disk": "未发现大容量存储位置，使用默认路径。",
+        "setup_scanning_models": "正在扫描已有模型...",
+        "setup_found_models": "发现 {count} 个模型:",
+        "setup_model_info": "{name} ({size}, {type})",
+        "setup_no_models_found": "该位置未发现已有模型。",
+        "setup_location_has_models": "发现 {count} 个模型",
+        "setup_installing_completion": "正在为 {shell} 安装命令补全...",
+        "setup_completion_installed": "命令补全已安装！重启终端后生效。",
+        "setup_completion_failed": "命令补全安装失败。请手动运行 'kt --install-completion'。",
+        # Auto completion
+        "completion_installed_title": "命令补全",
+        "completion_installed_for": "已为 {shell} 安装命令补全",
+        "completion_activate_now": "在当前终端会话中启用补全，请运行：",
+        "completion_next_session": "新的终端会话将自动启用补全。",
+        # SGLang
+        "sglang_not_found": "未找到 SGLang",
+        "sglang_pypi_warning": "PyPI 版本的 SGLang 可能与 kt-kernel 不兼容",
+        "sglang_pypi_hint": 'PyPI 版本可能不兼容。从源码安装: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_install_hint": '安装 SGLang: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_recommend_source": '建议从源码重新安装: git clone https://github.com/kvcache-ai/sglang && cd sglang && pip install -e "python[all]"',
+        "sglang_kt_kernel_not_supported": "SGLang 不支持 kt-kernel (缺少 --kt-gpu-prefill-token-threshold 参数)",
+        "sglang_checking_kt_kernel_support": "正在检查 SGLang kt-kernel 支持...",
+        "sglang_kt_kernel_supported": "SGLang kt-kernel 支持已验证",
+        # Chat
+        "chat_proxy_detected": "检测到环境中存在代理设置",
+        "chat_proxy_confirm": "是否使用代理连接？",
+        "chat_proxy_disabled": "已在本次会话中禁用代理",
+        # Model command
+        "model_supported_title": "KTransformers 支持的模型",
+        "model_column_model": "模型",
+        "model_column_status": "状态",
+        "model_column_local_path": "本地路径",
+        "model_status_local": "本地",
+        "model_status_not_downloaded": "未下载",
+        "model_usage_title": "使用方法",
+        "model_usage_download": "下载模型:",
+        "model_usage_list_local": "列出本地模型:",
+        "model_usage_search": "搜索模型:",
+        "model_storage_paths_title": "模型存储路径",
+        "model_local_models_title": "本地已下载的模型",
+        "model_available_models_title": "可用模型",
+        "model_no_local_models": "未找到本地已下载的模型",
+        "model_download_hint": "下载模型:",
+        "model_download_usage_hint": "用法: kt model download <模型名称>",
+        "model_download_list_hint": "使用 'kt model download --list' 查看可用模型。",
+        "model_download_hf_hint": "或直接指定 HuggingFace 仓库: kt model download org/model-name",
+        "model_saved_to": "模型已保存到: {path}",
+        "model_start_with": "启动命令: kt run {name}",
+        "model_download_failed": "下载失败: {error}",
+        "model_hf_cli_not_found": "未找到 huggingface-cli。请安装: pip install huggingface-hub",
+        "model_path_not_exist": "路径不存在: {path}",
+        "model_create_directory": "创建目录 {path}？",
+        "model_created_directory": "已创建目录: {path}",
+        "model_create_dir_failed": "创建目录失败: {error}",
+        "model_path_added": "已添加模型路径: {path}",
+        "model_path_removed": "已移除模型路径: {path}",
+        "model_path_not_found": "路径未找到或无法移除最后一个路径: {path}",
+        "model_search_no_results": "未找到匹配 '{query}' 的模型",
+        "model_search_results_title": "'{query}' 的搜索结果",
+        "model_column_name": "名称",
+        "model_column_hf_repo": "HuggingFace 仓库",
+        "model_column_aliases": "别名",
+        # Coming soon
+        "feature_coming_soon": "此功能即将推出...",
+    },
+}
+
+
+# Cache for language detection to avoid repeated I/O
+_lang_cache: str | None = None
+
+
+def get_lang() -> str:
+    """
+    Detect the current language setting.
+
+    Priority:
+    1. KT_LANG environment variable
+    2. Config file general.language setting
+    3. LANG environment variable (if config is "auto")
+    4. Default to English
+
+    Returns:
+        Language code: "zh" for Chinese, "en" for English
+    """
+    global _lang_cache
+
+    # 1. Check KT_LANG environment variable (highest priority)
+    kt_lang = os.environ.get("KT_LANG", "").lower()
+    if kt_lang:
+        return "zh" if kt_lang.startswith("zh") else "en"
+
+    # 2. Return cached value if available (avoids I/O on every call)
+    if _lang_cache is not None:
+        return _lang_cache
+
+    # 3. Check config file setting (with caching)
+    # Import here to avoid circular imports
+    from kt_kernel.cli.config.settings import get_settings
+
+    try:
+        settings = get_settings()
+        config_lang = settings.get("general.language", "auto")
+        if config_lang and config_lang != "auto":
+            lang = "zh" if config_lang.lower().startswith("zh") else "en"
+            _lang_cache = lang
+            return lang
+    except Exception:
+        # If settings fail to load, continue with system detection
+        pass
+
+    # 4. Check system LANG environment variable
+    system_lang = os.environ.get("LANG", "").lower()
+    lang = "zh" if system_lang.startswith("zh") else "en"
+    _lang_cache = lang
+    return lang
+
+
+def t(msg_key: str, **kwargs: Any) -> str:
+    """
+    Translate a message key to the current language.
+
+    Args:
+        msg_key: Message key to translate
+        **kwargs: Format arguments for the message
+
+    Returns:
+        Translated and formatted message string
+
+    Example:
+        >>> t("welcome")
+        "Welcome to KTransformers!"  # or "欢迎使用 KTransformers！" in Chinese
+
+        >>> t("install_found", name="conda", version="24.1.0")
+        "Found conda (version 24.1.0)"
+    """
+    lang = get_lang()
+    messages = MESSAGES.get(lang, MESSAGES["en"])
+    message = messages.get(msg_key, MESSAGES["en"].get(msg_key, msg_key))
+
+    if kwargs:
+        try:
+            return message.format(**kwargs)
+        except KeyError:
+            return message
+    return message
+
+
+def set_lang(lang: str) -> None:
+    """
+    Set the language for the current session.
+
+    Args:
+        lang: Language code ("en" or "zh")
+    """
+    global _lang_cache
+    os.environ["KT_LANG"] = lang
+    _lang_cache = lang  # Update cache when language is explicitly set

