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