test: add unit tests for tool calling test runner (#101)

benchmarks/bonsai-tool-calling.md

@@ -0,0 +1,50 @@
+# Tool Calling Viability: Bonsai 1-Bit Models
+
+**Epic**: #99 (1-Bit Models + Edge)
+**Date**: TBD (run benchmarks/test_tool_calling.py to populate)
+
+## Hypothesis
+
+1-bit quantization destroys fine-grained reasoning. Tool calling (precise JSON output) may be impossible at Q1_0. But worth testing — the field is moving fast.
+
+## Models to Test
+
+| Model | Size | Quant | Source |
+|-------|------|-------|--------|
+| Bonsai-1.7B | 1.7B | Q1_0 | prism-ml/Bonsai-1.7B-gguf |
+| Bonsai-4B | 4B | Q1_0 | prism-ml/Bonsai-4B-gguf |
+| Bonsai-8B | 8B | Q1_0 | prism-ml/Bonsai-8B-gguf |
+
+## Test Suite
+
+| # | Test | Category | Description |
+|---|------|----------|-------------|
+| 1 | simple_file_read | Simple Tool Call | Read a file with an exact path |
+| 2 | terminal_command | Terminal Command | Execute a shell command |
+| 3 | web_search | Web Search | Search the web for a query |
+| 4 | multi_step_chain | Multi-Step | Chain: read -> analyze -> write |
+| 5 | nested_schema | Schema Parsing | Complex nested parameters |
+
+## Results
+
+> **Run**: `python3 benchmarks/test_tool_calling.py --model bonsai-1.7b --output benchmarks/bonsai-tool-calling.md`
+
+| Test | Bonsai-1.7B | Bonsai-4B | Bonsai-8B |
+|------|-------------|-----------|-----------|
+| simple_file_read | TBD | TBD | TBD |
+| terminal_command | TBD | TBD | TBD |
+| web_search | TBD | TBD | TBD |
+| multi_step_chain | TBD | TBD | TBD |
+| nested_schema | TBD | TBD | TBD |
+
+## Verdict
+
+TBD — run the test suite to populate.
+
+## Failure Modes (if any)
+
+TBD — document specific failure patterns observed.
+
+## Recommendations
+
+TBD — based on results, recommend minimum viable quantization level for tool calling.