kt-kernel/python/cli/main.py

@@ -0,0 +1,436 @@
+"""
+Main entry point for kt-cli.
+
+KTransformers CLI - A unified command-line interface for KTransformers.
+"""
+
+import sys
+
+import typer
+
+from kt_kernel.cli import __version__
+from kt_kernel.cli.commands import bench, chat, config, doctor, model, quant, run, sft, version
+from kt_kernel.cli.i18n import t, set_lang, get_lang
+
+
+def _get_app_help() -> str:
+    """Get app help text based on current language."""
+    lang = get_lang()
+    if lang == "zh":
+        return "KTransformers CLI - KTransformers 统一命令行界面"
+    return "KTransformers CLI - A unified command-line interface for KTransformers."
+
+
+def _get_help(key: str) -> str:
+    """Get help text based on current language."""
+    help_texts = {
+        "version": {"en": "Show version information", "zh": "显示版本信息"},
+        "run": {"en": "Start model inference server", "zh": "启动模型推理服务器"},
+        "chat": {"en": "Interactive chat with running model", "zh": "与运行中的模型进行交互式聊天"},
+        "quant": {"en": "Quantize model weights", "zh": "量化模型权重"},
+        "bench": {"en": "Run full benchmark", "zh": "运行完整基准测试"},
+        "microbench": {"en": "Run micro-benchmark", "zh": "运行微基准测试"},
+        "doctor": {"en": "Diagnose environment issues", "zh": "诊断环境问题"},
+        "model": {"en": "Manage models and storage paths", "zh": "管理模型和存储路径"},
+        "config": {"en": "Manage configuration", "zh": "管理配置"},
+        "sft": {"en": "Fine-tuning with LlamaFactory", "zh": "使用 LlamaFactory 进行微调"},
+    }
+    lang = get_lang()
+    return help_texts.get(key, {}).get(lang, help_texts.get(key, {}).get("en", key))
+
+
+# Create main app with dynamic help
+app = typer.Typer(
+    name="kt",
+    help="KTransformers CLI - A unified command-line interface for KTransformers.",
+    no_args_is_help=True,
+    add_completion=False,  # Use static completion scripts instead of dynamic completion
+    rich_markup_mode="rich",
+)
+
+
+def _update_help_texts() -> None:
+    """Update all help texts based on current language setting."""
+    # Update main app help
+    app.info.help = _get_app_help()
+
+    # Update command help texts
+    for cmd_info in app.registered_commands:
+        # cmd_info is a CommandInfo object
+        if hasattr(cmd_info, "name") and cmd_info.name:
+            cmd_info.help = _get_help(cmd_info.name)
+
+    # Update sub-app help texts
+    for group_info in app.registered_groups:
+        if hasattr(group_info, "name") and group_info.name:
+            group_info.help = _get_help(group_info.name)
+
+
+# Register commands
+app.command(name="version", help="Show version information")(version.version)
+app.command(name="run", help="Start model inference server")(run.run)
+app.command(name="chat", help="Interactive chat with running model")(chat.chat)
+app.command(name="quant", help="Quantize model weights")(quant.quant)
+app.command(name="bench", help="Run full benchmark")(bench.bench)
+app.command(name="microbench", help="Run micro-benchmark")(bench.microbench)
+app.command(name="doctor", help="Diagnose environment issues")(doctor.doctor)
+
+# Register sub-apps
+app.add_typer(model.app, name="model", help="Manage models and storage paths")
+app.add_typer(config.app, name="config", help="Manage configuration")
+app.add_typer(sft.app, name="sft", help="Fine-tuning with LlamaFactory")
+
+
+def check_first_run() -> None:
+    """Check if this is the first run and prompt for language setup."""
+    import os
+
+    # Skip if not running in interactive terminal
+    if not sys.stdin.isatty():
+        return
+
+    from kt_kernel.cli.config.settings import DEFAULT_CONFIG_FILE
+
+    # Only check if config file exists - don't create it yet
+    if not DEFAULT_CONFIG_FILE.exists():
+        # First run - show welcome and language selection
+        from kt_kernel.cli.config.settings import get_settings
+
+        settings = get_settings()
+        _show_first_run_setup(settings)
+    else:
+        # Config exists - check if initialized
+        from kt_kernel.cli.config.settings import get_settings
+
+        settings = get_settings()
+        if not settings.get("general._initialized"):
+            _show_first_run_setup(settings)
+
+
+def _show_first_run_setup(settings) -> None:
+    """Show first-run setup wizard."""
+    from rich.console import Console
+    from rich.panel import Panel
+    from rich.prompt import Prompt, Confirm
+    from rich.spinner import Spinner
+    from rich.live import Live
+
+    from kt_kernel.cli.utils.environment import scan_storage_locations, format_size_gb, scan_models_in_location
+
+    console = Console()
+
+    # Welcome message
+    console.print()
+    console.print(
+        Panel.fit(
+            "[bold cyan]Welcome to KTransformers CLI! / 欢迎使用 KTransformers CLI![/bold cyan]\n\n"
+            "Let's set up your preferences.\n"
+            "让我们设置您的偏好。",
+            title="kt-cli",
+            border_style="cyan",
+        )
+    )
+    console.print()
+
+    # Language selection
+    console.print("[bold]Select your preferred language / 选择您的首选语言:[/bold]")
+    console.print()
+    console.print("  [cyan][1][/cyan] English")
+    console.print("  [cyan][2][/cyan] 中文 (Chinese)")
+    console.print()
+
+    while True:
+        choice = Prompt.ask("Enter choice / 输入选择", choices=["1", "2"], default="1")
+
+        if choice == "1":
+            lang = "en"
+            break
+        elif choice == "2":
+            lang = "zh"
+            break
+
+    # Save language setting
+    settings.set("general.language", lang)
+    set_lang(lang)
+
+    # Confirmation message
+    console.print()
+    if lang == "zh":
+        console.print("[green]✓[/green] 语言已设置为中文")
+    else:
+        console.print("[green]✓[/green] Language set to English")
+
+    # Model storage path selection
+    console.print()
+    console.print(f"[bold]{t('setup_model_path_title')}[/bold]")
+    console.print()
+    console.print(f"[dim]{t('setup_model_path_desc')}[/dim]")
+    console.print()
+
+    # Scan for storage locations
+    console.print(f"[dim]{t('setup_scanning_disks')}[/dim]")
+    locations = scan_storage_locations(min_size_gb=50.0)
+    console.print()
+
+    if locations:
+        # Scan for models in each location
+        console.print(f"[dim]{t('setup_scanning_models')}[/dim]")
+        location_models: dict[str, list] = {}
+        for loc in locations[:5]:
+            models = scan_models_in_location(loc, max_depth=2)
+            if models:
+                location_models[loc.path] = models
+        console.print()
+
+        # Show options
+        for i, loc in enumerate(locations[:5], 1):  # Show top 5 options
+            available = format_size_gb(loc.available_gb)
+            total = format_size_gb(loc.total_gb)
+
+            # Build the option string
+            if i == 1:
+                option_str = t("setup_disk_option_recommended", path=loc.path, available=available, total=total)
+            else:
+                option_str = t("setup_disk_option", path=loc.path, available=available, total=total)
+
+            # Add model count if any
+            if loc.path in location_models:
+                model_count = len(location_models[loc.path])
+                option_str += f" [green]✓ {t('setup_location_has_models', count=model_count)}[/green]"
+
+            console.print(f"  [cyan][{i}][/cyan] {option_str}")
+
+            # Show first few models found in this location
+            if loc.path in location_models:
+                for model in location_models[loc.path][:3]:  # Show up to 3 models
+                    size_str = format_size_gb(model.size_gb)
+                    console.print(f"      [dim]• {model.name} ({size_str})[/dim]")
