feat(us-016): mining-style node startup CLI + live dashboard

- `meshnet-node` with no args runs interactive setup wizard on first run,
  then starts directly on subsequent runs using saved config
- Wizard auto-detects all GPUs/VRAM, shows curated model list with per-quant
  VRAM requirements, marks models that exceed available VRAM as incompatible,
  offers HuggingFace Hub browse as escape hatch
- Persistent config saved to ~/.config/meshnet/config.json (0o600)
- Live rich dashboard (tokens/sec EMA, VRAM, requests, peers, uptime) with
  automatic plain-text fallback when stdout is not a TTY (WSL2/SSH/CI)
- All wizard values overridable via CLI flags; --reset-config re-runs wizard
- `meshnet-node models` lists curated models; `--browse` fetches HF Hub top-20
- `meshnet-node config` prints saved config
- `meshnet-node start ...` preserved for backward compatibility
- 19 new tests; 97 passed, 1 skipped (no regressions)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

packages/node/meshnet_node/model_catalog.py

@@ -0,0 +1,133 @@
+"""Curated list of models supported by the network with VRAM requirements."""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+
+@dataclass
+class ModelPreset:
+    name: str
+    hf_repo: str
+    num_layers: int
+    # VRAM in GB at each quantization level (None = too large to quantize this way)
+    vram_nf4: float
+    vram_int8: float
+    vram_bf16: float
+    description: str
+
+    def vram_for_quant(self, quant: str) -> float:
+        """Return VRAM requirement in GB for the given quantization."""
+        q = quant.lower().replace("bfloat16", "bf16")
+        if q == "nf4":
+            return self.vram_nf4
+        if q in ("int8", "int8"):
+            return self.vram_int8
+        if q in ("bf16", "bfloat16"):
+            return self.vram_bf16
+        raise ValueError(f"unknown quantization: {quant!r}")
+
+    def fits_vram(self, available_gb: float, quant: str) -> bool:
+        return self.vram_for_quant(quant) <= available_gb
+
+    def recommended_quant(self, available_gb: float) -> str | None:
+        """Return the highest-quality quantization that fits available VRAM, or None."""
+        if self.vram_bf16 <= available_gb:
+            return "bf16"
+        if self.vram_int8 <= available_gb:
+            return "int8"
+        if self.vram_nf4 <= available_gb:
+            return "nf4"
+        return None
+
+
+CURATED_MODELS: list[ModelPreset] = [
+    ModelPreset(
+        name="Llama-3-70B-Instruct",
+        hf_repo="meta-llama/Meta-Llama-3-70B-Instruct",
+        num_layers=80,
+        vram_nf4=18.0,
+        vram_int8=40.0,
+        vram_bf16=140.0,
+        description="Meta's flagship 70B instruction model",
+    ),
+    ModelPreset(
+        name="Qwen2.5-72B-Instruct",
+        hf_repo="Qwen/Qwen2.5-72B-Instruct",
+        num_layers=80,
+        vram_nf4=19.0,
+        vram_int8=41.0,
+        vram_bf16=145.0,
+        description="Alibaba's 72B multilingual instruction model",
+    ),
+    ModelPreset(
+        name="Mixtral-8x7B-Instruct",
+        hf_repo="mistralai/Mixtral-8x7B-Instruct-v0.1",
+        num_layers=32,
+        vram_nf4=7.0,
+        vram_int8=14.0,
+        vram_bf16=27.0,
+        description="Mistral's sparse MoE — fast and efficient",
+    ),
+    ModelPreset(
+        name="Llama-3-8B-Instruct",
+        hf_repo="meta-llama/Meta-Llama-3-8B-Instruct",
+        num_layers=32,
+        vram_nf4=4.5,
+        vram_int8=8.5,
+        vram_bf16=16.0,
+        description="Meta's compact 8B model — good for low-VRAM nodes",
+    ),
+    ModelPreset(
+        name="Phi-3-medium-128k",
+        hf_repo="microsoft/Phi-3-medium-128k-instruct",
+        num_layers=40,
+        vram_nf4=4.0,
+        vram_int8=8.0,
+        vram_bf16=15.0,
+        description="Microsoft's efficient 14B model with 128k context",
+    ),
+    ModelPreset(
+        name="Gemma-2-27B-IT",
+        hf_repo="google/gemma-2-27b-it",
+        num_layers=46,
+        vram_nf4=10.0,
+        vram_int8=20.0,
+        vram_bf16=54.0,
+        description="Google's 27B instruction-tuned model",
+    ),
+    ModelPreset(
+        name="DeepSeek-V2-Lite-Chat",
+        hf_repo="deepseek-ai/DeepSeek-V2-Lite-Chat",
+        num_layers=27,
+        vram_nf4=5.0,
+        vram_int8=9.0,
+        vram_bf16=16.0,
+        description="DeepSeek's efficient MoE — strong coding + reasoning",
+    ),
+]
+
+
+def browse_hf_hub(top_n: int = 20) -> list[dict]:
+    """Fetch top downloaded text-generation models from HuggingFace Hub."""
+    try:
+        from huggingface_hub import list_models  # type: ignore[import]
+
+        models = list(
+            list_models(
+                pipeline_tag="text-generation",
+                library="transformers",
+                sort="downloads",
+                direction=-1,
+                limit=top_n,
+            )
+        )
+        return [
+            {
+                "repo": m.id,
+                "downloads": getattr(m, "downloads", 0) or 0,
+            }
+            for m in models
+        ]
+    except Exception as exc:
+        raise RuntimeError(f"HuggingFace Hub lookup failed: {exc}") from exc