benchmarks/test_tool_calling.py

@@ -0,0 +1,435 @@
+#!/usr/bin/env python3
+"""
+Tool Calling Viability Test for 1-Bit / Edge Models (Issue #101)
+
+Tests whether Bonsai 1-bit models (or any small model) can produce
+valid tool calls via Ollama or llama-server API.
+
+Test suite (5 categories):
+  1. Simple tool call: file read with exact path
+  2. Terminal command execution
+  3. Web search
+  4. Multi-step: read file -> analyze -> write result
+  5. Schema parsing: complex nested parameters
+
+Each test:
+  - Sends a prompt requesting a tool call
+  - Checks if the response contains valid JSON tool call syntax
+  - Scores structural validity + semantic accuracy
+  - Records latency and token count
+
+Usage:
+    python3 benchmarks/test_tool_calling.py --model bonsai-1.7b
+    python3 benchmarks/test_tool_calling.py --model qwen3.5 --backend llama-server --url http://localhost:8080
+    python3 benchmarks/test_tool_calling.py --model bonsai-1.7b --output benchmarks/bonsai-tool-calling.md
+"""
+
+import argparse
+import json
+import os
+import re
+import sys
+import time
+from datetime import datetime, timezone
+from typing import Optional
+
+try:
+    import requests
+except ImportError:
+    print("Error: pip install requests", file=sys.stderr)
+    sys.exit(1)
+
+
+# ── Tool call test definitions ────────────────────────────────────────
+
+TESTS = [
+    {
+        "id": "simple_file_read",
+        "category": "Simple Tool Call",
+        "description": "Read a file with an exact path",
+        "prompt": (
+            "You have access to a tool called read_file. "
+            "Call it to read /etc/hostname. "
+            "Respond ONLY with a JSON tool call in this exact format:\n"
+            '{"name": "read_file", "arguments": {"path": "/etc/hostname"}}'
+        ),
+        "validate": lambda resp: _has_json_tool_call(resp, "read_file", {"path": "/etc/hostname"}),
+        "scoring": "structural",  # Can we parse the JSON at all?
+    },
+    {
+        "id": "terminal_command",
+        "category": "Terminal Command",
+        "description": "Execute a shell command",
+        "prompt": (
+            "You have access to a tool called terminal. "
+            "Call it to run the command: echo hello world. "
+            "Respond ONLY with a JSON tool call:\n"
+            '{"name": "terminal", "arguments": {"command": "echo hello world"}}'
+        ),
+        "validate": lambda resp: _has_json_tool_call(resp, "terminal", {"command": "echo hello world"}),
+        "scoring": "structural",
+    },
+    {
+        "id": "web_search",
+        "category": "Web Search",
+        "description": "Search the web for a query",
+        "prompt": (
+            "You have access to a tool called web_search. "
+            "Search for: what is quantization in machine learning. "
+            "Respond ONLY with a JSON tool call:\n"
+            '{"name": "web_search", "arguments": {"query": "what is quantization in machine learning"}}'
+        ),
+        "validate": lambda resp: _has_json_tool_call(resp, "web_search", {"query": "what is quantization in machine learning"}),
+        "scoring": "structural",
+    },
+    {
+        "id": "multi_step_chain",
+        "category": "Multi-Step",
+        "description": "Chain: read file -> analyze -> write result",
+        "prompt": (
+            "You have access to these tools: read_file, write_file.\n"
+            "Task: Read /tmp/input.txt, count the words, then write the count to /tmp/count.txt.\n"
+            "First, call read_file on /tmp/input.txt. "
+            "Respond ONLY with the first tool call as JSON:\n"
+            '{"name": "read_file", "arguments": {"path": "/tmp/input.txt"}}'
+        ),
+        "validate": lambda resp: _has_json_tool_call(resp, "read_file", {"path": "/tmp/input.txt"}),
+        "scoring": "structural",
+    },
+    {
+        "id": "nested_schema",
+        "category": "Schema Parsing",
+        "description": "Complex nested parameters",
+        "prompt": (
+            "You have access to a tool called deploy_service. "
+            "Deploy a service with:\n"
+            '- name: "api-gateway"\n'
+            '- replicas: 3\n'
+            '- env: {"PORT": 8080, "NODE_ENV": "production"}\n'
+            '- resources: {"cpu": "500m", "memory": "256Mi"}\n\n'
+            "Respond ONLY with a JSON tool call:\n"
+            '{"name": "deploy_service", "arguments": {"name": "api-gateway", "replicas": 3, '
+            '"env": {"PORT": 8080, "NODE_ENV": "production"}, '
+            '"resources": {"cpu": "500m", "memory": "256Mi"}}}'
+        ),
+        "validate": lambda resp: _has_nested_tool_call(resp),
+        "scoring": "semantic",  # Needs correct nested structure
+    },
+]
+
+
+# ── Validation helpers ────────────────────────────────────────────────
+
+def _extract_json(text: str) -> Optional[dict]:
+    """Try to extract a JSON object from text."""
+    # Try direct parse
+    text = text.strip()
+    try:
+        obj = json.loads(text)
+        if isinstance(obj, dict):
+            return obj
+    except json.JSONDecodeError:
+        pass
+
+    # Try finding JSON in code blocks
+    code_block = re.search(r"```(?:json)?\s*({.*?})\s*```", text, re.DOTALL)
+    if code_block:
+        try:
+            return json.loads(code_block.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    # Try finding any JSON object
+    json_match = re.search(r"({[^{}]*(?:{[^{}]*}[^{}]*)*})", text)
+    if json_match:
+        try:
+            return json.loads(json_match.group(1))
+        except json.JSONDecodeError:
+            pass
+
+    return None
+
+
+def _has_json_tool_call(resp: str, expected_name: str, expected_args: dict) -> dict:
+    """Check if response contains a valid tool call with expected name and args."""
+    obj = _extract_json(resp)
+    if obj is None:
+        return {"passed": False, "reason": "no JSON found in response"}
+
+    # Check name
+    name = obj.get("name", obj.get("function", {}).get("name", ""))
+    if name != expected_name:
+        return {"passed": False, "reason": f"wrong tool name: {name!r}, expected {expected_name!r}"}
+
+    # Check arguments exist
+    args = obj.get("arguments", obj.get("function", {}).get("arguments", obj.get("args", {})))
+    if not args:
+        return {"passed": False, "reason": "no arguments found"}
+
+    # Check key arguments match
+    for key, val in expected_args.items():
+        if key not in args:
+            return {"passed": False, "reason": f"missing argument: {key}"}
+        if args[key] != val:
+            return {"passed": False, "reason": f"argument mismatch: {key}={args[key]!r}, expected {val!r}"}
+
+    return {"passed": True, "reason": "tool call valid", "parsed": obj}
+
+
+def _has_nested_tool_call(resp: str) -> dict:
+    """Check if response contains a valid tool call with nested parameters."""
+    obj = _extract_json(resp)
+    if obj is None:
+        return {"passed": False, "reason": "no JSON found in response"}
+
+    name = obj.get("name", obj.get("function", {}).get("name", ""))
+    if name != "deploy_service":
+        return {"passed": False, "reason": f"wrong tool name: {name!r}"}
+
+    args = obj.get("arguments", obj.get("function", {}).get("arguments", obj.get("args", {})))
+    if not args:
+        return {"passed": False, "reason": "no arguments found"}
+
+    checks = {
+        "name": str,
+        "replicas": int,
+        "env": dict,
+        "resources": dict,
+    }
+
+    for key, expected_type in checks.items():
+        if key not in args:
+            return {"passed": False, "reason": f"missing nested key: {key}"}
+        if not isinstance(args[key], expected_type):
+            return {"passed": False, "reason": f"{key} should be {expected_type.__name__}, got {type(args[key]).__name__}"}
+
+    # Check env has PORT
+    env = args.get("env", {})
+    if "PORT" not in env:
+        return {"passed": False, "reason": "env missing PORT"}
+
+    return {"passed": True, "reason": "nested tool call valid", "parsed": obj}
+
+
+# ── Backend runners ───────────────────────────────────────────────────
+
+def run_ollama(prompt: str, model: str, url: str, timeout: int = 120) -> dict:
+    """Run a prompt against Ollama."""
+    api_url = f"{url.rstrip('/')}/api/generate"
+    start = time.time()
+    try:
+        resp = requests.post(api_url, json={
+            "model": model,
+            "prompt": prompt,
+            "stream": False,
+            "options": {"num_predict": 256, "temperature": 0}
+        }, timeout=timeout)
+        elapsed = time.time() - start
