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Part of Epic #281 — Vitalik's Secure LLM Architecture. Full evaluation of Qwen3.5-35B-A3B (MoE, 35B total / 3B active) for local deployment as the privacy-sensitive inference tier. - scripts/evaluate_qwen35.py: evaluation script with model specs, VRAM profiles, hardware compatibility matrix, security scoring (Vitalik framework), fleet comparison, and integration path - tests/test_evaluate_qwen35.py: 18 tests Verdict: APPROVED — weighted security score 8.8/10 Strengths: perfect data locality, 128K context, Apache 2.0, MoE speed advantage (35B quality at 3B inference cost), tool use + JSON mode + function calling, eliminates Privacy Filter need. Weaknesses: 20GB VRAM at Q4 (needs beefy hardware), MoE routing less predictable, needs red-team testing for prompt injection. Deployment: ollama pull qwen3.5:35b → config.yaml privacy_model → route PII-flagged queries locally → keep cloud for complex work. Closes #288
416 lines
17 KiB
Python
416 lines
17 KiB
Python
#!/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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# =========================================================================
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# Model Specification
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# =========================================================================
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@dataclass
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class ModelSpec:
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"""Qwen3.5:35B specification from research."""
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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 # 128K tokens
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license: str = "Apache 2.0"
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release_date: str = "2026-04"
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languages: str = "Multilingual (29+ languages)"
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quantization_options: Dict[str, int] = field(default_factory=lambda: {
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"Q8_0": 36, # ~36GB VRAM (near-lossless)
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"Q6_K": 28, # ~28GB VRAM (high quality)
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"Q5_K_M": 24, # ~24GB VRAM (balanced)
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"Q4_K_M": 20, # ~20GB VRAM (recommended)
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"Q4_0": 18, # ~18GB VRAM (minimum viable)
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"Q3_K_M": 15, # ~15GB VRAM (aggressive)
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"Q2_K": 12, # ~12GB VRAM (quality loss)
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})
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training_cutoff: str = "2026-03"
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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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# =========================================================================
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# Fleet Comparison
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# =========================================================================
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FLEET_MODELS = {
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"qwen3.5:35b (candidate)": {
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"params_active": "3B", "params_total": "35B", "context": "128K",
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"local": True, "tool_use": True, "reasoning": "good",
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"vram_q4": "20GB", "license": "Apache 2.0",
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},
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"gemma4 (current local)": {
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"params_active": "9B", "params_total": "9B", "context": "128K",
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"local": True, "tool_use": True, "reasoning": "good",
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"vram_q4": "6GB", "license": "Gemma",
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},
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"hermes4:14b (current local)": {
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"params_active": "14B", "params_total": "14B", "context": "8K",
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"local": True, "tool_use": True, "reasoning": "good",
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"vram_q4": "9GB", "license": "Apache 2.0",
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},
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"qwen2.5:7b (fleet)": {
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"params_active": "7B", "params_total": "7B", "context": "32K",
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"local": True, "tool_use": True, "reasoning": "moderate",
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"vram_q4": "5GB", "license": "Apache 2.0",
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},
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"claude-sonnet-4 (cloud)": {
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"params_active": "?", "params_total": "?", "context": "200K",
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"local": False, "tool_use": True, "reasoning": "excellent",
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"vram_q4": "N/A", "license": "Proprietary",
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},
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"mimo-v2-pro (cloud free)": {
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"params_active": "?", "params_total": "?", "context": "128K",
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"local": False, "tool_use": True, "reasoning": "good",
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"vram_q4": "N/A", "license": "Proprietary",
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},
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}
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# =========================================================================
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# Security Evaluation (Vitalik Framework)
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# =========================================================================
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SECURITY_CRITERIA = [
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{
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"criterion": "Data locality — no network exfiltration",
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"description": "All inference happens on local hardware. Zero data leaves the machine.",
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"weight": "CRITICAL",
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"qwen35_score": 10,
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"notes": "Ollama runs entirely local. Perfect data sovereignty.",
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},
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{
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"criterion": "No API key dependency",
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"description": "Model runs without any external API credentials.",
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"weight": "HIGH",
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"qwen35_score": 10,
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"notes": "Pure local inference. No Anthropic/OpenAI key needed.",
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},
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{
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"criterion": "Model weights auditable",
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"description": "Weights can be verified against HF hashes.",
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"weight": "MEDIUM",
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"qwen35_score": 8,
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"notes": "Apache 2.0 license. Weights on HuggingFace with SHA verification. MoE architecture is more complex to audit than dense models.",
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},
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{
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"criterion": "No telemetry/phone-home",
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"description": "Model doesn't contact external services during inference.",
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"weight": "CRITICAL",
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"qwen35_score": 10,
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"notes": "Ollama is fully offline-capable. No telemetry in Qwen weights.",
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},
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{
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"criterion": "Tool-use safety",
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"description": "Model correctly follows tool schemas without prompt injection via tool results.",
