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queue/378-
| Author | SHA1 | Date | |
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| 628487f7bd |
@@ -13,6 +13,7 @@ import concurrent.futures
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import json
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import logging
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import os
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import re
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import subprocess
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import sys
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@@ -643,7 +644,56 @@ def _build_job_prompt(job: dict) -> str:
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return "\n".join(parts)
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def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:
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# Regex patterns for local service references that fail on cloud endpoints
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_CLOUD_INCOMPATIBLE_PATTERNS = [
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(re.compile(r"\b[Cc]heck\s+(?:that\s+)?[Oo]llama\s+(?:is\s+)?(?:responding|running|up|available)", re.IGNORECASE),
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"Verify system services are healthy using available tools"),
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(re.compile(r"\b[Vv]erify\s+(?:that\s+)?[Oo]llama\s+(?:is\s+)?(?:responding|running|up)", re.IGNORECASE),
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"Verify system services are healthy using available tools"),
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(re.compile(r"\bcurl\s+localhost:\d+", re.IGNORECASE),
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"use available tools to check service health"),
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(re.compile(r"\bcurl\s+127\.0\.0\.1:\d+", re.IGNORECASE),
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"use available tools to check service health"),
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(re.compile(r"\bpoll\s+localhost", re.IGNORECASE),
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"check service health via available tools"),
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]
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def _rewrite_cloud_incompatible_prompt(prompt: str, base_url: str) -> str:
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"""Rewrite prompt instructions that assume local service access when running on cloud.
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When a cron job runs on a cloud inference endpoint (Nous, OpenRouter, Anthropic),
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instructions to "Check Ollama" or "curl localhost:11434" are impossible.
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Instead of just warning, this rewrites the instruction to a cloud-compatible
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equivalent that the agent can actually execute.
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Returns the (possibly rewritten) prompt.
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"""
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try:
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from agent.model_metadata import is_local_endpoint
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except ImportError:
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return prompt
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if is_local_endpoint(base_url or ""):
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return prompt # Local — no rewrite needed
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rewritten = prompt
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for pattern, replacement in _CLOUD_INCOMPATIBLE_PATTERNS:
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rewritten = pattern.sub(replacement, rewritten)
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if rewritten != prompt:
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rewritten = (
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"[NOTE: Some instructions were adjusted for cloud execution. "
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"Local service checks were rewritten to use available tools.]
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"
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+ rewritten
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)
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return rewritten
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def run_job(job: dict) -> tuple[bool, str, str, Optional[str]]:(job: dict) -> tuple[bool, str, str, Optional[str]]:
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"""
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Execute a single cron job.
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@@ -1,114 +0,0 @@
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#!/usr/bin/env python3
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"""Evaluate Qwen3.5:35B as a local model option -- Issue #288, Epic #281."""
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import json, sys, time
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from dataclasses import dataclass, field
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from typing import Any, Dict
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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)": {"params_total": "35B", "context": "128K", "local": True, "tool_use": True, "reasoning": "good"},
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"gemma4 (current local)": {"params_total": "9B", "context": "128K", "local": True, "tool_use": True, "reasoning": "good"},
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"hermes4:14b (current local)": {"params_total": "14B", "context": "8K", "local": True, "tool_use": True, "reasoning": "good"},
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"qwen2.5:7b (fleet)": {"params_total": "7B", "context": "32K", "local": True, "tool_use": True, "reasoning": "moderate"},
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"claude-sonnet-4 (cloud)": {"params_total": "?", "context": "200K", "local": False, "tool_use": True, "reasoning": "excellent"},
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"mimo-v2-pro (cloud free)": {"params_total": "?", "context": "128K", "local": False, "tool_use": True, "reasoning": "good"},
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}
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SECURITY_CRITERIA = [
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{"criterion": "Data locality", "weight": "CRITICAL", "score": 10, "notes": "All inference local via Ollama. Zero exfiltration."},
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{"criterion": "No API key dependency", "weight": "HIGH", "score": 10, "notes": "Pure local inference. No external creds needed."},
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{"criterion": "No telemetry", "weight": "CRITICAL", "score": 10, "notes": "Ollama fully offline-capable. No phone-home."},
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{"criterion": "Model weights auditable", "weight": "MEDIUM", "score": 8, "notes": "Apache 2.0, HF SHA verification. MoE harder to audit."},
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{"criterion": "Tool-use safety", "weight": "HIGH", "score": 7, "notes": "Function calling supported, MoE routing less predictable. Benchmark: #502."},
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{"criterion": "Privacy filter compat", "weight": "HIGH", "score": 9, "notes": "Local = Privacy Filter unnecessary for most queries."},
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{"criterion": "Two-factor confirmation", "weight": "MEDIUM", "score": 8, "notes": "3B active = fast inference for confirmation prompts."},
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{"criterion": "Prompt injection resistance", "weight": "HIGH", "score": 6, "notes": "3B active may be weaker. Needs red-team (#324)."},
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]
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HARDWARE_PROFILES = {
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"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},
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"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},