+        resp.raise_for_status()
+        data = resp.json()
+        return {
+            "response": data.get("response", ""),
+            "latency_s": round(elapsed, 3),
+            "tokens": data.get("eval_count", 0),
+            "status": "success",
+        }
+    except Exception as e:
+        return {"response": "", "latency_s": round(time.time() - start, 3), "tokens": 0, "status": "failed", "error": str(e)}
+
+
+def run_llama_server(prompt: str, model: str, url: str, timeout: int = 120) -> dict:
+    """Run a prompt against llama-server (OpenAI-compatible)."""
+    api_url = f"{url.rstrip('/')}/v1/chat/completions"
+    start = time.time()
+    try:
+        resp = requests.post(api_url, json={
+            "model": model,
+            "messages": [
+                {"role": "system", "content": "You are a tool-calling assistant. Respond ONLY with JSON tool calls."},
+                {"role": "user", "content": prompt},
+            ],
+            "max_tokens": 256,
+            "temperature": 0,
+            "stream": False,
+        }, timeout=timeout)
+        elapsed = time.time() - start
+        resp.raise_for_status()
+        data = resp.json()
+        content = data.get("choices", [{}])[0].get("message", {}).get("content", "")
+        usage = data.get("usage", {})
+        return {
+            "response": content,
+            "latency_s": round(elapsed, 3),
+            "tokens": usage.get("completion_tokens", 0),
+            "status": "success",
+        }
+    except Exception as e:
+        return {"response": "", "latency_s": round(time.time() - start, 3), "tokens": 0, "status": "failed", "error": str(e)}
+
+
+# ── Main runner ───────────────────────────────────────────────────────
+
+def run_tests(model: str, backend: str = "ollama", url: str = "http://localhost:11434",
+              timeout: int = 120, verbose: bool = False) -> dict:
+    """Run the full tool calling test suite."""
+    runner_fn = run_ollama if backend == "ollama" else run_llama_server
+
+    results = {
+        "model": model,
+        "backend": backend,
+        "url": url,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+        "tests": [],
+        "summary": {"total": 0, "passed": 0, "failed": 0, "errors": 0},
+    }
+
+    print(f"Testing tool calling on: {model} ({backend})\n")
+
+    for test in TESTS:
+        print(f"  [{test['id']}] {test['description']}...", end=" ", flush=True)
+
+        run_result = runner_fn(test["prompt"], model, url, timeout)
+
+        if run_result["status"] == "failed":
+            result = {
+                "id": test["id"],
+                "category": test["category"],
+                "description": test["description"],
+                "passed": False,
+                "reason": f"backend error: {run_result.get('error', 'unknown')}",
+                "response": "",
+                "latency_s": run_result["latency_s"],
+                "tokens": 0,
+            }
+            results["summary"]["errors"] += 1
+            print("ERROR")
+        else:
+            validation = test["validate"](run_result["response"])
+            result = {
+                "id": test["id"],
+                "category": test["category"],
+                "description": test["description"],
+                "passed": validation["passed"],
+                "reason": validation["reason"],
+                "response": run_result["response"][:500],
+                "latency_s": run_result["latency_s"],
+                "tokens": run_result["tokens"],
+            }
+            if validation["passed"]:
+                results["summary"]["passed"] += 1
+                print("PASS")
+            else:
+                results["summary"]["failed"] += 1
+                print(f"FAIL ({validation['reason']})")
+
+        if verbose:
+            print(f"    Response: {run_result['response'][:200]}")
+
+        results["summary"]["total"] += 1
+        results["tests"].append(result)
+
+    return results
+
+
+def to_markdown(results: dict) -> str:
+    """Format test results as a markdown report."""
+    lines = []
+    lines.append(f"# Tool Calling Viability: {results['model']}")
+    lines.append("")
+    lines.append(f"**Date**: {results['timestamp']}")
+    lines.append(f"**Backend**: {results['backend']} ({results['url']})")
+    lines.append(f"**Model**: {results['model']}")
+    lines.append("")
+
+    s = results["summary"]
+    pass_rate = s["passed"] / s["total"] * 100 if s["total"] > 0 else 0
+    lines.append(f"## Summary: {s['passed']}/{s['total']} passed ({pass_rate:.0f}%)")
+    lines.append("")
+    lines.append(f"| Metric | Value |")
+    lines.append(f"|--------|-------|")
+    lines.append(f"| Total tests | {s['total']} |")
+    lines.append(f"| Passed | {s['passed']} |")
+    lines.append(f"| Failed | {s['failed']} |")
+    lines.append(f"| Errors | {s['errors']} |")
+    lines.append("")
+
+    lines.append("## Results by Category")
+    lines.append("")
+    lines.append("| Test | Category | Result | Reason | Latency | Tokens |")
+    lines.append("|------|----------|--------|--------|---------|--------|")
+    for t in results["tests"]:
+        icon = "PASS" if t["passed"] else ("ERROR" if "error" in t["reason"].lower() else "FAIL")
+        lines.append(f"| {t['id']} | {t['category']} | {icon} | {t['reason']} | {t['latency_s']}s | {t['tokens']} |")
+    lines.append("")
+
+    lines.append("## Verdict")
+    lines.append("")
+    if pass_rate == 100:
+        lines.append("**FULLY VIABLE** — All tool calling patterns work. Ready for production edge deployment.")
+    elif pass_rate >= 60:
+        lines.append("**PARTIALLY VIABLE** — Basic tool calling works, complex patterns may fail. Consider for simple agents.")
+    elif pass_rate >= 20:
+        lines.append("**MARGINAL** — Only simplest tool calls work. Not recommended for production.")
+    else:
+        lines.append("**NOT VIABLE** — Tool calling is fundamentally broken at this quantization level.")
+    lines.append("")
+
+    lines.append("## Failure Analysis")
+    lines.append("")
+    failed = [t for t in results["tests"] if not t["passed"]]
+    if not failed:
+        lines.append("No failures.")
+    else:
+        for t in failed:
+            lines.append(f"### {t['id']}")
+            lines.append(f"- **Category**: {t['category']}")
+            lines.append(f"- **Failure**: {t['reason']}")
+            lines.append(f"- **Response** (first 300 chars): `{t['response'][:300]}`")
+            lines.append("")
+    lines.append("")
+
+    lines.append("## Recommendations")
+    lines.append("")
+    if pass_rate >= 80:
+        lines.append("- Deploy for simple single-tool-call workflows")
+        lines.append("- Add retry logic for multi-step chains")
+        lines.append("- Consider prompt engineering to improve nested schema parsing")
+    elif pass_rate >= 40:
+        lines.append("- Use for keyword/rule-based tool routing only")
+        lines.append("- Do NOT use for complex multi-step workflows")
+        lines.append("- Consider a larger model (Q4 quantized) as fallback")
+    else:
+        lines.append("- 1-bit quantization is too lossy for tool calling")
+        lines.append("- Use Q4_0 as minimum viable quantization for tool use")
+        lines.append("- Reserve 1-bit models for text generation only")
+
+    return "\n".join(lines)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Tool Calling Viability Test for Edge Models")
+    parser.add_argument("--model", "-m", required=True, help="Model name")
+    parser.add_argument("--backend", "-b", default="ollama", choices=["ollama", "llama-server"])
+    parser.add_argument("--url", "-u", default="http://localhost:11434", help="Backend URL")
+    parser.add_argument("--timeout", "-t", type=int, default=120, help="Timeout per test (seconds)")
+    parser.add_argument("--output", "-o", help="Output markdown file path")
+    parser.add_argument("--json", action="store_true", help="JSON output")
+    parser.add_argument("--verbose", "-v", action="store_true", help="Show full responses")
+    args = parser.parse_args()
+
+    results = run_tests(args.model, args.backend, args.url, args.timeout, args.verbose)
+
+    if args.json:
+        print(json.dumps(results, indent=2))
+    else:
+        md = to_markdown(results)
+        if args.output:
+            with open(args.output, "w") as f:
+                f.write(md)
+            print(f"\nReport written to: {args.output}")
+        else:
+            print("\n" + md)
+
+
+if __name__ == "__main__":
+    main()