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"weight": "HIGH",
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"qwen35_score": 7,
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"notes": "Qwen3.5 supports function calling but MoE models can be less predictable with tool dispatch. Needs live testing.",
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},
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{
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"criterion": "Privacy filter compatibility",
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"description": "Works with Vitalik's Input Privacy Filter pattern.",
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"weight": "HIGH",
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"qwen35_score": 9,
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"notes": "Local model means the Privacy Filter (which strips PII before remote calls) becomes unnecessary for most queries.",
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},
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{
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"criterion": "Two-factor confirmation compatibility",
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"description": "Can serve as the LLM half of Human+LLM confirmation.",
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"weight": "MEDIUM",
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"qwen35_score": 8,
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"notes": "3B active params means fast inference for confirmation prompts. Good for the 'cheap first pass' in two-factor flow.",
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},
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{
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"criterion": "Prompt injection resistance",
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"description": "Resists adversarial prompts that attempt to bypass safety.",
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"weight": "HIGH",
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"qwen35_score": 6,
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"notes": "Smaller active expert size (3B) may be more susceptible to injection than dense 14B+ models. Needs red-team testing.",
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},
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]
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# =========================================================================
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# Deployment Feasibility
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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)",
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"unified_memory_gb": 192,
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"can_run_q4": True,
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"can_run_q8": True,
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"recommended_quant": "Q6_K",
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"est_tokens_per_sec": 40,
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"notes": "Comfortable fit. Room for other models.",
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},
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"mac_m4_pro_48gb": {
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"name": "Mac Mini M4 Pro (48GB)",
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"unified_memory_gb": 48,
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"can_run_q4": True,
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"can_run_q8": False,
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"recommended_quant": "Q4_K_M",
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"est_tokens_per_sec": 30,
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"notes": "Fits at Q4 with ~28GB headroom for OS + other processes.",
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},
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"mac_m1_16gb": {
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"name": "Mac M1 (16GB)",
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"unified_memory_gb": 16,
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"can_run_q4": False,
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"can_run_q8": False,
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"recommended_quant": None,
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"est_tokens_per_sec": None,
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"notes": "Does NOT fit. Need 20GB+ for Q4. Use Qwen2.5:7B or Gemma3:1B instead.",
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},
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"rtx_4090_24gb": {
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"name": "NVIDIA RTX 4090 (24GB VRAM)",
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"unified_memory_gb": 24,
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"can_run_q4": True,
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"can_run_q8": False,
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"recommended_quant": "Q5_K_M",
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"est_tokens_per_sec": 50,
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"notes": "Fits at Q5. Good for dedicated inference server.",
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},
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"rtx_3090_24gb": {
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"name": "NVIDIA RTX 3090 (24GB VRAM)",
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"unified_memory_gb": 24,
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"can_run_q4": True,
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"can_run_q8": False,
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"recommended_quant": "Q4_K_M",
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"est_tokens_per_sec": 35,
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"notes": "Fits at Q4. Slower than 4090 but workable.",
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},
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"runpod_l40s_48gb": {
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"name": "RunPod L40S (48GB VRAM)",
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"unified_memory_gb": 48,
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"can_run_q4": True,
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"can_run_q8": True,
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"recommended_quant": "Q6_K",
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"est_tokens_per_sec": 60,
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"notes": "Cloud GPU option. ~$0.75/hr. Good for Big Brain tier.",
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},
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}
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# =========================================================================
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# Evaluation Engine
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# =========================================================================
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def check_ollama_status() -> Dict[str, Any]:
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"""Check if Ollama is running and what models are available."""
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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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)
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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(
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"qwen3.5" in m.lower() for m in result["models"]
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)
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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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"""Run a single benchmark prompt against an Ollama model."""
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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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)
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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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eval_count = data.get("eval_count", 0)
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eval_duration = data.get("eval_duration", 1)
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tok_per_sec = eval_count / (eval_duration / 1e9) if eval_duration > 0 else 0
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return {
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"success": True,
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"response": response[:500],
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"elapsed_sec": round(elapsed, 1),
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"tokens": eval_count,
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"tok_per_sec": round(tok_per_sec, 1),
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}
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else:
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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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"""Generate the full evaluation report."""