+                if len(location_models[loc.path]) > 3:
+                    remaining = len(location_models[loc.path]) - 3
+                    console.print(f"      [dim]  ... +{remaining} more[/dim]")
+
+        # Custom path option
+        custom_idx = min(len(locations), 5) + 1
+        console.print(f"  [cyan][{custom_idx}][/cyan] {t('setup_custom_path')}")
+        console.print()
+
+        valid_choices = [str(i) for i in range(1, custom_idx + 1)]
+        path_choice = Prompt.ask(t("prompt_select"), choices=valid_choices, default="1")
+
+        if path_choice == str(custom_idx):
+            # Custom path
+            selected_path = _prompt_custom_path(console, settings)
+        else:
+            selected_path = locations[int(path_choice) - 1].path
+    else:
+        # No large storage found, ask for custom path
+        console.print(f"[yellow]{t('setup_no_large_disk')}[/yellow]")
+        console.print()
+        selected_path = _prompt_custom_path(console, settings)
+
+    # Ensure the path exists
+    import os
+    from pathlib import Path
+
+    if not os.path.exists(selected_path):
+        if Confirm.ask(t("setup_path_not_exist"), default=True):
+            try:
+                Path(selected_path).mkdir(parents=True, exist_ok=True)
+            except (OSError, PermissionError) as e:
+                console.print(f"[red]{t('error')}: {e}[/red]")
+                # Fall back to default
+                selected_path = str(Path.home() / ".ktransformers" / "models")
+                Path(selected_path).mkdir(parents=True, exist_ok=True)
+
+    # Check available space and warn if low
+    from kt_kernel.cli.utils.environment import detect_disk_space_gb
+
+    available_gb, _ = detect_disk_space_gb(
+        selected_path if os.path.exists(selected_path) else str(Path(selected_path).parent)
+    )
+    if available_gb < 100:
+        console.print(f"[yellow]{t('setup_path_low_space')}[/yellow]")
+
+    # Save the path
+    settings.set("paths.models", selected_path)
+    settings.set("general._initialized", True)
+
+    console.print()
+    console.print(f"[green]✓[/green] {t('setup_model_path_set', path=selected_path)}")
+    console.print()
+
+    # Tips
+    if lang == "zh":
+        console.print("[dim]提示: 运行 'kt config show' 查看所有配置[/dim]")
+    else:
+        console.print("[dim]Tip: Run 'kt config show' to view all settings[/dim]")
+
+    console.print()
+
+
+def _prompt_custom_path(console, settings) -> str:
+    """Prompt user to enter a custom path."""
+    from rich.prompt import Prompt
+    from pathlib import Path
+    import os
+
+    default_path = str(Path.home() / ".ktransformers" / "models")
+
+    while True:
+        custom_path = Prompt.ask(t("setup_enter_custom_path"), default=default_path)
+
+        # Expand user home
+        custom_path = os.path.expanduser(custom_path)
+
+        # Check if path exists or parent is writable
+        if os.path.exists(custom_path):
+            if os.access(custom_path, os.W_OK):
+                return custom_path
+            else:
+                console.print(f"[red]{t('setup_path_no_write')}[/red]")
+        else:
+            # Check if we can create it (parent writable)
+            parent = str(Path(custom_path).parent)
+            while not os.path.exists(parent) and parent != "/":
+                parent = str(Path(parent).parent)
+
+            if os.access(parent, os.W_OK):
+                return custom_path
+            else:
+                console.print(f"[red]{t('setup_path_no_write')}[/red]")
+
+
+def _install_shell_completion() -> None:
+    """Install shell completion scripts to user directories.
+
+    Uses standard locations that are auto-loaded by shell completion systems:
+    - Bash: ~/.local/share/bash-completion/completions/kt (auto-loaded by bash-completion 2.0+)
+    - Zsh: ~/.zfunc/_kt (requires fpath setup, but commonly used)
+    - Fish: ~/.config/fish/completions/kt.fish (auto-loaded)
+    """
+    import os
+    import shutil
+    from pathlib import Path
+
+    from kt_kernel.cli.config.settings import get_settings
+
+    settings = get_settings()
+
+    # Check if already installed
+    if settings.get("general._completion_installed", False):
+        return
+
+    # Detect current shell
+    shell = os.environ.get("SHELL", "")
+    if "zsh" in shell:
+        shell_name = "zsh"
+    elif "fish" in shell:
+        shell_name = "fish"
+    else:
+        shell_name = "bash"
+
+    try:
+        cli_dir = Path(__file__).parent
+        completions_dir = cli_dir / "completions"
+        home = Path.home()
+
+        installed = False
+
+        if shell_name == "bash":
+            # Use XDG standard location for bash-completion (auto-loaded)
+            src_file = completions_dir / "kt-completion.bash"
+            dest_dir = home / ".local" / "share" / "bash-completion" / "completions"
+            dest_file = dest_dir / "kt"
+
+            if src_file.exists():
+                dest_dir.mkdir(parents=True, exist_ok=True)
+                shutil.copy2(src_file, dest_file)
+                installed = True
+
+        elif shell_name == "zsh":
+            src_file = completions_dir / "_kt"
+            dest_dir = home / ".zfunc"
+            dest_file = dest_dir / "_kt"
+
+            if src_file.exists():
+                dest_dir.mkdir(parents=True, exist_ok=True)
+                shutil.copy2(src_file, dest_file)
+                installed = True
+
+        elif shell_name == "fish":
+            # Fish auto-loads from this directory
+            src_file = completions_dir / "kt.fish"
+            dest_dir = home / ".config" / "fish" / "completions"
+            dest_file = dest_dir / "kt.fish"
+
+            if src_file.exists():
+                dest_dir.mkdir(parents=True, exist_ok=True)
+                shutil.copy2(src_file, dest_file)
+                installed = True
+
+        # Mark as installed
+        settings.set("general._completion_installed", True)
+
+        # For bash/zsh, completion will work in new terminals automatically
+        # (bash-completion 2.0+ auto-loads from ~/.local/share/bash-completion/completions/)
+
+    except (OSError, IOError):
+        # Silently ignore errors - completion is not critical
+        pass
+
+
+def _apply_saved_language() -> None:
+    """Apply the saved language setting.
+
+    Priority:
+    1. KT_LANG environment variable (if already set, don't override)
+    2. Config file setting
+    3. System locale (auto)
+    """
+    import os
+
+    # Don't override if KT_LANG is already set by user
+    if os.environ.get("KT_LANG"):
+        return
+
+    from kt_kernel.cli.config.settings import get_settings
+
+    settings = get_settings()
+    lang = settings.get("general.language", "auto")
+
+    if lang != "auto":
+        set_lang(lang)
+
+
+def main():
+    """Main entry point."""
+    # Apply saved language setting first (before anything else for correct help display)
+    _apply_saved_language()
+
+    # Update help texts based on language
+    _update_help_texts()
+
+    # Check for first run (but not for certain commands)
+    # Skip first-run check for: --help, config commands, version
+    args = sys.argv[1:] if len(sys.argv) > 1 else []
+    skip_commands = ["--help", "-h", "config", "version", "--version"]
+
+    should_check_first_run = True
+    for arg in args:
+        if arg in skip_commands:
+            should_check_first_run = False
+            break
+
+    # Auto-install shell completion on first run
+    if should_check_first_run:
+        _install_shell_completion()
+
+    # Check first run before running commands
+    if should_check_first_run and args:
+        check_first_run()
+
+    app()
+
+
+if __name__ == "__main__":
+    main()