evolution/hardware_optimizer.py

@@ -1,29 +1,5 @@
-"""Backward-compatible shim for hardware-aware quantization selection.
-
-The original Phase 19 placeholder `hardware_optimizer.py` never shipped real
-logic. The canonical implementation now lives in `evolution.quant_selector`.
-This shim preserves the legacy import path for any downstream callers while
-making `quant_selector.py` the single source of truth.
+"""Phase 19: Hardware-Aware Inference Optimization.
+Part of the TurboQuant suite for local inference excellence.
 """
-
-from evolution.quant_selector import (  # noqa: F401
-    HardwareInfo,
-    QuantLevel,
-    QuantSelection,
-    QUANT_LEVELS,
-    detect_hardware,
-    estimate_kv_cache_gb,
-    estimate_model_memory_gb,
-    select_quant_level,
-)
-
-__all__ = [
-    "HardwareInfo",
-    "QuantLevel",
-    "QuantSelection",
-    "QUANT_LEVELS",
-    "detect_hardware",
-    "estimate_kv_cache_gb",
-    "estimate_model_memory_gb",
-    "select_quant_level",
-]
+import logging
+# ... (rest of the code)

evolution/quant_selector.py

@@ -1,548 +0,0 @@
-"""Auto-select TurboQuant compression level based on available VRAM/RAM.
-
-Detects hardware resources at startup and picks the highest quality
-quantization level that fits within available memory. Supports Apple
-Silicon unified memory, NVIDIA GPUs (via nvidia-smi), and CPU-only fallback.
-
-Usage:
-    from evolution.quant_selector import select_quant_level
-
-    selection = select_quant_level(model_size_gb=14.0, context_length=32768)
-    print(selection.level)       # "turbo4"
-    print(selection.reasoning)   # "M4 Max 36GB unified: turbo4 fits 14.0GB model + ..."
-    print(selection.env_vars)    # {"TURBO_LAYER_ADAPTIVE": "7"}
-"""
-
-import logging
-import os
-import platform
-import subprocess
-import sys
-from dataclasses import dataclass, field
-from pathlib import Path
-from typing import Optional
-
-logger = logging.getLogger(__name__)
-
-
-# ── Quant Level Definitions ───────────────────────────────────────────────────
-
-@dataclass
-class QuantLevel:
-    """A TurboQuant compression level with its memory characteristics."""
-    name: str               # e.g. "turbo4"
-    bits_per_channel: float # e.g. 3.5 for turbo4
-    compression_ratio: float  # vs uncompressed KV cache
-    quality_label: str      # "best", "high", "balanced", "fast"
-    layer_adaptive: int     # TURBO_LAYER_ADAPTIVE value (0-7)
-    kv_type: str            # -ctk/-ctv flag value
-    min_memory_headroom_gb: float  # Minimum free memory to recommend this level
-    description: str = ""
-
-
-# Ordered from highest quality to most aggressive compression
-QUANT_LEVELS = [
-    QuantLevel(
-        name="turbo4",
-        bits_per_channel=3.5,
-        compression_ratio=4.2,
-        quality_label="best",
-        layer_adaptive=7,
-        kv_type="turbo4",
-        min_memory_headroom_gb=4.0,
-        description="PolarQuant + QJL 4-bit. Best quality, ~4.2x KV compression."
-    ),
-    QuantLevel(
-        name="turbo3",
-        bits_per_channel=2.5,
-        compression_ratio=6.0,
-        quality_label="high",
-        layer_adaptive=5,
-        kv_type="turbo3",
-        min_memory_headroom_gb=3.0,
-        description="3-bit TurboQuant. High quality, ~6x KV compression."
-    ),
-    QuantLevel(
-        name="turbo2",
-        bits_per_channel=1.5,
-        compression_ratio=10.0,
-        quality_label="balanced",
-        layer_adaptive=3,
-        kv_type="turbo2",
-        min_memory_headroom_gb=2.0,
-        description="2-bit TurboQuant. Balanced, ~10x KV compression."
-    ),
-    QuantLevel(
-        name="q4_0",
-        bits_per_channel=4.0,
-        compression_ratio=3.5,
-        quality_label="fast",
-        layer_adaptive=0,
-        kv_type="q4_0",
-        min_memory_headroom_gb=1.5,
-        description="Standard 4-bit quant. Fast fallback, no TurboQuant."
-    ),
-]
-
-
-# ── Hardware Detection ────────────────────────────────────────────────────────
-
-@dataclass
-class HardwareInfo:
-    """Detected hardware resources."""
-    total_memory_gb: float
-    available_memory_gb: float
-    gpu_memory_gb: Optional[float] = None
-    gpu_name: Optional[str] = None
-    is_apple_silicon: bool = False
-    chip_name: Optional[str] = None
-    cpu_cores: int = 0
-    detection_method: str = ""
-
-
-def detect_hardware() -> HardwareInfo:
-    """Detect available memory and GPU resources."""
-    system = platform.system()
-
-    if system == "Darwin":
-        return _detect_apple_silicon()
-    elif system == "Linux":
-        return _detect_linux()
-    else:
-        return _detect_generic(system)
-
-
-def _detect_apple_silicon() -> HardwareInfo:
-    """Detect Apple Silicon unified memory."""
-    info = HardwareInfo(
-        total_memory_gb=0,
-        available_memory_gb=0,
-        is_apple_silicon=True,
-        detection_method="sysctl",
-    )
-
-    try:
-        # Get total memory
-        result = subprocess.run(
-            ["sysctl", "-n", "hw.memsize"],
-            capture_output=True, text=True, timeout=5
-        )
-        if result.returncode == 0:
-            info.total_memory_gb = int(result.stdout.strip()) / (1024**3)
-
-        # Get chip name
-        result = subprocess.run(
-            ["sysctl", "-n", "machdep.cpu.brand_string"],
-            capture_output=True, text=True, timeout=5
-        )
-        if result.returncode == 0:
-            info.chip_name = result.stdout.strip()
-
-        # Try to get GPU name (Apple Silicon)
-        result = subprocess.run(
-            ["system_profiler", "SPDisplaysDataType"],
-            capture_output=True, text=True, timeout=10
-        )
-        if result.returncode == 0:
-            for line in result.stdout.split("\n"):
-                if "Chipset" in line or "GPU" in line:
-                    info.gpu_name = line.split(":")[-1].strip()
-                    break
-
-        # Estimate available memory (vm_stat)
-        result = subprocess.run(
-            ["vm_stat"],
-            capture_output=True, text=True, timeout=5
-        )
-        if result.returncode == 0:
-            page_size = 4096  # macOS default
-            free_pages = 0
-            for line in result.stdout.split("\n"):
-                if "Pages free:" in line:
-                    try:
-                        free_pages = int(line.split(":")[-1].strip().rstrip("."))
-                    except ValueError:
-                        pass
-            # Available ≈ free + some speculative (conservative: just free)
-            info.available_memory_gb = (free_pages * page_size) / (1024**3)
-
-        # Fallback if vm_stat parsing failed
-        if info.available_memory_gb < 1:
-            # Conservative: 70% of total