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"mac_m1_16gb": {"name": "Mac M1 (16GB)", "mem_gb": 16, "fits_q4": False, "fits_q8": False, "rec": None, "tok_sec": None},
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"rtx_4090_24gb": {"name": "NVIDIA RTX 4090 (24GB)", "mem_gb": 24, "fits_q4": True, "fits_q8": False, "rec": "Q5_K_M", "tok_sec": 50},
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"rtx_3090_24gb": {"name": "NVIDIA RTX 3090 (24GB)", "mem_gb": 24, "fits_q4": True, "fits_q8": False, "rec": "Q4_K_M", "tok_sec": 35},
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"runpod_l40s_48gb": {"name": "RunPod L40S (48GB)", "mem_gb": 48, "fits_q4": True, "fits_q8": True, "rec": "Q6_K", "tok_sec": 60},
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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(["curl", "-s", "--max-time", "5", "http://localhost:11434/api/tags"], 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 generate_report() -> str:
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spec = ModelSpec()
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ollama = check_ollama_status()
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lines = ["=" * 72, "Qwen3.5:35B EVALUATION REPORT -- Issue #288", "Epic #281 -- Vitalik Secure LLM Architecture", "=" * 72]
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lines.append("\n## 1. Model Specification\n")
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lines.append(f" Name: {spec.name} | Arch: {spec.architecture}")
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lines.append(f" Params: {spec.total_params} total, {spec.active_params} | Context: {spec.context_length:,} tokens")
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lines.append(f" License: {spec.license} | Tools: {spec.tool_use_support} | JSON: {spec.json_mode_support}")
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lines.append("\n## 2. VRAM\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\n")
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for hw in HARDWARE_PROFILES.values():
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lines.append(f" {hw['name']} {hw['mem_gb']}GB Q4:{'YES' if hw['fits_q4'] else 'NO '} Rec:{hw['rec'] or 'N/A'} ~{hw['tok_sec'] or 'N/A'} tok/s")
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lines.append("\n## 4. Security (Vitalik Framework)\n")
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wm = {"CRITICAL": 3, "HIGH": 2, "MEDIUM": 1}
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tw = sum(wm[c["weight"]] for c in SECURITY_CRITERIA)
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ws = sum(c["score"] * wm[c["weight"]] for c in SECURITY_CRITERIA)
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for c in SECURITY_CRITERIA:
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lines.append(f" [{c['weight']:<8}] {c['criterion']}: {c['score']}/10 -- {c['notes']}")
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avg = ws / tw
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lines.append(f"\n Weighted: {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\n")
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lines.append(f" Running: {'Yes' if ollama['running'] else 'No'} | 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, 128K context, Apache 2.0, MoE speed")
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lines.append(" + Tool use + JSON mode, eliminates Privacy Filter for most queries")
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lines.append(" - 20GB VRAM at Q4, MoE less predictable, needs red-team testing")
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lines.append("\n Follow-up issues filed:")
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lines.append(" #502: live tool dispatch benchmark")
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lines.append(" #503: reasoning benchmark vs hermes4:14b")
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lines.append(" #518: document minimum hardware requirements fleet-wide")
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lines.append(" #324: prompt injection red-team testing")
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lines.append("\n Deployment: ollama pull qwen3.5:35b -> config.yaml privacy_model")
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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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else:
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print(generate_report())
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@@ -1,50 +0,0 @@
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"""Tests for Qwen3.5:35B evaluation -- Issue #288."""
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import pytest
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from scripts.evaluate_qwen35 import ModelSpec, FLEET_MODELS, SECURITY_CRITERIA, HARDWARE_PROFILES, check_ollama_status, generate_report
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class TestModelSpec:
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def test_fields(self):
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s = ModelSpec()
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assert s.name == "Qwen3.5-35B-A3B"
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assert s.context_length == 131072
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assert s.license == "Apache 2.0"
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assert s.tool_use_support is True
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def test_quant_vram_decreasing(self):
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s = ModelSpec()
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items = sorted(s.quantization_options.items(), key=lambda x: x[1])
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for i in range(1, len(items)):
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assert items[i][1] >= items[i-1][1]
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class TestSecurity:
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def test_scores(self):
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for c in SECURITY_CRITERIA:
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assert 1 <= c["score"] <= 10
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def test_weighted_avg(self):
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wm = {"CRITICAL": 3, "HIGH": 2, "MEDIUM": 1}
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tw = sum(wm[c["weight"]] for c in SECURITY_CRITERIA)
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ws = sum(c["score"] * wm[c["weight"]] for c in SECURITY_CRITERIA)
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assert ws / tw >= 7.0
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class TestHardware:
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def test_m2_fits(self):
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assert HARDWARE_PROFILES["mac_m2_ultra_192gb"]["fits_q4"] is True
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def test_m1_no(self):
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assert HARDWARE_PROFILES["mac_m1_16gb"]["fits_q4"] is False
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class TestReport:
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def test_sections(self):
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r = generate_report()
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for s in ["Model Specification", "VRAM", "Hardware", "Security", "Fleet", "Recommendation"]:
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assert s in r
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def test_approved(self):
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assert "APPROVED" in generate_report()
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def test_follow_up_issues_referenced(self):
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r = generate_report()
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for issue in ["#502", "#503", "#518", "#324"]:
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assert issue in r
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class TestOllama:
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def test_returns_dict(self):
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r = check_ollama_status()
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assert isinstance(r, dict)
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assert "running" in r
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