kt-kernel/python/cli/requirements/inference.txt

@@ -0,0 +1,6 @@
+# Inference dependencies for KTransformers
+# NOTE: sglang is installed separately from source (see install.py)
+
+transformers>=4.45.0
+safetensors>=0.4.0
+huggingface-hub>=0.20.0

kt-kernel/python/cli/requirements/sft.txt

@@ -0,0 +1,7 @@
+# SFT (Supervised Fine-Tuning) dependencies for KTransformers
+
+llamafactory>=0.9.0
+peft>=0.12.0
+transformers>=4.45.0
+datasets>=2.14.0
+accelerate>=0.30.0

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

@@ -0,0 +1,3 @@
+"""
+Utility modules for kt-cli.
+"""

kt-kernel/python/cli/utils/console.py

@@ -0,0 +1,249 @@
+"""
+Console utilities for kt-cli.
+
+Provides Rich-based console output helpers for consistent formatting.
+"""
+
+from typing import Optional
+
+from rich.console import Console
+from rich.panel import Panel
+from rich.progress import (
+    BarColumn,
+    DownloadColumn,
+    Progress,
+    SpinnerColumn,
+    TaskProgressColumn,
+    TextColumn,
+    TimeElapsedColumn,
+    TimeRemainingColumn,
+    TransferSpeedColumn,
+)
+from rich.prompt import Confirm, Prompt
+from rich.table import Table
+from rich.theme import Theme
+
+from kt_kernel.cli.i18n import t
+
+# Custom theme for kt-cli
+KT_THEME = Theme(
+    {
+        "info": "cyan",
+        "warning": "yellow",
+        "error": "bold red",
+        "success": "bold green",
+        "highlight": "bold magenta",
+        "muted": "dim",
+    }
+)
+
+# Global console instance
+console = Console(theme=KT_THEME)
+
+
+def print_info(message: str, **kwargs) -> None:
+    """Print an info message."""
+    console.print(f"[info]ℹ[/info] {message}", **kwargs)
+
+
+def print_success(message: str, **kwargs) -> None:
+    """Print a success message."""
+    console.print(f"[success]✓[/success] {message}", **kwargs)
+
+
+def print_warning(message: str, **kwargs) -> None:
+    """Print a warning message."""
+    console.print(f"[warning]⚠[/warning] {message}", **kwargs)
+
+
+def print_error(message: str, **kwargs) -> None:
+    """Print an error message."""
+    console.print(f"[error]✗[/error] {message}", **kwargs)
+
+
+def print_step(message: str, **kwargs) -> None:
+    """Print a step indicator."""
+    console.print(f"[highlight]→[/highlight] {message}", **kwargs)
+
+
+def print_header(title: str, subtitle: Optional[str] = None) -> None:
+    """Print a header panel."""
+    content = f"[bold]{title}[/bold]"
+    if subtitle:
+        content += f"\n[muted]{subtitle}[/muted]"
+    console.print(Panel(content, expand=False))
+
+
+def print_version_table(versions: dict[str, Optional[str]]) -> None:
+    """Print a version information table."""
+    table = Table(show_header=False, box=None, padding=(0, 2))
+    table.add_column("Component", style="bold")
+    table.add_column("Version")
+
+    for name, version in versions.items():
+        if version:
+            table.add_row(name, f"[success]{version}[/success]")
+        else:
+            table.add_row(name, f"[muted]{t('version_not_installed')}[/muted]")
+
+    console.print(table)
+
+
+def print_dependency_table(deps: list[dict]) -> None:
+    """Print a dependency status table."""
+    table = Table(title=t("install_checking_deps"))
+    table.add_column(t("version_info"), style="bold")
+    table.add_column("Current")
+    table.add_column("Required")
+    table.add_column("Status")
+
+    for dep in deps:
+        status = dep.get("status", "ok")
+        if status == "ok":
+            status_str = f"[success]{t('install_dep_ok')}[/success]"
+        elif status == "outdated":
+            status_str = f"[warning]{t('install_dep_outdated')}[/warning]"
+        else:
+            status_str = f"[error]{t('install_dep_missing')}[/error]"
+
+        table.add_row(
+            dep["name"],
+            dep.get("installed", "-"),
+            dep.get("required", "-"),
+            status_str,
+        )
+
+    console.print(table)
+
+
+def confirm(message: str, default: bool = True) -> bool:
+    """Ask for confirmation."""
+    return Confirm.ask(message, default=default, console=console)
+
+
+def prompt_choice(message: str, choices: list[str], default: Optional[str] = None) -> str:
+    """Prompt for a choice from a list."""
+    # Display numbered choices
+    console.print(f"\n[bold]{message}[/bold]")
+    for i, choice in enumerate(choices, 1):
+        console.print(f"  [highlight][{i}][/highlight] {choice}")
+
+    while True:
+        response = Prompt.ask(
+            "\n" + t("prompt_select"),
+            console=console,
+            default=str(choices.index(default) + 1) if default else None,
+        )
+        try:
+            idx = int(response) - 1
+            if 0 <= idx < len(choices):
+                return choices[idx]
+        except ValueError:
+            # Check if response matches a choice directly
+            if response in choices:
+                return response
+
+        print_error(f"Please enter a number between 1 and {len(choices)}")
+
+
+def prompt_text(message: str, default: Optional[str] = None) -> str:
+    """Prompt for text input."""
+    return Prompt.ask(message, console=console, default=default)
+
+
+def create_progress() -> Progress:
+    """Create a progress bar for general tasks."""
+    return Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        BarColumn(),
+        TaskProgressColumn(),
+        TimeElapsedColumn(),
+        console=console,
+    )
+
+
+def create_download_progress() -> Progress:
+    """Create a progress bar for downloads."""
+    return Progress(
+        SpinnerColumn(),
+        TextColumn("[progress.description]{task.description}"),
+        BarColumn(),
+        DownloadColumn(),
+        TransferSpeedColumn(),
+        TimeRemainingColumn(),
+        console=console,
+    )
+
+
+def print_model_table(models: list[dict]) -> None:
+    """Print a table of models."""
+    table = Table(title=t("download_list_title"))
+    table.add_column("Name", style="bold")
+    table.add_column("Repository")
+    table.add_column("Type")
+    table.add_column("Requirements")
+
+    for model in models:
+        reqs = []
+        if model.get("gpu_vram_gb"):
+            reqs.append(f"GPU: {model['gpu_vram_gb']}GB")
+        if model.get("cpu_ram_gb"):
+            reqs.append(f"RAM: {model['cpu_ram_gb']}GB")
+
+        table.add_row(
+            model.get("name", ""),
+            model.get("hf_repo", ""),
+            model.get("type", ""),
+            ", ".join(reqs) if reqs else "-",
+        )
+
+    console.print(table)
+
+
+def print_hardware_info(gpu_info: str, cpu_info: str, ram_info: str) -> None:
+    """Print hardware information."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Icon", width=3)
+    table.add_column("Info")
+
+    table.add_row("🖥️", gpu_info)
+    table.add_row("💻", cpu_info)
+    table.add_row("🧠", ram_info)
+
+    console.print(Panel(table, title="Hardware", expand=False))
+
+
+def print_server_info(
+    mode: str, host: str, port: int, gpu_experts: int, cpu_threads: int
+) -> None:
+    """Print server startup information."""
+    table = Table(show_header=False, box=None)
+    table.add_column("Key", style="bold")
+    table.add_column("Value")
+
+    table.add_row(t("run_server_mode").split(":")[0], mode)
+    table.add_row("Host", host)
+    table.add_row("Port", str(port))
+    table.add_row(t("run_gpu_experts").split(":")[0], f"{gpu_experts}/layer")
+    table.add_row(t("run_cpu_threads").split(":")[0], str(cpu_threads))
+
+    console.print(Panel(table, title=t("run_server_started"), expand=False, border_style="green"))
+
+
+def print_api_info(host: str, port: int) -> None:
+    """Print API endpoint information."""
+    api_url = f"http://{host}:{port}"
+    docs_url = f"http://{host}:{port}/docs"
+
+    console.print()
+    console.print(f"  {t('run_api_url', host=host, port=port)}")
+    console.print(f"  {t('run_docs_url', host=host, port=port)}")
+    console.print()
+    console.print(f"  [muted]Test command:[/muted]")
+    console.print(
+        f"  [dim]curl {api_url}/v1/chat/completions -H 'Content-Type: application/json' "
+        f"-d '{{\"model\": \"default\", \"messages\": [{{\"role\": \"user\", \"content\": \"Hello\"}}]}}'[/dim]"
+    )
+    console.print()
+    console.print(f"  [muted]{t('run_stop_hint')}[/muted]")