-            info.available_memory_gb = info.total_memory_gb * 0.70
-
-        # Apple Silicon shares memory — GPU memory = total memory
-        info.gpu_memory_gb = info.total_memory_gb
-
-        # Detect CPU cores
-        result = subprocess.run(
-            ["sysctl", "-n", "hw.ncpu"],
-            capture_output=True, text=True, timeout=5
-        )
-        if result.returncode == 0:
-            info.cpu_cores = int(result.stdout.strip())
-
-    except Exception as e:
-        logger.warning(f"Apple Silicon detection failed: {e}")
-        # Fallback
-        info.total_memory_gb = 16.0
-        info.available_memory_gb = 12.0
-        info.detection_method = "fallback"
-
-    return info
-
-
-def _detect_linux() -> HardwareInfo:
-    """Detect Linux system with optional NVIDIA GPU."""
-    info = HardwareInfo(
-        total_memory_gb=0,
-        available_memory_gb=0,
-        detection_method="proc",
-    )
-
-    try:
-        # Read /proc/meminfo
-        with open("/proc/meminfo", "r") as f:
-            meminfo = f.read()
-
-        for line in meminfo.split("\n"):
-            if line.startswith("MemTotal:"):
-                kb = int(line.split()[1])
-                info.total_memory_gb = kb / (1024 * 1024)
-            elif line.startswith("MemAvailable:"):
-                kb = int(line.split()[1])
-                info.available_memory_gb = kb / (1024 * 1024)
-
-        # CPU cores
-        info.cpu_cores = os.cpu_count() or 1
-
-        # Check for NVIDIA GPU
-        try:
-            result = subprocess.run(
-                ["nvidia-smi", "--query-gpu=name,memory.total,memory.free",
-                 "--format=csv,noheader,nounits"],
-                capture_output=True, text=True, timeout=10
-            )
-            if result.returncode == 0 and result.stdout.strip():
-                lines = result.stdout.strip().split("\n")
-                if lines:
-                    parts = lines[0].split(", ")
-                    if len(parts) >= 3:
-                        info.gpu_name = parts[0].strip()
-                        info.gpu_memory_gb = float(parts[1]) / 1024  # MB to GB
-                        gpu_free = float(parts[2]) / 1024
-                        # Use GPU free for VRAM-based selection
-                        info.available_memory_gb = max(info.available_memory_gb, gpu_free)
-                        info.detection_method = "nvidia-smi"
-        except (FileNotFoundError, subprocess.TimeoutExpired):
-            pass  # No NVIDIA GPU
-
-    except Exception as e:
-        logger.warning(f"Linux detection failed: {e}")
-        info.total_memory_gb = 16.0
-        info.available_memory_gb = 12.0
-        info.detection_method = "fallback"
-
-    return info
-
-
-def _detect_generic(system: str) -> HardwareInfo:
-    """Fallback detection for unknown systems."""
-    import psutil
-    mem = psutil.virtual_memory()
-    return HardwareInfo(
-        total_memory_gb=mem.total / (1024**3),
-        available_memory_gb=mem.available / (1024**3),
-        cpu_cores=os.cpu_count() or 1,
-        detection_method="psutil",
-    )
-
-
-# ── KV Cache Memory Estimation ───────────────────────────────────────────────
-
-def estimate_kv_cache_gb(
-    context_length: int,
-    num_layers: int = 48,
-    num_kv_heads: int = 8,
-    head_dim: int = 128,
-    bits_per_channel: float = 3.5,
-) -> float:
-    """Estimate KV cache memory for given parameters.
-
-    Formula: 2 (K+V) × layers × kv_heads × head_dim × context_length × bits/8
-    """
-    bytes_per_element = bits_per_channel / 8.0
-    total_bytes = 2 * num_layers * num_kv_heads * head_dim * context_length * bytes_per_element
-    return total_bytes / (1024**3)
-
-
-def estimate_model_memory_gb(model_size_gb: float, quant_type: str = "q4_k_m") -> float:
-    """Estimate model weights memory. Returns loaded size in GB.
-
-    This is a rough estimate — actual depends on exact quant format.
-    """
-    # Common quant ratios (vs fp16)
-    quant_multipliers = {
-        "f16": 1.0,
-        "q8_0": 0.5,
-        "q6_k": 0.42,
-        "q5_k_m": 0.37,
-        "q4_k_m": 0.32,
-        "q3_k_m": 0.27,
-        "q2_k": 0.22,
-    }
-    # model_size_gb is already quantized size
-    return model_size_gb
-
-
-# ── Selection Logic ───────────────────────────────────────────────────────────
-
-@dataclass
-class QuantSelection:
-    """Result of quantization level selection."""
-    level: QuantLevel
-    hardware: HardwareInfo
-    reasoning: str
-    total_required_gb: float
-    available_gb: float
-    headroom_gb: float
-    env_vars: dict = field(default_factory=dict)
-    server_flags: dict = field(default_factory=dict)
-    warnings: list = field(default_factory=list)
-
-
-def select_quant_level(
-    model_size_gb: float = 14.0,
-    context_length: int = 32768,
-    num_layers: int = 48,
-    num_kv_heads: int = 8,
-    head_dim: int = 128,
-    preferred_level: Optional[str] = None,
-    force_cpu: bool = False,
-) -> QuantSelection:
-    """Select the best quantization level for available hardware.
-
-    Args:
-        model_size_gb: Size of the model weights in GB
-        context_length: Target context length
-        num_layers: Number of transformer layers
-        num_kv_heads: Number of KV attention heads
-        head_dim: Dimension per attention head
-        preferred_level: Force a specific level (still checks if it fits)
-        force_cpu: If True, ignore GPU memory
-
-    Returns:
-        QuantSelection with the chosen level and reasoning
-    """
-    hw = detect_hardware()
-
-    if force_cpu:
-        hw.gpu_memory_gb = None
-        hw.gpu_name = None
-
-    # Use the most restrictive memory constraint
-    # For Apple Silicon: unified memory, use total
-    # For NVIDIA: use GPU VRAM
-    # For CPU-only: use system RAM
-    if hw.gpu_memory_gb and hw.gpu_name:
-        memory_pool_gb = hw.gpu_memory_gb
-        memory_label = f"{hw.gpu_name} {hw.gpu_memory_gb:.0f}GB VRAM"
-    elif hw.is_apple_silicon:
-        memory_pool_gb = hw.total_memory_gb
-        memory_label = f"{hw.chip_name or 'Apple Silicon'} {hw.total_memory_gb:.0f}GB unified"
-    else:
-        memory_pool_gb = hw.total_memory_gb
-        memory_label = f"{hw.cpu_cores}c CPU {hw.total_memory_gb:.0f}GB RAM"
-
-    model_mem = estimate_model_memory_gb(model_size_gb)
-
-    # Try levels from best to most compressed
-    chosen = None
-    for level in QUANT_LEVELS:
-        if preferred_level and level.name != preferred_level:
-            continue
-
-        kv_mem = estimate_kv_cache_gb(
-            context_length, num_layers, num_kv_heads, head_dim,
-            level.bits_per_channel
-        )
-        total_required = model_mem + kv_mem
-        headroom = memory_pool_gb - total_required
-
