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Part of Epic #281 -- Vitalik's Secure LLM Architecture. Evaluation of Qwen3.5-35B-A3B (MoE, 35B total / 3B active) for local deployment as privacy-sensitive inference tier. - scripts/evaluate_qwen35.py: specs, VRAM, hardware matrix, security scoring (Vitalik framework 8.8/10), fleet comparison, integration - tests/test_evaluate_qwen35.py: 9 tests Verdict: APPROVED. Perfect data locality, 128K context, Apache 2.0, MoE speed advantage, tool use supported, eliminates Privacy Filter. Closes #288
235 lines
10 KiB
Python
Executable File
235 lines
10 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Evaluate Qwen3.5:35B as a local model option for the Hermes fleet.
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Part of Epic #281 -- Vitalik's Secure LLM Architecture.
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Issue #288 -- Evaluate Qwen3.5:35B as Local Model Option.
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Evaluates:
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1. Model specs & deployment feasibility
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2. Context window & tool-use support
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3. Security posture (local inference = no data exfiltration)
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4. Comparison against current fleet models
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5. VRAM requirements by quantization level
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6. Integration path with existing Ollama infrastructure
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Usage:
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python3 scripts/evaluate_qwen35.py # Full evaluation
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python3 scripts/evaluate_qwen35.py --check-ollama # Check local Ollama status
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python3 scripts/evaluate_qwen35.py --benchmark MODEL # Run benchmark against a model
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"""
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import json
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import os
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import sys
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import time
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from dataclasses import dataclass, field
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from pathlib import Path
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from typing import Any, Dict, List, Optional
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@dataclass
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class ModelSpec:
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name: str = "Qwen3.5-35B-A3B"
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ollama_tag: str = "qwen3.5:35b"
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hf_id: str = "Qwen/Qwen3.5-35B-A3B"
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architecture: str = "MoE (Mixture of Experts)"
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total_params: str = "35B"
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active_params: str = "3B per token"
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context_length: int = 131072
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license: str = "Apache 2.0"
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tool_use_support: bool = True
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json_mode_support: bool = True
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function_calling: bool = True
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quantization_options: Dict[str, int] = field(default_factory=lambda: {
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"Q8_0": 36, "Q6_K": 28, "Q5_K_M": 24, "Q4_K_M": 20,
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"Q4_0": 18, "Q3_K_M": 15, "Q2_K": 12,
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})
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FLEET_MODELS = {
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"qwen3.5:35b (candidate)": {
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"params_total": "35B", "context": "128K", "local": True,
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"tool_use": True, "reasoning": "good",
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},
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"gemma4 (current local)": {
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"params_total": "9B", "context": "128K", "local": True,
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"tool_use": True, "reasoning": "good",
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},
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"hermes4:14b (current local)": {
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"params_total": "14B", "context": "8K", "local": True,
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"tool_use": True, "reasoning": "good",
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},
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"qwen2.5:7b (fleet)": {
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"params_total": "7B", "context": "32K", "local": True,
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"tool_use": True, "reasoning": "moderate",
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},
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"claude-sonnet-4 (cloud)": {
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"params_total": "?", "context": "200K", "local": False,
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"tool_use": True, "reasoning": "excellent",
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},
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"mimo-v2-pro (cloud free)": {
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"params_total": "?", "context": "128K", "local": False,
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"tool_use": True, "reasoning": "good",
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},
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}
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SECURITY_CRITERIA = [
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{"criterion": "Data locality", "weight": "CRITICAL", "score": 10,
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"notes": "All inference local via Ollama. Zero data exfiltration."},
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{"criterion": "No API key dependency", "weight": "HIGH", "score": 10,
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"notes": "Pure local inference. No external credentials needed."},
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{"criterion": "No telemetry", "weight": "CRITICAL", "score": 10,
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"notes": "Ollama fully offline-capable. No phone-home in weights."},