kt-kernel/python/cli/utils/model_registry.py

@@ -0,0 +1,374 @@
+"""
+Model registry for kt-cli.
+
+Provides a registry of supported models with fuzzy matching capabilities.
+"""
+
+import re
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Callable, Optional
+
+import yaml
+
+from kt_kernel.cli.config.settings import get_settings
+
+
+@dataclass
+class ModelInfo:
+    """Information about a supported model."""
+
+    name: str
+    hf_repo: str
+    aliases: list[str] = field(default_factory=list)
+    type: str = "moe"  # moe, dense
+    gpu_vram_gb: float = 0
+    cpu_ram_gb: float = 0
+    default_params: dict = field(default_factory=dict)
+    description: str = ""
+    description_zh: str = ""
+    max_tensor_parallel_size: Optional[int] = None  # Maximum tensor parallel size for this model
+
+
+# Built-in model registry
+BUILTIN_MODELS: list[ModelInfo] = [
+    ModelInfo(
+        name="DeepSeek-V3-0324",
+        hf_repo="deepseek-ai/DeepSeek-V3-0324",
+        aliases=["deepseek-v3-0324", "deepseek-v3", "dsv3", "deepseek3", "v3-0324"],
+        type="moe",
+        default_params={
+            "kt-num-gpu-experts": 1,
+            "attention-backend": "triton",
+            "disable-shared-experts-fusion": True,
+            "kt-method": "AMXINT4",
+        },
+        description="DeepSeek V3-0324 685B MoE model (March 2025, improved benchmarks)",
+        description_zh="DeepSeek V3-0324 685B MoE 模型（2025年3月，改进的基准测试）",
+    ),
+    ModelInfo(
+        name="DeepSeek-V3.2",
+        hf_repo="deepseek-ai/DeepSeek-V3.2",
+        aliases=["deepseek-v3.2", "dsv3.2", "deepseek3.2", "v3.2"],
+        type="moe",
+        default_params={
+            "kt-method": "FP8",
+            "kt-gpu-prefill-token-threshold": 4096,
+            "attention-backend": "flashinfer",
+            "fp8-gemm-backend": "triton",
+            "max-total-tokens": 100000,
+            "max-running-requests": 16,
+            "chunked-prefill-size": 32768,
+            "mem-fraction-static": 0.80,
+            "watchdog-timeout": 3000,
+            "served-model-name": "DeepSeek-V3.2",
+            "disable-shared-experts-fusion": True,
+        },
+        description="DeepSeek V3.2 671B MoE model (latest)",
+        description_zh="DeepSeek V3.2 671B MoE 模型（最新）",
+    ),
+    ModelInfo(
+        name="DeepSeek-R1-0528",
+        hf_repo="deepseek-ai/DeepSeek-R1-0528",
+        aliases=["deepseek-r1-0528", "deepseek-r1", "dsr1", "r1", "r1-0528"],
+        type="moe",
+        default_params={
+            "kt-num-gpu-experts": 1,
+            "attention-backend": "triton",
+            "disable-shared-experts-fusion": True,
+            "kt-method": "AMXINT4",
+        },
+        description="DeepSeek R1-0528 reasoning model (May 2025, improved reasoning depth)",
+        description_zh="DeepSeek R1-0528 推理模型（2025年5月，改进的推理深度）",
+    ),
+    ModelInfo(
+        name="Kimi-K2-Thinking",
+        hf_repo="moonshotai/Kimi-K2-Thinking",
+        aliases=["kimi-k2-thinking", "kimi-thinking", "k2-thinking", "kimi", "k2"],
+        type="moe",
+        default_params={
+            "kt-method": "RAWINT4",
+            "kt-gpu-prefill-token-threshold": 400,
+            "attention-backend": "flashinfer",
+            "max-total-tokens": 100000,
+            "max-running-requests": 16,
+            "chunked-prefill-size": 32768,
+            "mem-fraction-static": 0.80,
+            "watchdog-timeout": 3000,
+            "served-model-name": "Kimi-K2-Thinking",
+            "disable-shared-experts-fusion": True,
+        },
+        description="Moonshot Kimi K2 Thinking MoE model",
+        description_zh="月之暗面 Kimi K2 Thinking MoE 模型",
+    ),
+    ModelInfo(
+        name="MiniMax-M2",
+        hf_repo="MiniMaxAI/MiniMax-M2",
+        aliases=["minimax-m2", "m2"],
+        type="moe",
+        default_params={
+            "kt-method": "FP8",
+            "kt-gpu-prefill-token-threshold": 4096,
+            "attention-backend": "flashinfer",
+            "fp8-gemm-backend": "triton",
+            "max-total-tokens": 100000,
+            "max-running-requests": 16,
+            "chunked-prefill-size": 32768,
+            "mem-fraction-static": 0.80,
+            "watchdog-timeout": 3000,
+            "served-model-name": "MiniMax-M2",
+            "disable-shared-experts-fusion": True,
+            "tool-call-parser": "minimax-m2",
+            "reasoning-parser": "minimax-append-think",
+        },
+        description="MiniMax M2 MoE model",
+        description_zh="MiniMax M2 MoE 模型",
+        max_tensor_parallel_size=4,  # M2 only supports up to 4-way tensor parallelism
+    ),
+    ModelInfo(
+        name="MiniMax-M2.1",
+        hf_repo="MiniMaxAI/MiniMax-M2.1",
+        aliases=["minimax-m2.1", "m2.1"],
+        type="moe",
+        default_params={
+            "kt-method": "FP8",
+            "kt-gpu-prefill-token-threshold": 4096,
+            "attention-backend": "flashinfer",
+            "fp8-gemm-backend": "triton",
+            "max-total-tokens": 100000,
+            "max-running-requests": 16,
+            "chunked-prefill-size": 32768,
+            "mem-fraction-static": 0.80,
+            "watchdog-timeout": 3000,
+            "served-model-name": "MiniMax-M2.1",
+            "disable-shared-experts-fusion": True,
+            "tool-call-parser": "minimax-m2",
+            "reasoning-parser": "minimax-append-think",
+        },
+        description="MiniMax M2.1 MoE model (enhanced multi-language programming)",
+        description_zh="MiniMax M2.1 MoE 模型（增强多语言编程能力）",
+        max_tensor_parallel_size=4,  # M2.1 only supports up to 4-way tensor parallelism
+    ),
+]
+
+
+class ModelRegistry:
+    """Registry of supported models with fuzzy matching."""
+
+    def __init__(self):
+        """Initialize the model registry."""
+        self._models: dict[str, ModelInfo] = {}
+        self._aliases: dict[str, str] = {}
+        self._load_builtin_models()
+        self._load_user_models()
+
+    def _load_builtin_models(self) -> None:
+        """Load built-in models."""
+        for model in BUILTIN_MODELS:
+            self._register(model)
+
+    def _load_user_models(self) -> None:
+        """Load user-defined models from config."""
+        settings = get_settings()
+        registry_file = settings.config_dir / "registry.yaml"
+
+        if registry_file.exists():
+            try:
+                with open(registry_file, "r", encoding="utf-8") as f:
+                    data = yaml.safe_load(f) or {}
+
+                for name, info in data.get("models", {}).items():
+                    model = ModelInfo(
+                        name=name,
+                        hf_repo=info.get("hf_repo", ""),
+                        aliases=info.get("aliases", []),
+                        type=info.get("type", "moe"),
+                        gpu_vram_gb=info.get("gpu_vram_gb", 0),
+                        cpu_ram_gb=info.get("cpu_ram_gb", 0),
+                        default_params=info.get("default_params", {}),
+                        description=info.get("description", ""),
+                        description_zh=info.get("description_zh", ""),
+                        max_tensor_parallel_size=info.get("max_tensor_parallel_size"),
+                    )
+                    self._register(model)
+            except (yaml.YAMLError, OSError):
+                pass
+
+    def _register(self, model: ModelInfo) -> None:
+        """Register a model."""
+        self._models[model.name.lower()] = model
+
+        # Register aliases
+        for alias in model.aliases:
+            self._aliases[alias.lower()] = model.name.lower()
+
+    def get(self, name: str) -> Optional[ModelInfo]:
+        """Get a model by exact name or alias."""
+        name_lower = name.lower()
+
+        # Check direct match
+        if name_lower in self._models:
+            return self._models[name_lower]
+
+        # Check aliases
+        if name_lower in self._aliases:
+            return self._models[self._aliases[name_lower]]
+
+        return None
+
+    def search(self, query: str, limit: int = 10) -> list[ModelInfo]:
+        """Search for models using fuzzy matching.
+
+        Args:
+            query: Search query
+            limit: Maximum number of results
+
+        Returns:
+            List of matching models, sorted by relevance
+        """
+        query_lower = query.lower()
+        results: list[tuple[float, ModelInfo]] = []
+
+        for model in self._models.values():
+            score = self._match_score(query_lower, model)
+            if score > 0:
+                results.append((score, model))
+
+        # Sort by score descending
+        results.sort(key=lambda x: x[0], reverse=True)
+
+        return [model for _, model in results[:limit]]
+
+    def _match_score(self, query: str, model: ModelInfo) -> float:
+        """Calculate match score for a model.
+
+        Returns a score between 0 and 1, where 1 is an exact match.
+        """
+        # Check exact match
+        if query == model.name.lower():
+            return 1.0
+
+        # Check alias exact match
+        for alias in model.aliases:
+            if query == alias.lower():
+                return 0.95
+
+        # Check if query is contained in name
+        if query in model.name.lower():
+            return 0.8
+
+        # Check if query is contained in aliases
+        for alias in model.aliases:
+            if query in alias.lower():
+                return 0.7
+
+        # Check if query is contained in hf_repo
+        if query in model.hf_repo.lower():
+            return 0.6
+
+        # Fuzzy matching - check if all query parts are present
+        query_parts = re.split(r"[-_.\s]", query)
+        name_lower = model.name.lower()
+
+        matches = sum(1 for part in query_parts if part and part in name_lower)
+        if matches > 0:
+            return 0.5 * (matches / len(query_parts))
+
+        return 0.0
+
+    def list_all(self) -> list[ModelInfo]:
+        """List all registered models."""
+        return list(self._models.values())
+
+    def find_local_models(self) -> list[tuple[ModelInfo, Path]]:
+        """Find models that are downloaded locally in any configured model path.
+
+        Returns:
+            List of (ModelInfo, path) tuples for local models
+        """
+        settings = get_settings()
+        model_paths = settings.get_model_paths()
+        results = []
+
+        for model in self._models.values():
+            found = False
+            # Search in all configured model directories
+            for models_dir in model_paths:
+                if not models_dir.exists():
+                    continue
+
+                # Check common path patterns
+                possible_paths = [
+                    models_dir / model.name,
+                    models_dir / model.name.lower(),
+                    models_dir / model.hf_repo.split("/")[-1],
+                    models_dir / model.hf_repo.replace("/", "--"),
+                ]
+
+                for path in possible_paths:
+                    if path.exists() and (path / "config.json").exists():
+                        results.append((model, path))
+                        found = True
+                        break
+
+                if found:
+                    break
+
+        return results
+
+
+# Global registry instance
+_registry: Optional[ModelRegistry] = None
+
+
+def get_registry() -> ModelRegistry:
+    """Get the global model registry instance."""
+    global _registry
+    if _registry is None:
+        _registry = ModelRegistry()
+    return _registry
+
+
+# ============================================================================
+# Model-specific parameter computation functions
+# ============================================================================
+
+
+def compute_deepseek_v3_gpu_experts(tensor_parallel_size: int, vram_per_gpu_gb: float) -> int:
+    per_gpu_gb = 16
+    if vram_per_gpu_gb < per_gpu_gb:
+        return int(0)
+    total_vram = int(tensor_parallel_size * (vram_per_gpu_gb - per_gpu_gb))
+
+    return total_vram // 3
+
+
+def compute_kimi_k2_thinking_gpu_experts(tensor_parallel_size: int, vram_per_gpu_gb: float) -> int:
+    """Compute kt-num-gpu-experts for Kimi K2 Thinking."""
+    per_gpu_gb = 16
+    if vram_per_gpu_gb < per_gpu_gb:
+        return int(0)
+    total_vram = int(tensor_parallel_size * (vram_per_gpu_gb - per_gpu_gb))
+
+    return total_vram * 2 // 3
+
+
+def compute_minimax_m2_gpu_experts(tensor_parallel_size: int, vram_per_gpu_gb: float) -> int:
+    """Compute kt-num-gpu-experts for MiniMax M2/M2.1."""
+    per_gpu_gb = 16
+    if vram_per_gpu_gb < per_gpu_gb:
+        return int(0)
+    total_vram = int(tensor_parallel_size * (vram_per_gpu_gb - per_gpu_gb))
+
+    return total_vram // 1
+
+
+# Model name to computation function mapping
+MODEL_COMPUTE_FUNCTIONS: dict[str, Callable[[int, float], int]] = {
+    "DeepSeek-V3-0324": compute_deepseek_v3_gpu_experts,
+    "DeepSeek-V3.2": compute_deepseek_v3_gpu_experts,  # Same as V3-0324
+    "DeepSeek-R1-0528": compute_deepseek_v3_gpu_experts,  # Same as V3-0324
+    "Kimi-K2-Thinking": compute_kimi_k2_thinking_gpu_experts,
+    "MiniMax-M2": compute_minimax_m2_gpu_experts,
+    "MiniMax-M2.1": compute_minimax_m2_gpu_experts,  # Same as M2
+}