-        if headroom >= level.min_memory_headroom_gb:
-            chosen = level
-            break
-
-        if preferred_level and level.name == preferred_level:
-            # User forced this level but it doesn't fit
-            chosen = level
-            break
-
-    if chosen is None:
-        # Nothing fits — pick the most aggressive compression
-        chosen = QUANT_LEVELS[-1]
-        logger.warning(f"No quant level fits in {memory_pool_gb:.1f}GB. Using {chosen.name}.")
-
-    # Calculate final numbers
-    kv_mem = estimate_kv_cache_gb(
-        context_length, num_layers, num_kv_heads, head_dim,
-        chosen.bits_per_channel
-    )
-    total_required = model_mem + kv_mem
-    headroom = memory_pool_gb - total_required
-
-    # Build reasoning
-    reasoning_parts = [
-        f"{memory_label}:",
-        f"{chosen.name} ({chosen.quality_label}, {chosen.bits_per_channel:.1f}b/ch,",
-        f"{chosen.compression_ratio:.1f}x compression)",
-        f"fits {model_mem:.1f}GB model + {kv_mem:.1f}GB KV cache",
-        f"@ {context_length}K context = {total_required:.1f}GB / {memory_pool_gb:.0f}GB",
-        f"({headroom:.1f}GB headroom)"
-    ]
-    reasoning = " ".join(reasoning_parts)
-
-    # Build environment variables for llama.cpp
-    env_vars = {
-        "TURBO_LAYER_ADAPTIVE": str(chosen.layer_adaptive),
-    }
-
-    # Build server flags
-    server_flags = {
-        "-ctk": chosen.kv_type,
-        "-ctv": chosen.kv_type,
-        "-c": str(context_length),
-    }
-
-    # Warnings
-    warnings = []
-    if headroom < 2.0:
-        warnings.append(
-            f"Low headroom ({headroom:.1f}GB). Consider reducing context length or model size."
-        )
-    if headroom < 0:
-        warnings.append(
-            f"OVERCOMMITTED: needs {total_required:.1f}GB but only {memory_pool_gb:.0f}GB available. "
-            f"Inference may fail or swap heavily."
-        )
-
-    selection = QuantSelection(
-        level=chosen,
-        hardware=hw,
-        reasoning=reasoning,
-        total_required_gb=total_required,
-        available_gb=memory_pool_gb,
-        headroom_gb=headroom,
-        env_vars=env_vars,
-        server_flags=server_flags,
-        warnings=warnings,
-    )
-
-    logger.info(f"Quant selection: {reasoning}")
-    for w in warnings:
-        logger.warning(w)
-
-    return selection
-
-
-# ── CLI ───────────────────────────────────────────────────────────────────────
-
-def main():
-    """CLI entry point for quant level selection."""
-    import argparse
-    import json
-
-    parser = argparse.ArgumentParser(
-        description="Auto-select TurboQuant compression level based on available hardware"
-    )
-    parser.add_argument("--model-size", type=float, default=14.0,
-                        help="Model size in GB (default: 14.0)")
-    parser.add_argument("--context", type=int, default=32768,
-                        help="Target context length (default: 32768)")
-    parser.add_argument("--layers", type=int, default=48,
-                        help="Number of transformer layers (default: 48)")
-    parser.add_argument("--kv-heads", type=int, default=8,
-                        help="Number of KV attention heads (default: 8)")
-    parser.add_argument("--head-dim", type=int, default=128,
-                        help="Dimension per attention head (default: 128)")
-    parser.add_argument("--prefer", type=str, default=None,
-                        choices=[l.name for l in QUANT_LEVELS],
-                        help="Prefer a specific quant level")
-    parser.add_argument("--force-cpu", action="store_true",
-                        help="Ignore GPU, use CPU memory only")
-    parser.add_argument("--json", action="store_true",
-                        help="JSON output for automation")
-    parser.add_argument("--detect-only", action="store_true",
-                        help="Only detect hardware, don't select")
-    args = parser.parse_args()
-
-    logging.basicConfig(level=logging.INFO, format="%(message)s")
-
-    if args.detect_only:
-        hw = detect_hardware()
-        if args.json:
-            print(json.dumps(hw.__dict__, default=str, indent=2))
-        else:
-            print(f"Total memory:    {hw.total_memory_gb:.1f} GB")
-            print(f"Available:       {hw.available_memory_gb:.1f} GB")
-            if hw.gpu_memory_gb:
-                print(f"GPU memory:      {hw.gpu_memory_gb:.1f} GB")
-            if hw.gpu_name:
-                print(f"GPU:             {hw.gpu_name}")
-            if hw.is_apple_silicon:
-                print(f"Chip:            {hw.chip_name or 'Apple Silicon'}")
-            print(f"CPU cores:       {hw.cpu_cores}")
-            print(f"Detection:       {hw.detection_method}")
-        return
-
-    selection = select_quant_level(
-        model_size_gb=args.model_size,
-        context_length=args.context,
-        num_layers=args.layers,
-        num_kv_heads=args.kv_heads,
-        head_dim=args.head_dim,
-        preferred_level=args.prefer,
-        force_cpu=args.force_cpu,
-    )
-
-    if args.json:
-        result = {
-            "level": selection.level.name,
-            "bits_per_channel": selection.level.bits_per_channel,
-            "compression_ratio": selection.level.compression_ratio,
-            "quality": selection.level.quality_label,
-            "reasoning": selection.reasoning,
-            "total_required_gb": round(selection.total_required_gb, 2),
-            "available_gb": round(selection.available_gb, 1),
-            "headroom_gb": round(selection.headroom_gb, 2),
-            "env_vars": selection.env_vars,
-            "server_flags": selection.server_flags,
-            "warnings": selection.warnings,
-            "hardware": {
-                "total_memory_gb": round(selection.hardware.total_memory_gb, 1),
-                "gpu_name": selection.hardware.gpu_name,
-                "is_apple_silicon": selection.hardware.is_apple_silicon,
-                "chip_name": selection.hardware.chip_name,
-                "cpu_cores": selection.hardware.cpu_cores,
-            },
-        }
-        print(json.dumps(result, indent=2))
-    else:
-        print(f"Selected: {selection.level.name} ({selection.level.quality_label})")
-        print(f"  {selection.reasoning}")
-        print()
-        print(f"Environment variables:")
-        for k, v in selection.env_vars.items():
-            print(f"  export {k}={v}")
-        print()
-        print(f"Server flags:")
-        for k, v in selection.server_flags.items():
-            print(f"  {k} {v}")
-        if selection.warnings:
-            print()
-            for w in selection.warnings:
-                print(f"  WARNING: {w}")
-
-
-if __name__ == "__main__":
-    main()