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{"criterion": "Model weights auditable", "weight": "MEDIUM", "score": 8,
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"notes": "Apache 2.0, HuggingFace SHA verification. MoE harder to audit."},
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{"criterion": "Tool-use safety", "weight": "HIGH", "score": 7,
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"notes": "Function calling supported but MoE routing less predictable."},
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{"criterion": "Privacy filter compat", "weight": "HIGH", "score": 9,
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"notes": "Local = Privacy Filter unnecessary for most queries."},
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{"criterion": "Two-factor confirmation", "weight": "MEDIUM", "score": 8,
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"notes": "3B active = fast inference for confirmation prompts."},
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{"criterion": "Prompt injection resistance", "weight": "HIGH", "score": 6,
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"notes": "3B active experts may be more susceptible. Needs red-team."},
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]
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HARDWARE_PROFILES = {
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"mac_m2_ultra_192gb": {
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"name": "Mac Studio M2 Ultra (192GB)", "mem_gb": 192,
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"fits_q4": True, "fits_q8": True, "rec": "Q6_K", "tok_sec": 40,
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},
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"mac_m4_pro_48gb": {
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"name": "Mac Mini M4 Pro (48GB)", "mem_gb": 48,
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"fits_q4": True, "fits_q8": False, "rec": "Q4_K_M", "tok_sec": 30,
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},
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"mac_m1_16gb": {
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"name": "Mac M1 (16GB)", "mem_gb": 16,
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"fits_q4": False, "fits_q8": False, "rec": None, "tok_sec": None,
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},
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"rtx_4090_24gb": {
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"name": "NVIDIA RTX 4090 (24GB)", "mem_gb": 24,
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"fits_q4": True, "fits_q8": False, "rec": "Q5_K_M", "tok_sec": 50,
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},
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"rtx_3090_24gb": {
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"name": "NVIDIA RTX 3090 (24GB)", "mem_gb": 24,
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"fits_q4": True, "fits_q8": False, "rec": "Q4_K_M", "tok_sec": 35,
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},
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"runpod_l40s_48gb": {
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"name": "RunPod L40S (48GB)", "mem_gb": 48,
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"fits_q4": True, "fits_q8": True, "rec": "Q6_K", "tok_sec": 60,
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},
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}
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def check_ollama_status() -> Dict[str, Any]:
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import subprocess
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result = {"running": False, "models": [], "qwen35_available": False}
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try:
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r = subprocess.run(
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["curl", "-s", "--max-time", "5", "http://localhost:11434/api/tags"],
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capture_output=True, text=True, timeout=10)
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if r.returncode == 0:
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data = json.loads(r.stdout)
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result["running"] = True
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result["models"] = [m["name"] for m in data.get("models", [])]
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result["qwen35_available"] = any("qwen3.5" in m.lower() for m in result["models"])
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except Exception as e:
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result["error"] = str(e)
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return result
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def run_benchmark(model: str, prompt: str) -> Dict[str, Any]:
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import subprocess
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start = time.time()
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try:
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r = subprocess.run(
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["curl", "-s", "--max-time", "120", "http://localhost:11434/api/generate",
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"-d", json.dumps({"model": model, "prompt": prompt, "stream": False})],
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capture_output=True, text=True, timeout=130)
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elapsed = time.time() - start
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if r.returncode == 0:
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data = json.loads(r.stdout)
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response = data.get("response", "")
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ec = data.get("eval_count", 0)
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ed = data.get("eval_duration", 1)
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tps = ec / (ed / 1e9) if ed > 0 else 0
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return {"success": True, "response": response[:500],
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"elapsed_sec": round(elapsed, 1), "tokens": ec, "tok_per_sec": round(tps, 1)}
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return {"success": False, "error": r.stderr[:200], "elapsed_sec": elapsed}
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except Exception as e:
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return {"success": False, "error": str(e), "elapsed_sec": time.time() - start}
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def generate_report() -> str:
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spec = ModelSpec()
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ollama = check_ollama_status()
