kt-cli enhancement (#1834)

* [feat]: redesign kt run interactive configuration with i18n support

- Redesign kt run with 8-step interactive flow (model selection, inference method, NUMA/CPU, GPU experts, KV cache, GPU/TP selection, parsers, host/port)
- Add configuration save/load system (~/.ktransformers/run_configs.yaml)
- Add i18n support for kt chat (en/zh translations)
- Add universal input validators with auto-retry and Chinese comma support
- Add port availability checker with auto-suggestion
- Add parser configuration (--tool-call-parser, --reasoning-parser)
- Remove tuna command and clean up redundant files
- Fix: variable reference bug in run.py, filter to show only MoE models

* [feat]: unify model selection UI and enable shared experts fusion by default

- Unify kt run model selection table with kt model list display
  * Add Total size, MoE Size, Repo, and SHA256 status columns
  * Use consistent formatting and styling
  * Improve user decision-making with more information

- Enable --disable-shared-experts-fusion by default
  * Change default value from False to True
  * Users can still override with --enable-shared-experts-fusion

* [feat]: improve kt chat with performance metrics and better CJK support

- Add performance metrics display after each response
  * Total time, TTFT (Time To First Token), TPOT (Time Per Output Token)
  * Accurate input/output token counts using model tokenizer
  * Fallback to estimation if tokenizer unavailable
  * Metrics shown in dim style (not prominent)

- Fix Chinese character input issues
  * Replace Prompt.ask() with console.input() for better CJK support
  * Fixes backspace deletion showing half-characters

- Suppress NumPy subnormal warnings
  * Filter "The value of the smallest subnormal" warnings
  * Cleaner CLI output on certain hardware environments

* [fix]: correct TTFT measurement in kt chat

- Move start_time initialization before API call
- Previously start_time was set when receiving first chunk, causing TTFT ≈ 0ms
- Now correctly measures time from request sent to first token received

* [docs]: 添加 Clawdbot 集成指南 - KTransformers 企业级 AI 助手部署方案

* [docs]: 强调推荐使用 Kimi K2.5 作为核心模型，突出企业级推理能力

* [docs]: 添加 Clawdbot 飞书接入教程链接

* [feat]: improve CLI table display, model verification, and chat experience

- Add sequence number (#) column to all model tables by default
- Filter kt edit to show only MoE GPU models (exclude AMX)
- Extend kt model verify to check *.json and *.py files in addition to weights
- Fix re-verification bug where repaired files caused false failures
- Suppress tokenizer debug output in kt chat token counting

* [fix]: fix cpu cores.

---------

Co-authored-by: skqliao <skqliao@gmail.com>

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

@@ -0,0 +1,146 @@
+"""Helper functions for interactive model download."""
+
+from pathlib import Path
+from typing import Dict, List, Tuple
+import fnmatch
+
+
+def list_remote_files_hf(repo_id: str, use_mirror: bool = False) -> List[Dict[str, any]]:
+    """
+    List files in a HuggingFace repository.
+
+    Returns:
+        List of dicts with keys: 'path', 'size' (in bytes)
+    """
+    from huggingface_hub import HfApi
+    import os
+
+    # Set mirror if needed
+    original_endpoint = os.environ.get("HF_ENDPOINT")
+    if use_mirror and not original_endpoint:
+        os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+
+    try:
+        api = HfApi()
+        files_info = api.list_repo_tree(repo_id=repo_id, recursive=True)
+
+        result = []
+        for item in files_info:
+            # Skip directories
+            if hasattr(item, "type") and item.type == "directory":
+                continue
+
+            # Get file info
+            file_path = item.path if hasattr(item, "path") else str(item)
+            file_size = item.size if hasattr(item, "size") else 0
+
+            result.append({"path": file_path, "size": file_size})
+
+        return result
+    finally:
+        # Restore original endpoint
+        if use_mirror and not original_endpoint:
+            os.environ.pop("HF_ENDPOINT", None)
+        elif original_endpoint:
+            os.environ["HF_ENDPOINT"] = original_endpoint
+
+
+def list_remote_files_ms(repo_id: str) -> List[Dict[str, any]]:
+    """
+    List files in a ModelScope repository.
+
+    Returns:
+        List of dicts with keys: 'path', 'size' (in bytes)
+    """
+    from modelscope.hub.api import HubApi
+
+    api = HubApi()
+    files_info = api.get_model_files(model_id=repo_id, recursive=True)
+
+    result = []
+    for file_info in files_info:
+        file_path = file_info.get("Name", file_info.get("Path", ""))
+        file_size = file_info.get("Size", 0)
+
+        result.append({"path": file_path, "size": file_size})
+
+    return result
+
+
+def filter_files_by_pattern(files: List[Dict[str, any]], pattern: str) -> List[Dict[str, any]]:
+    """Filter files by glob pattern."""
+    if pattern == "*":
+        return files
+
+    filtered = []
+    for file in files:
+        # Check if filename matches pattern
+        filename = Path(file["path"]).name
+        full_path = file["path"]
+
+        if fnmatch.fnmatch(filename, pattern) or fnmatch.fnmatch(full_path, pattern):
+            filtered.append(file)
+
+    return filtered
+
+
+def calculate_total_size(files: List[Dict[str, any]]) -> int:
+    """Calculate total size of files in bytes."""
+    return sum(f["size"] for f in files)
+
+
+def format_file_list_table(files: List[Dict[str, any]], max_display: int = 10):
+    """Format file list as a table for display."""
+    from rich.table import Table
+    from kt_kernel.cli.utils.model_scanner import format_size
+
+    table = Table(show_header=True, header_style="bold")
+    table.add_column("File", style="cyan", overflow="fold")
+    table.add_column("Size", justify="right")
+
+    # Show first max_display files
+    for file in files[:max_display]:
+        table.add_row(file["path"], format_size(file["size"]))
+
+    if len(files) > max_display:
+        table.add_row(f"... and {len(files) - max_display} more files", "[dim]...[/dim]")
+
+    return table
+
+
+def verify_repo_exists(repo_id: str, repo_type: str, use_mirror: bool = False) -> Tuple[bool, str]:
+    """
+    Verify if a repository exists.
+
+    Returns:
+        (exists: bool, message: str)
+    """
+    try:
+        if repo_type == "huggingface":
+            import os
+
+            original_endpoint = os.environ.get("HF_ENDPOINT")
+            if use_mirror and not original_endpoint:
+                os.environ["HF_ENDPOINT"] = "https://hf-mirror.com"
+
+            from huggingface_hub import HfApi
+
+            try:
+                api = HfApi()
+                api.repo_info(repo_id=repo_id, repo_type="model")
+                return True, "Repository found"
+            finally:
+                if use_mirror and not original_endpoint:
+                    os.environ.pop("HF_ENDPOINT", None)
+                elif original_endpoint:
+                    os.environ["HF_ENDPOINT"] = original_endpoint
+
+        else:  # modelscope
+            from modelscope.hub.api import HubApi
+
+            api = HubApi()
+            api.get_model(model_id=repo_id)
+            return True, "Repository found"
+
+    except Exception as e:
+        return False, f"Repository not found: {str(e)}"