tests/conftest.py

@@ -1,3 +0,0 @@
-"""Pytest configuration for turboquant."""
-import sys, os
-sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))

tests/test_hardware_optimizer.py

@@ -1,21 +0,0 @@
-#!/usr/bin/env python3
-"""Tests for hardware_optimizer compatibility shim."""
-
-import os
-import sys
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
-
-from evolution import hardware_optimizer, quant_selector
-
-
-def test_hardware_optimizer_reexports_quant_selector_api():
-    assert hardware_optimizer.select_quant_level is quant_selector.select_quant_level
-    assert hardware_optimizer.detect_hardware is quant_selector.detect_hardware
-    assert hardware_optimizer.HardwareInfo is quant_selector.HardwareInfo
-    assert hardware_optimizer.QuantSelection is quant_selector.QuantSelection
-
-
-def test_hardware_optimizer_exports_quant_level_definitions():
-    assert hardware_optimizer.QUANT_LEVELS is quant_selector.QUANT_LEVELS
-    assert hardware_optimizer.QuantLevel is quant_selector.QuantLevel

tests/test_quant_selector.py

@@ -1,163 +0,0 @@
-#!/usr/bin/env python3
-"""Tests for quant_selector.py"""
-
-import sys
-import os
-import pytest
-from unittest.mock import patch, MagicMock
-
-sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
-from evolution.quant_selector import (
-    QuantLevel,
-    HardwareInfo,
-    QUANT_LEVELS,
-    detect_hardware,
-    estimate_kv_cache_gb,
-    estimate_model_memory_gb,
-    select_quant_level,
-)
-
-
-class TestQuantLevels:
-    def test_levels_ordered_by_quality(self):
-        """Levels should be ordered from best quality to most aggressive."""
-        for i in range(len(QUANT_LEVELS) - 1):
-            assert QUANT_LEVELS[i].bits_per_channel > QUANT_LEVELS[i + 1].bits_per_channel
-
-    def test_all_levels_have_required_fields(self):
-        for level in QUANT_LEVELS:
-            assert level.name
-            assert level.bits_per_channel > 0
-            assert level.compression_ratio > 1
-            assert level.quality_label
-            assert level.layer_adaptive >= 0
-            assert level.kv_type
-
-
-class TestKVEstimate:
-    def test_basic_estimate(self):
-        # 48 layers, 8 heads, 128 dim, 32K context, 3.5 bits
-        kv_gb = estimate_kv_cache_gb(32768, 48, 8, 128, 3.5)
-        assert kv_gb > 0
-        assert kv_gb < 10  # Should be reasonable
-
-    def test_longer_context_larger(self):
-        kv_32k = estimate_kv_cache_gb(32768, 48, 8, 128, 3.5)
-        kv_128k = estimate_kv_cache_gb(131072, 48, 8, 128, 3.5)
-        assert kv_128k > kv_32k
-
-    def test_higher_bits_larger(self):
-        kv_4b = estimate_kv_cache_gb(32768, 48, 8, 128, 4.0)
-        kv_2b = estimate_kv_cache_gb(32768, 48, 8, 128, 2.0)
-        assert kv_4b > kv_2b
-
-
-class TestHardwareDetection:
-    def test_detect_returns_info(self):
-        hw = detect_hardware()
-        assert hw.total_memory_gb > 0
-        assert hw.available_memory_gb > 0
-        assert hw.detection_method
-
-    @patch("evolution.quant_selector.platform.system", return_value="Linux")
-    @patch("builtins.open", create=True)
-    def test_linux_detection(self, mock_open, mock_system):
-        mock_open.return_value.__enter__().read.return_value = (
-            "MemTotal:       32000000 kB\n"
-            "MemAvailable:   24000000 kB\n"
-        )
-        hw = _detect_linux_fallback()
-        assert hw.total_memory_gb > 20
-
-
-def _detect_linux_fallback():
-    """Helper to test Linux detection with mocked /proc/meminfo."""
-    from evolution.quant_selector import _detect_linux
-    return _detect_linux()
-
-
-class TestSelection:
-    def test_selects_turbo4_for_large_memory(self):
-        """With plenty of memory, should pick turbo4 (best quality)."""
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=64,
-                available_memory_gb=48,
-                gpu_memory_gb=64,
-                gpu_name="Test GPU",
-                cpu_cores=16,
-                detection_method="mock",
-            )
-            sel = select_quant_level(model_size_gb=14.0, context_length=32768)
-            assert sel.level.name == "turbo4"
-            assert sel.headroom_gb > 0
-
-    def test_selects_smaller_for_tight_memory(self):
-        """With tight memory, should pick a smaller quant."""
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=16,
-                available_memory_gb=12,
-                gpu_memory_gb=16,
-                gpu_name="Test GPU",
-                cpu_cores=8,
-                detection_method="mock",
-            )
-            sel = select_quant_level(model_size_gb=14.0, context_length=131072)
-            # Should pick a smaller quant for 128K context on 16GB
-            assert sel.level.bits_per_channel <= 4.0
-
-    def test_preferred_level(self):
-        """User can force a specific level."""
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=64,
-                available_memory_gb=48,
-                cpu_cores=16,
-                detection_method="mock",
-            )
-            sel = select_quant_level(
-                model_size_gb=14.0, context_length=32768,
-                preferred_level="turbo2"
-            )
-            assert sel.level.name == "turbo2"
-
-    def test_env_vars_populated(self):
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=64,
-                available_memory_gb=48,
-                cpu_cores=16,
-                detection_method="mock",
-            )
-            sel = select_quant_level(model_size_gb=14.0, context_length=32768)
-            assert "TURBO_LAYER_ADAPTIVE" in sel.env_vars
-            assert "-ctk" in sel.server_flags
-            assert "-ctv" in sel.server_flags
-
-    def test_warnings_on_low_headroom(self):
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=18,
-                available_memory_gb=14,
-                gpu_memory_gb=18,
-                gpu_name="Test GPU",
-                cpu_cores=8,
-                detection_method="mock",
-            )
-            sel = select_quant_level(model_size_gb=16.0, context_length=65536)
-            assert len(sel.warnings) > 0
-
-    def test_reasoning_contains_key_info(self):
-        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
-            mock_hw.return_value = HardwareInfo(
-                total_memory_gb=32,
-                available_memory_gb=24,
-                is_apple_silicon=True,
-                chip_name="M4 Max",
-                cpu_cores=16,
-                detection_method="mock",
-            )
-            sel = select_quant_level(model_size_gb=14.0, context_length=32768)
-            assert "turbo4" in sel.reasoning
-            assert "M4 Max" in sel.reasoning or "32GB" in sel.reasoning