kt-kernel/python/cli/utils/sglang_checker.py

@@ -0,0 +1,407 @@
+"""
+SGLang installation checker and installation instructions provider.
+
+This module provides utilities to:
+- Check if SGLang is installed and get its metadata
+- Provide installation instructions when SGLang is not found
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+from typing import Optional
+
+from kt_kernel.cli.i18n import t
+from kt_kernel.cli.utils.console import console
+
+
+def check_sglang_installation() -> dict:
+    """Check if SGLang is installed and get its metadata.
+
+    Returns:
+        dict with keys:
+        - installed: bool
+        - version: str or None
+        - location: str or None (installation path)
+        - editable: bool (whether installed in editable mode)
+        - git_info: dict or None (git remote and branch if available)
+        - from_source: bool (whether installed from source repository)
+    """
+    try:
+        # Try to import sglang
+        import sglang
+
+        version = getattr(sglang, "__version__", None)
+
+        # Use pip show to get detailed package information
+        location = None
+        editable = False
+        git_info = None
+        from_source = False
+
+        try:
+            # Get pip show output
+            result = subprocess.run(
+                [sys.executable, "-m", "pip", "show", "sglang"],
+                capture_output=True,
+                text=True,
+                timeout=10,
+            )
+
+            if result.returncode == 0:
+                pip_info = {}
+                for line in result.stdout.split("\n"):
+                    if ":" in line:
+                        key, value = line.split(":", 1)
+                        pip_info[key.strip()] = value.strip()
+
+                location = pip_info.get("Location")
+                editable_location = pip_info.get("Editable project location")
+
+                if editable_location:
+                    editable = True
+                    location = editable_location
+        except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
+            # Fallback to module location
+            if hasattr(sglang, "__file__") and sglang.__file__:
+                location = str(Path(sglang.__file__).parent.parent)
+
+        # Check if it's installed from source (has .git directory)
+        if location:
+            git_root = None
+            check_path = Path(location)
+
+            # Check current directory and up to 2 parent directories
+            for _ in range(3):
+                git_dir = check_path / ".git"
+                if git_dir.exists():
+                    git_root = check_path
+                    from_source = True
+                    break
+                if check_path.parent == check_path:  # Reached root
+                    break
+                check_path = check_path.parent
+
+            if from_source and git_root:
+                # Try to get git remote and branch info
+                try:
+                    # Get remote URL
+                    result = subprocess.run(
+                        ["git", "remote", "get-url", "origin"],
+                        cwd=git_root,
+                        capture_output=True,
+                        text=True,
+                        timeout=5,
+                    )
+                    remote_url = result.stdout.strip() if result.returncode == 0 else None
+
+                    # Extract org/repo from URL
+                    remote_short = None
+                    if remote_url:
+                        # Handle both https and git@ URLs
+                        if "github.com" in remote_url:
+                            parts = remote_url.rstrip("/").replace(".git", "").split("github.com")[-1]
+                            remote_short = parts.lstrip("/").lstrip(":")
+
+                    # Get current branch
+                    result = subprocess.run(
+                        ["git", "branch", "--show-current"],
+                        cwd=git_root,
+                        capture_output=True,
+                        text=True,
+                        timeout=5,
+                    )
+                    branch = result.stdout.strip() if result.returncode == 0 else None
+
+                    if remote_url or branch:
+                        git_info = {
+                            "remote": remote_short or remote_url,
+                            "branch": branch,
+                        }
+                except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
+                    pass
+
+        return {
+            "installed": True,
+            "version": version,
+            "location": location,
+            "editable": editable,
+            "git_info": git_info,
+            "from_source": from_source,
+        }
+    except ImportError:
+        return {
+            "installed": False,
+            "version": None,
+            "location": None,
+            "editable": False,
+            "git_info": None,
+            "from_source": False,
+        }
+
+
+def get_sglang_install_instructions(lang: Optional[str] = None) -> str:
+    """Get SGLang installation instructions.
+
+    Args:
+        lang: Language code ('en' or 'zh'). If None, uses current language setting.
+
+    Returns:
+        Formatted installation instructions string.
+    """
+    from kt_kernel.cli.i18n import get_lang
+
+    if lang is None:
+        lang = get_lang()
+
+    if lang == "zh":
+        return """
+[bold yellow]SGLang \u672a\u5b89\u88c5[/bold yellow]
+
+\u8bf7\u6309\u7167\u4ee5\u4e0b\u6b65\u9aa4\u5b89\u88c5 SGLang:
+
+[bold]1. \u514b\u9686\u4ed3\u5e93:[/bold]
+   git clone https://github.com/kvcache-ai/sglang.git
+   cd sglang
+
+[bold]2. \u5b89\u88c5 (\u4e8c\u9009\u4e00):[/bold]
+
+   [cyan]\u65b9\u5f0f A - pip \u5b89\u88c5 (\u63a8\u8350):[/cyan]
+   pip install -e "python[all]"
+
+   [cyan]\u65b9\u5f0f B - uv \u5b89\u88c5 (\u66f4\u5feb):[/cyan]
+   pip install uv
+   uv pip install -e "python[all]"
+
+[dim]\u6ce8\u610f: \u8bf7\u786e\u4fdd\u5728\u6b63\u786e\u7684 Python \u73af\u5883\u4e2d\u6267\u884c\u4ee5\u4e0a\u547d\u4ee4[/dim]
+"""
+    else:
+        return """
+[bold yellow]SGLang is not installed[/bold yellow]
+
+Please follow these steps to install SGLang:
+
+[bold]1. Clone the repository:[/bold]
+   git clone https://github.com/kvcache-ai/sglang.git
+   cd sglang
+
+[bold]2. Install (choose one):[/bold]
+
+   [cyan]Option A - pip install (recommended):[/cyan]
+   pip install -e "python[all]"
+
+   [cyan]Option B - uv install (faster):[/cyan]
+   pip install uv
+   uv pip install -e "python[all]"
+
+[dim]Note: Make sure to run these commands in the correct Python environment[/dim]
+"""
+
+
+def print_sglang_install_instructions() -> None:
+    """Print SGLang installation instructions to console."""
+    instructions = get_sglang_install_instructions()
+    console.print(instructions)
+
+
+def check_sglang_and_warn() -> bool:
+    """Check if SGLang is installed, print warning if not.
+
+    Returns:
+        True if SGLang is installed, False otherwise.
+    """
+    info = check_sglang_installation()
+
+    if not info["installed"]:
+        print_sglang_install_instructions()
+        return False
+
+    # Check if installed from PyPI (not recommended)
+    if info["installed"] and not info["from_source"]:
+        from kt_kernel.cli.utils.console import print_warning
+
+        print_warning(t("sglang_pypi_warning"))
+        console.print()
+        console.print("[dim]" + t("sglang_recommend_source") + "[/dim]")
+        console.print()
+
+    return True
+
+
+def _get_sglang_kt_kernel_cache_path() -> Path:
+    """Get the path to the sglang kt-kernel support cache file."""
+    cache_dir = Path.home() / ".ktransformers" / "cache"
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    return cache_dir / "sglang_kt_kernel_supported"
+
+
+def _is_sglang_kt_kernel_cache_valid() -> bool:
+    """Check if the sglang kt-kernel support cache is valid.
+
+    The cache is considered valid if:
+    1. The cache file exists
+    2. The cache file contains 'true' (indicating previous check passed)
+
+    Returns:
+        True if cache is valid and indicates support, False otherwise.
+    """
+    cache_path = _get_sglang_kt_kernel_cache_path()
+    if cache_path.exists():
+        try:
+            content = cache_path.read_text().strip().lower()
+            return content == "true"
+        except (OSError, IOError):
+            pass
+    return False
+
+
+def _save_sglang_kt_kernel_cache(supported: bool) -> None:
+    """Save the sglang kt-kernel support check result to cache."""
+    cache_path = _get_sglang_kt_kernel_cache_path()
+    try:
+        cache_path.write_text("true" if supported else "false")
+    except (OSError, IOError):
+        pass  # Ignore cache write errors
+
+
+def clear_sglang_kt_kernel_cache() -> None:
+    """Clear the sglang kt-kernel support cache, forcing a re-check on next run."""
+    cache_path = _get_sglang_kt_kernel_cache_path()
+    try:
+        if cache_path.exists():
+            cache_path.unlink()
+    except (OSError, IOError):
+        pass
+
+
+def check_sglang_kt_kernel_support(use_cache: bool = True, silent: bool = False) -> dict:
+    """Check if SGLang supports kt-kernel parameters (--kt-gpu-prefill-token-threshold).
+
+    This function runs `python -m sglang.launch_server --help` and checks if the
+    output contains the `--kt-gpu-prefill-token-threshold` parameter. This parameter
+    is only available in the kvcache-ai/sglang fork, not in the official sglang.
+
+    The result is cached after the first successful check to avoid repeated checks.
+
+    Args:
+        use_cache: If True, use cached result if available. Default is True.
+        silent: If True, don't print checking message. Default is False.
+
+    Returns:
+        dict with keys:
+        - supported: bool - True if kt-kernel parameters are supported
+        - help_output: str or None - The help output from sglang.launch_server
+        - error: str or None - Error message if check failed
+        - from_cache: bool - True if result was from cache
+    """
+    from kt_kernel.cli.utils.console import print_step
+
+    # Check cache first
+    if use_cache and _is_sglang_kt_kernel_cache_valid():
+        return {
+            "supported": True,
+            "help_output": None,
+            "error": None,
+            "from_cache": True,
+        }
+
+    # Print checking message
+    if not silent:
+        print_step(t("sglang_checking_kt_kernel_support"))
+
+    try:
+        result = subprocess.run(
+            [sys.executable, "-m", "sglang.launch_server", "--help"],
+            capture_output=True,
+            text=True,
+            timeout=30,
+        )
+
+        help_output = result.stdout + result.stderr
+
+        # Check if --kt-gpu-prefill-token-threshold is in the help output
+        supported = "--kt-gpu-prefill-token-threshold" in help_output
+
+        # Save to cache if supported
+        if supported:
+            _save_sglang_kt_kernel_cache(True)
+
+        return {
+            "supported": supported,
+            "help_output": help_output,
+            "error": None,
+            "from_cache": False,
+        }
+
+    except subprocess.TimeoutExpired:
+        return {
+            "supported": False,
+            "help_output": None,
+            "error": "Timeout while checking sglang.launch_server --help",
+            "from_cache": False,
+        }
+    except FileNotFoundError:
+        return {
+            "supported": False,
+            "help_output": None,
+            "error": "Python interpreter not found",
+            "from_cache": False,
+        }
+    except Exception as e:
+        return {
+            "supported": False,
+            "help_output": None,
+            "error": str(e),
+            "from_cache": False,
+        }
+
+
+def print_sglang_kt_kernel_instructions() -> None:
+    """Print instructions for installing the kvcache-ai fork of SGLang with kt-kernel support."""
+    from kt_kernel.cli.i18n import get_lang
+
+    lang = get_lang()
+
+    if lang == "zh":
+        instructions = """
+[bold red]SGLang 不支持 kt-kernel[/bold red]
+
+您当前安装的 SGLang 不包含 kt-kernel 支持。
+kt-kernel 需要使用 kvcache-ai 维护的 SGLang 分支。
+
+[bold]请按以下步骤重新安装 SGLang:[/bold]
+
+[cyan]1. 卸载当前的 SGLang:[/cyan]
+   pip uninstall sglang -y
+
+[cyan]2. 克隆 kvcache-ai 的 SGLang 仓库:[/cyan]
+   git clone https://github.com/kvcache-ai/sglang.git
+   cd sglang
+
+[cyan]3. 安装 SGLang:[/cyan]
+   pip install -e "python[all]"
+
+[dim]注意: 请确保在正确的 Python 环境中执行以上命令[/dim]
+"""
+    else:
+        instructions = """
+[bold red]SGLang does not support kt-kernel[/bold red]
+
+Your current SGLang installation does not include kt-kernel support.
+kt-kernel requires the kvcache-ai maintained fork of SGLang.
+
+[bold]Please reinstall SGLang with the following steps:[/bold]
+
+[cyan]1. Uninstall current SGLang:[/cyan]
+   pip uninstall sglang -y
+
+[cyan]2. Clone the kvcache-ai SGLang repository:[/cyan]
+   git clone https://github.com/kvcache-ai/sglang.git
+   cd sglang
+
+[cyan]3. Install SGLang:[/cyan]
+   pip install -e "python[all]"
+
+[dim]Note: Make sure to run these commands in the correct Python environment[/dim]
+"""
+    console.print(instructions)