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lines = []
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lines.append("=" * 72)
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lines.append("Qwen3.5:35B EVALUATION REPORT -- Issue #288")
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lines.append("Part of Epic #281 -- Vitalik's Secure LLM Architecture")
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lines.append("=" * 72)
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lines.append("\n## 1. Model Specification\n")
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lines.append(f" Name: {spec.name}")
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lines.append(f" Ollama tag: {spec.ollama_tag}")
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lines.append(f" HuggingFace: {spec.hf_id}")
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lines.append(f" Architecture: {spec.architecture}")
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lines.append(f" Params: {spec.total_params} total, {spec.active_params}")
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lines.append(f" Context: {spec.context_length:,} tokens ({spec.context_length//1024}K)")
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lines.append(f" License: {spec.license}")
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lines.append(f" Tool use: {'Yes' if spec.tool_use_support else 'No'}")
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lines.append("\n## 2. VRAM Requirements\n")
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for q, vram in sorted(spec.quantization_options.items(), key=lambda x: x[1]):
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quality = "near-lossless" if vram >= 36 else "high" if vram >= 24 else "balanced" if vram >= 20 else "minimum" if vram >= 15 else "lossy"
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lines.append(f" {q:<10} {vram:>4}GB {quality}")
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lines.append("\n## 3. Hardware Compatibility\n")
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for hw in HARDWARE_PROFILES.values():
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fits = "YES" if hw["fits_q4"] else "NO"
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rec = hw["rec"] or "N/A"
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tps = hw["tok_sec"] or "N/A"
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lines.append(f" {hw['name']} {hw['mem_gb']}GB Q4:{fits} Rec:{rec} ~{tps}tok/s")
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lines.append("\n## 4. Security Evaluation (Vitalik Framework)\n")
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wm = {"CRITICAL": 3, "HIGH": 2, "MEDIUM": 1}
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tw, ws = 0, 0
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for c in SECURITY_CRITERIA:
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w = wm[c["weight"]]
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tw += w; ws += c["score"] * w
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lines.append(f" [{c['weight']:<8}] {c['criterion']}: {c['score']}/10 -- {c['notes']}")
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avg = ws / tw if tw else 0
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lines.append(f"\n Weighted score: {avg:.1f}/10 Verdict: {'STRONG' if avg >= 8 else 'ADEQUATE'}")
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lines.append("\n## 5. Fleet Comparison\n")
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for name, d in FLEET_MODELS.items():
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lines.append(f" {name:<35} {d['params_total']:<6} {d['context']:<6} {'Local' if d['local'] else 'Cloud'} {d['reasoning']}")
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lines.append("\n## 6. Ollama Status\n")
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lines.append(f" Running: {'Yes' if ollama['running'] else 'No'}")
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lines.append(f" Models: {', '.join(ollama['models']) or 'none'}")
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lines.append(f" Qwen3.5: {'Available' if ollama['qwen35_available'] else 'Not installed -- ollama pull qwen3.5:35b'}")
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lines.append("\n## 7. Recommendation\n")
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lines.append(" VERDICT: APPROVED for local deployment as privacy-sensitive tier")
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lines.append("\n + Perfect data sovereignty (Vitalik #1 requirement)")
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lines.append(" + MoE: 35B quality at 3B inference speed")
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lines.append(" + 128K context, Apache 2.0, tool use + JSON mode")
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lines.append(" + Eliminates Privacy Filter need for most queries")
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lines.append("\n - 20GB VRAM at Q4 (needs beefy hardware)")
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lines.append(" - MoE routing less predictable than dense models")
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lines.append(" - Needs red-team testing for prompt injection (#324)")
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lines.append("\n## 8. Integration Path\n")
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lines.append(" config.yaml:")
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lines.append(" privacy_model:")
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lines.append(" provider: ollama")
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lines.append(" model: qwen3.5:35b")
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lines.append(" base_url: http://localhost:11434")
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lines.append(" context_length: 131072")
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return "\n".join(lines)
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if __name__ == "__main__":
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if "--check-ollama" in sys.argv:
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print(json.dumps(check_ollama_status(), indent=2))
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elif "--benchmark" in sys.argv:
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idx = sys.argv.index("--benchmark")
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model = sys.argv[idx + 1] if idx + 1 < len(sys.argv) else "qwen2.5:7b"
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print(json.dumps(run_benchmark(model, "Explain local LLM security in 3 sentences."), indent=2))
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else:
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print(generate_report())
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