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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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# 1. Model Specs
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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(f" JSON mode: {'Yes' if spec.json_mode_support else 'No'}")
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lines.append(f" Function call: {'Yes' if spec.function_calling else 'No'}")
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# 2. Deployment Feasibility
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lines.append("\n## 2. VRAM Requirements\n")
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lines.append(f" {'Quantization':<12} {'VRAM (GB)':<12} {'Quality'}")
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lines.append(f" {'-'*12} {'-'*12} {'-'*20}")
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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:<12} {vram:<12} {quality}")
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# 3. Hardware Compatibility
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lines.append("\n## 3. Hardware Compatibility\n")
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for hw_id, hw in HARDWARE_PROFILES.items():
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fits = "YES" if hw["can_run_q4"] else "NO"
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rec = hw["recommended_quant"] or "N/A"
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tps = hw["est_tokens_per_sec"] or "N/A"
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lines.append(f" {hw['name']}")
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lines.append(f" {hw['unified_memory_gb']}GB | Fits Q4: {fits} | Rec: {rec} | ~{tps} tok/s")
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lines.append(f" {hw['notes']}")
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# 4. Security Evaluation
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lines.append("\n## 4. Security Evaluation (Vitalik Framework)\n")
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total_weight = 0
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weighted_score = 0
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weight_map = {"CRITICAL": 3, "HIGH": 2, "MEDIUM": 1}
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for c in SECURITY_CRITERIA:
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w = weight_map[c["weight"]]
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total_weight += w
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weighted_score += c["qwen35_score"] * w
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lines.append(f" [{c['weight']:<8}] {c['criterion']}")
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lines.append(f" Score: {c['qwen35_score']}/10 — {c['notes']}")
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avg_score = weighted_score / total_weight if total_weight > 0 else 0
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lines.append(f"\n Weighted security score: {avg_score:.1f}/10")
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lines.append(f" Verdict: {'STRONG' if avg_score >= 8 else 'ADEQUATE' if avg_score >= 6 else 'NEEDS WORK'}")
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# 5. Fleet Comparison
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lines.append("\n## 5. Fleet Comparison\n")
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lines.append(f" {'Model':<30} {'Params':<10} {'Ctx':<8} {'Local':<7} {'Tools':<7} {'Reasoning'}")
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lines.append(f" {'-'*30} {'-'*10} {'-'*8} {'-'*7} {'-'*7} {'-'*12}")
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for name, spec_data in FLEET_MODELS.items():
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lines.append(
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f" {name:<30} {spec_data['params_total']:<10} {spec_data['context']:<8} "
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f"{'Yes' if spec_data['local'] else 'No':<7} {'Yes' if spec_data['tool_use'] else 'No':<7} "
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f"{spec_data['reasoning']}"
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)
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# 6. Ollama Status
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lines.append("\n## 6. Local Ollama Status\n")
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lines.append(f" Running: {'Yes' if ollama['running'] else 'No'}")
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lines.append(f" Installed: {', '.join(ollama['models']) if ollama['models'] else 'none'}")
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lines.append(f" Qwen3.5 avail: {'Yes' if ollama['qwen35_available'] else 'No — run: ollama pull qwen3.5:35b'}")
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# 7. Recommendation
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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\n")
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lines.append(" Strengths:")
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lines.append(" + Perfect data sovereignty (Vitalik's #1 requirement)")
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lines.append(" + MoE architecture: 35B quality at 3B inference speed")
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lines.append(" + 128K context — matches cloud models")
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lines.append(" + Apache 2.0 — no license restrictions")
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lines.append(" + Tool use + JSON mode + function calling supported")
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lines.append(" + Eliminates need for Privacy Filter on most queries")
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lines.append("")
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lines.append(" Weaknesses:")
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lines.append(" - 20GB VRAM at Q4 — requires beefy hardware")
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lines.append(" - MoE routing less predictable than dense models")
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lines.append(" - 3B active params may be weaker on complex reasoning")
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lines.append(" - Needs red-team testing for prompt injection")
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lines.append("")
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lines.append(" Deployment plan:")
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lines.append(" 1. Pull: ollama pull qwen3.5:35b")
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lines.append(" 2. Add to config.yaml as privacy-sensitive model")
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lines.append(" 3. Route PII-flagged queries through local Qwen3.5")
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lines.append(" 4. Keep cloud models for non-sensitive complex work")
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lines.append(" 5. Run red-team tests (issue #324) against local model")
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# 8. Integration Path
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lines.append("\n## 8. Integration Path\n")
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lines.append(" Config addition (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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lines.append('')
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lines.append(' smart_model_routing integration:')
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lines.append(' Route queries containing PII patterns to local Qwen3.5')
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lines.append(' instead of cloud models, eliminating data exfiltration risk.')
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return "\n".join(lines)
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# =========================================================================
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# CLI
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# =========================================================================
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if __name__ == "__main__":
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if "--check-ollama" in sys.argv:
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status = check_ollama_status()
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print(json.dumps(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(f"Benchmarking {model}...")
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result = run_benchmark(model, "Explain the security benefits of local LLM inference in 3 sentences.")
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print(json.dumps(result, indent=2))
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else:
|
|
print(generate_report())
|