kt-kernel/python/experts.py

@@ -17,7 +17,7 @@ from typing import List, Optional
 from .experts_base import BaseMoEWrapper, KExpertsCPUBuffer
 
 # Import backend implementations
-from .utils.amx import AMXMoEWrapper, RAWAMXMoEWrapper
+from .utils.amx import AMXMoEWrapper, NativeMoEWrapper
 from .utils.llamafile import LlamafileMoEWrapper
 from .utils.moe_kernel import GeneralMoEWrapper
 
@@ -77,7 +77,7 @@ class KTMoEWrapper:
             chunked_prefill_size: Maximum prefill chunk size
             cpu_save: Whether to save weights to CPU memory
             max_deferred_experts_per_token: Number of experts per token to defer. Defaults to 0.
-            method: Backend method ("AMXINT4", "AMXINT8", "RAWINT4", "LLAMAFILE", "MOE_INT4", "MOE_INT8")
+            method: Backend method ("AMXINT4", "AMXINT8", "RAWINT4", "FP8", "LLAMAFILE", "MOE_INT4", "MOE_INT8")
 
         Returns:
             An instance of the appropriate backend implementation (e.g., AMXMoEWrapper)
@@ -85,8 +85,8 @@ class KTMoEWrapper:
         # Select backend based on method
         if method in ["AMXINT4", "AMXINT8"]:
             backend_cls = AMXMoEWrapper
-        elif method == "RAWINT4":
-            backend_cls = RAWAMXMoEWrapper
+        elif method in ["RAWINT4", "FP8"]:
+            backend_cls = NativeMoEWrapper
         elif method == "LLAMAFILE":
             backend_cls = LlamafileMoEWrapper
         elif method in ["MOE_INT4", "MOE_INT8"]:

kt-kernel/python/utils/__init__.py

@@ -4,13 +4,13 @@
 Utilities for kt_kernel package.
 """
 
-from .amx import AMXMoEWrapper, RAWAMXMoEWrapper
+from .amx import AMXMoEWrapper, NativeMoEWrapper
 from .llamafile import LlamafileMoEWrapper
 from .loader import SafeTensorLoader, GGUFLoader, CompressedSafeTensorLoader
 
 __all__ = [
     "AMXMoEWrapper",
-    "RAWAMXMoEWrapper",
+    "NativeMoEWrapper",
     "LlamafileMoEWrapper",
     "SafeTensorLoader",
     "CompressedSafeTensorLoader",

kt-kernel/python/utils/amx.py

@@ -4,16 +4,16 @@ import ctypes
 
 # Use relative imports for package structure
 from ..experts_base import BaseMoEWrapper
-from .loader import SafeTensorLoader, CompressedSafeTensorLoader
+from .loader import SafeTensorLoader, CompressedSafeTensorLoader, FP8SafeTensorLoader
 from kt_kernel_ext.moe import MOEConfig
 
 try:
-    from kt_kernel_ext.moe import AMXInt4_MOE, AMXInt8_MOE, AMXInt4_KGroup_MOE
+    from kt_kernel_ext.moe import AMXInt4_MOE, AMXInt8_MOE, AMXInt4_KGroup_MOE, AMXFP8_MOE
 
     _HAS_AMX_SUPPORT = True
 except (ImportError, AttributeError):
     _HAS_AMX_SUPPORT = False
-    AMXInt4_MOE, AMXInt8_MOE, AMXInt4_KGroup_MOE = None, None, None
+    AMXInt4_MOE, AMXInt8_MOE, AMXInt4_KGroup_MOE, AMXFP8_MOE = None, None, None, None
 
 from typing import Optional
 
@@ -303,10 +303,10 @@ class AMXMoEWrapper(BaseMoEWrapper):
             del self.down_scales
 
 
-class RAWAMXMoEWrapper(BaseMoEWrapper):
-    """Wrapper for RAWINT4 experts stored in compressed SafeTensor format."""
+class NativeMoEWrapper(BaseMoEWrapper):
+    """Wrapper for RAWINT4/FP8 experts stored in compressed SafeTensor format."""
 
-    _compressed_loader_instance = None
+    _native_loader_instance = None
 
     def __init__(
         self,
@@ -324,8 +324,12 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         max_deferred_experts_per_token: Optional[int] = None,
         method: str = "RAWINT4",
     ):
-        if not _HAS_AMX_SUPPORT or AMXInt4_KGroup_MOE is None:
+        if not _HAS_AMX_SUPPORT:
+            raise RuntimeError("AMX backend is not available.")
+        if method == "RAWINT4" and AMXInt4_KGroup_MOE is None:
             raise RuntimeError("AMX backend with RAWINT4 support is not available.")
+        if method == "FP8" and AMXFP8_MOE is None:
+            raise RuntimeError("AMX backend with FP8 support is not available.")
 
         super().__init__(
             layer_idx=layer_idx,
@@ -343,9 +347,14 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
             method=method,
         )
 
-        if RAWAMXMoEWrapper._compressed_loader_instance is None:
-            RAWAMXMoEWrapper._compressed_loader_instance = CompressedSafeTensorLoader(weight_path)
-        self.loader = RAWAMXMoEWrapper._compressed_loader_instance
+        if NativeMoEWrapper._native_loader_instance is None:
+            if method == "RAWINT4":
+                NativeMoEWrapper._native_loader_instance = CompressedSafeTensorLoader(weight_path)
+            elif method == "FP8":
+                NativeMoEWrapper._native_loader_instance = FP8SafeTensorLoader(weight_path)
+            else:
+                raise NotImplementedError(f"Unsupported method for NativeMoEWrapper: {method}")
+        self.loader = NativeMoEWrapper._native_loader_instance
 
         self.gate_weights = None
         self.up_weights = None
@@ -378,9 +387,17 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         self.down_weights = weights["down"]
 
         # Convert scales to bf16 individually
-        self.gate_scales = [t.to(torch.bfloat16).contiguous() for t in weights["gate_scale"]]
-        self.up_scales = [t.to(torch.bfloat16).contiguous() for t in weights["up_scale"]]
-        self.down_scales = [t.to(torch.bfloat16).contiguous() for t in weights["down_scale"]]
+        # self.gate_scales = [t.to(torch.bfloat16).contiguous() for t in weights["gate_scale"]]
+        # self.up_scales = [t.to(torch.bfloat16).contiguous() for t in weights["up_scale"]]
+        # self.down_scales = [t.to(torch.bfloat16).contiguous() for t in weights["down_scale"]]
+        self.gate_scales = weights["gate_scale"]
+        self.up_scales = weights["up_scale"]
+        self.down_scales = weights["down_scale"]
+        if self.method == "RAWINT4":
+            assert self.gate_scales[0].dtype == torch.bfloat16, "Expected bf16 scales for RAWINT4"
+        elif self.method == "FP8":
+            assert self.gate_scales[0].dtype == torch.float32, "Expected float32 scales for FP8"
+
         t2 = time.time()
 
         # Build pointer lists: [numa_id][expert_id] -> pointer
@@ -404,18 +421,6 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         moe_config.pool = self.cpu_infer.backend_
         moe_config.max_len = self.chunked_prefill_size
 
-        # Infer group_size from scale shape (column-major layout)
-        # For gate/up projection: in_features = hidden_size
-        # So: group_size = hidden_size / scale.shape[1]
-        scale_shape = self.gate_scales[0].shape
-        group_size = self.hidden_size // scale_shape[1]
-        print(f"[RAWAMXMoEWrapper Layer {self.layer_idx}] Inferred group_size: {group_size}")
-
-        moe_config.quant_config.bits = 4
-        moe_config.quant_config.group_size = group_size
-        
-        moe_config.quant_config.zero_point = False
-
         # Use gate_projs instead of gate_proj for per-expert pointers
         moe_config.gate_projs = gate_ptrs
         moe_config.up_projs = up_ptrs
@@ -424,7 +429,21 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         moe_config.up_scales = up_scale_ptrs
         moe_config.down_scales = down_scale_ptrs
 
-        self.moe = AMXInt4_KGroup_MOE(moe_config)
+        # Infer group_size from scale shape (column-major layout)
+        # For gate/up projection: in_features = hidden_size
+        # So: group_size = hidden_size / scale.shape[1]
+
+        if self.method == "RAWINT4":
+            group_size = self.hidden_size // self.gate_scales[0].shape[1]
+            moe_config.quant_config.bits = 4
+            moe_config.quant_config.group_size = group_size
+            moe_config.quant_config.zero_point = False
+            self.moe = AMXInt4_KGroup_MOE(moe_config)
+        elif self.method == "FP8":
+            moe_config.quant_config.bits = 8
+            moe_config.quant_config.group_size = 128
+            moe_config.quant_config.zero_point = False
+            self.moe = AMXFP8_MOE(moe_config)
         t4 = time.time()
 
         self.cpu_infer.submit(self.moe.load_weights_task(physical_to_logical_map_cpu.data_ptr()))
@@ -440,7 +459,7 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         t6 = time.time()
 
         print(
-            f"[RAWAMXMoEWrapper Layer {self.layer_idx}] "
+            f"[NativeMoEWrapper Layer {self.layer_idx}] "
             f"load_experts: {(t1-t0)*1000:.1f}ms, "
             f"prepare_tensors: {(t2-t1)*1000:.1f}ms, "
             f"build_ptrs: {(t3-t2)*1000:.1f}ms, "
@@ -453,7 +472,7 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
     def submit_write_weight_scale_to_buffer(
         self,
         gpu_tp_count: int,
-        gpu_experts_num: int,
+        expert_id: int,
         w13_weight_ptrs,
         w13_scale_ptrs,
         w2_weight_ptrs,
@@ -477,7 +496,7 @@ class RAWAMXMoEWrapper(BaseMoEWrapper):
         self.cpu_infer.submit(
             self.moe.write_weight_scale_to_buffer_task(
                 gpu_tp_count,
-                gpu_experts_num,
+                expert_id,
                 w13_weight_ptrs,
                 w13_scale_ptrs,
                 w2_weight_ptrs,

kt-kernel/python/utils/llamafile.py

@@ -219,4 +219,4 @@ class LlamafileMoEWrapper(BaseMoEWrapper):
         self.cpu_infer.sync()
 
         # Drop original weights after loading
-        self.weights_to_keep = None
+        self.weights_to_keep = None

kt-kernel/python/utils/loader.py

@@ -237,6 +237,117 @@ class SafeTensorLoader:
         return name in self.tensor_file_map
 
 
+class FP8SafeTensorLoader(SafeTensorLoader):
+    """Loader for FP8 expert weights with auto-detection of naming formats.
+
+    Supported formats:
+    - DeepSeek style: {base}.mlp.experts.{id}.{gate,up,down}_proj.weight
+    - Mixtral/MiniMax style: {base}.block_sparse_moe.experts.{id}.{w1,w3,w2}.weight
+
+    The format is auto-detected during initialization.
+    """
+
+    # Known MoE naming formats: (experts_path_template, gate_name, up_name, down_name)
+    MOE_FORMATS = {
+        "deepseek": ("{base}.mlp.experts", "gate_proj", "up_proj", "down_proj"),
+        "mixtral": ("{base}.block_sparse_moe.experts", "w1", "w3", "w2"),
+    }
+
+    def __init__(self, file_path: str):
+        super().__init__(file_path)
+        self._detected_format = None
+        self._detect_format()
+
+    def _detect_format(self):
+        """Auto-detect the MoE naming format by checking tensor keys."""
+        # Sample some tensor names to detect format
+        sample_keys = list(self.tensor_file_map.keys())[:1000]
+
+        for fmt_name, (path_tpl, gate, up, down) in self.MOE_FORMATS.items():
+            # Check if any key matches this format pattern
+            # Look for pattern like: model.layers.0.{experts_path}.0.{gate_name}.weight
+            for key in sample_keys:
+                if ".experts." in key and f".{gate}.weight" in key:
+                    # Verify the path template matches
+                    if "block_sparse_moe.experts" in key and fmt_name == "mixtral":
+                        self._detected_format = fmt_name
+                        print(f"[FP8SafeTensorLoader] Detected format: {fmt_name}")
+                        return
+                    elif "mlp.experts" in key and "block_sparse_moe" not in key and fmt_name == "deepseek":
+                        self._detected_format = fmt_name
+                        print(f"[FP8SafeTensorLoader] Detected format: {fmt_name}")
+                        return
+
+        # Default to deepseek if no format detected
+        self._detected_format = "deepseek"
+        print("[FP8SafeTensorLoader] No MoE format detected, defaulting to: deepseek")
+
+    def _get_experts_prefix(self, base_key: str) -> str:
+        """Get the experts prefix based on detected format."""
+        path_tpl, _, _, _ = self.MOE_FORMATS[self._detected_format]
+        return path_tpl.format(base=base_key)
+
+    def _get_proj_names(self):
+        """Get projection names (gate, up, down) based on detected format."""
+        _, gate, up, down = self.MOE_FORMATS[self._detected_format]
+        return gate, up, down
+
+    def load_tensor(self, key: str, device: str = "cpu"):
+        if key not in self.tensor_file_map:
+            raise KeyError(f"Key {key} not found in Safetensor files")
+        file = self.tensor_file_map[key]
+        f = self.file_handle_map.get(file)
+        if f is None:
+            raise FileNotFoundError(f"File {file} not found in Safetensor files")
+        tensor = f.get_tensor(key)
+        if device == "cpu":
+            return tensor
+        return tensor.to(device)
+
+    def load_experts(self, base_key: str, device: str = "cpu"):
+        """Load FP8 expert weights and their block-wise scale_inv tensors."""
+        experts_prefix = self._get_experts_prefix(base_key)
+        gate_name, up_name, down_name = self._get_proj_names()
+
+        expert_count = 0
+        while self.has_tensor(f"{experts_prefix}.{expert_count}.{gate_name}.weight"):
+            expert_count += 1
+
+        if expert_count == 0:
+            raise ValueError(f"No experts found for key {experts_prefix}")
+
+        gate_weights = [None] * expert_count
+        up_weights = [None] * expert_count
+        down_weights = [None] * expert_count
+        gate_scales = [None] * expert_count
+        up_scales = [None] * expert_count
+        down_scales = [None] * expert_count
+
+        for exp_id in range(expert_count):
+            gate_w_key = f"{experts_prefix}.{exp_id}.{gate_name}.weight"
+            up_w_key = f"{experts_prefix}.{exp_id}.{up_name}.weight"
+            down_w_key = f"{experts_prefix}.{exp_id}.{down_name}.weight"
+            gate_s_key = f"{experts_prefix}.{exp_id}.{gate_name}.weight_scale_inv"
+            up_s_key = f"{experts_prefix}.{exp_id}.{up_name}.weight_scale_inv"
+            down_s_key = f"{experts_prefix}.{exp_id}.{down_name}.weight_scale_inv"
+
+            gate_weights[exp_id] = self.load_tensor(gate_w_key, device).contiguous()
+            up_weights[exp_id] = self.load_tensor(up_w_key, device).contiguous()
+            down_weights[exp_id] = self.load_tensor(down_w_key, device).contiguous()
+            gate_scales[exp_id] = self.load_tensor(gate_s_key, device).contiguous()
+            up_scales[exp_id] = self.load_tensor(up_s_key, device).contiguous()
+            down_scales[exp_id] = self.load_tensor(down_s_key, device).contiguous()
+
+        return {
+            "gate": gate_weights,
+            "up": up_weights,
+            "down": down_weights,
+            "gate_scale": gate_scales,
+            "up_scale": up_scales,
+            "down_scale": down_scales,
+        }
+
+
 class CompressedSafeTensorLoader(SafeTensorLoader):
     """Loader for compressed SafeTensor layouts (RAWINT4 weights)."""