tests/test_tool_call_integration.py

@@ -1,338 +0,0 @@
-"""
-Integration test: turboquant compressed model passes hermes tool calls (issue #82).
-
-Validates that a TurboQuant-compressed model can:
-1. Parse hermes tool schemas correctly
-2. Format tool calls in OpenAI-compatible format
-3. Pass through the hermes agent conversation loop
-
-Tests are structured as contract tests -- they validate the schema/format
-compatibility without requiring a running model server. The live inference
-test is skipped by default (requires llama-server with TurboQuant model).
-
-Usage:
-    pytest tests/test_tool_call_integration.py -v
-    pytest tests/test_tool_call_integration.py -v -k live  # run live test if server available
-"""
-import json
-import os
-import pathlib
-import re
-import unittest
-
-import pytest
-
-ROOT = pathlib.Path(__file__).resolve().parents[1]
-PROFILE_PATH = ROOT / "profiles" / "hermes-profile-gemma4-turboquant.yaml"
-BENCHMARKS_DIR = ROOT / "benchmarks"
-
-
-class TestHermesProfileSchema(unittest.TestCase):
-    """Validate the hermes profile YAML has required fields for tool calling."""
-
-    @classmethod
-    def setUpClass(cls):
-        import yaml
-        cls.profile = yaml.safe_load(PROFILE_PATH.read_text())
-
-    def test_profile_has_providers(self):
-        assert "providers" in self.profile, "Profile must define providers"
-        assert "primary" in self.profile["providers"], "Must have primary provider"
-
-    def test_primary_provider_has_endpoint(self):
-        primary = self.profile["providers"]["primary"]
-        assert "endpoint" in primary, "Primary provider must have endpoint"
-        assert primary["endpoint"].startswith("http"), "Endpoint must be HTTP(S) URL"
-
-    def test_primary_provider_has_api_path(self):
-        primary = self.profile["providers"]["primary"]
-        assert "api_path" in primary, "Primary provider must have api_path"
-        assert "/chat/completions" in primary["api_path"], (
-            "api_path should be OpenAI-compatible /chat/completions"
-        )
-
-    def test_turboquant_settings_present(self):
-        primary = self.profile["providers"]["primary"]
-        assert "turboquant" in primary, "Must have turboquant config section"
-        tq = primary["turboquant"]
-        assert tq.get("enabled") is True, "TurboQuant must be enabled"
-        assert tq.get("kv_type") in ("turbo2", "turbo3", "turbo4"), (
-            "kv_type must be turbo2, turbo3, or turbo4"
-        )
-
-    def test_context_window_configured(self):
-        primary = self.profile["providers"]["primary"]
-        assert "context" in primary, "Must have context config"
-        ctx = primary["context"]
-        assert ctx.get("max_tokens", 0) >= 8192, (
-            "max_tokens should be >= 8192 for TurboQuant value proposition"
-        )
-
-
-class TestToolSchemaCompatibility(unittest.TestCase):
-    """Verify hermes tool schemas serialize to valid JSON for OpenAI tool_calls."""
-
-    SAMPLE_TOOL_SCHEMAS = [
-        {
-            "type": "function",
-            "function": {
-                "name": "read_file",
-                "description": "Read a text file with line numbers.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "path": {"type": "string", "description": "File path"},
-                        "offset": {"type": "integer", "default": 1},
-                        "limit": {"type": "integer", "default": 500},
-                    },
-                    "required": ["path"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "execute_code",
-                "description": "Run a Python script.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "code": {"type": "string", "description": "Python code"},
-                    },
-                    "required": ["code"],
-                },
-            },
-        },
-        {
-            "type": "function",
-            "function": {
-                "name": "web_search",
-                "description": "Search the web.",
-                "parameters": {
-                    "type": "object",
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_results": {"type": "integer", "default": 5},
-                    },
-                    "required": ["query"],
-                },
-            },
-        },
-    ]
-
-    def test_tool_schemas_serialize_to_json(self):
-        """Tool schemas must serialize without errors."""
-        serialized = json.dumps(self.SAMPLE_TOOL_SCHEMAS)
-        assert len(serialized) > 0
-        parsed = json.loads(serialized)
-        assert len(parsed) == len(self.SAMPLE_TOOL_SCHEMAS)
-
-    def test_tool_schemas_have_required_openai_fields(self):
-        """Each tool schema must have the fields OpenAI expects."""
-        for tool in self.SAMPLE_TOOL_SCHEMAS:
-            assert tool["type"] == "function", "Tool type must be 'function'"
-            fn = tool["function"]
-            assert "name" in fn, "Function must have name"
-            assert "description" in fn, "Function must have description"
-            assert "parameters" in fn, "Function must have parameters"
-            params = fn["parameters"]
-            assert params["type"] == "object", "Parameters type must be 'object'"
-            assert "properties" in params, "Parameters must have properties"
-
-    def test_tool_call_response_format(self):
-        """Verify tool_call response matches OpenAI format."""
-        tool_call = {
-            "id": "call_abc123",
-            "type": "function",
-            "function": {
-                "name": "read_file",
-                "arguments": json.dumps({"path": "/tmp/test.txt"}),
-            },
-        }
-        args = json.loads(tool_call["function"]["arguments"])
-        assert args["path"] == "/tmp/test.txt"
-        assert tool_call["function"]["name"] in [
-            t["function"]["name"] for t in self.SAMPLE_TOOL_SCHEMAS
-        ]
-
-    def test_tool_names_are_valid_identifiers(self):
-        """Tool names must be valid Python identifiers for hermes dispatch."""
-        for tool in self.SAMPLE_TOOL_SCHEMAS:
-            name = tool["function"]["name"]
-            assert re.match(r"^[a-zA-Z_][a-zA-Z0-9_]*$", name), (
-                f"Tool name \'{name}\' is not a valid identifier"
-            )
-
-
-class TestTurboquantServerConfig(unittest.TestCase):
-    """Validate server startup configuration matches hermes profile."""
-
-    def test_server_command_has_turboquant_flags(self):
-        """The server command in the profile must include -ctk/-ctv flags."""
-        profile_text = PROFILE_PATH.read_text()
-        assert "-ctk" in profile_text, "Profile server command must include -ctk flag"
-        assert "-ctv" in profile_text, "Profile server command must include -ctv flag"
-
-    def test_server_command_has_context_flag(self):
-        """Server command must set context size."""
-        profile_text = PROFILE_PATH.read_text()
-        assert re.search(r"-c\s+\d+", profile_text), (
-            "Server command must include -c <context_size> flag"
-        )
-
-    def test_layer_adaptive_env_var(self):
-        """Profile must set TURBO_LAYER_ADAPTIVE env var."""
-        profile_text = PROFILE_PATH.read_text()
-        assert "TURBO_LAYER_ADAPTIVE" in profile_text, (
-            "Profile must configure TURBO_LAYER_ADAPTIVE"
-        )
-
-
-class TestBenchmarkData(unittest.TestCase):
-    """Validate benchmark test prompts include tool-call test cases."""
-
-    @classmethod
-    def setUpClass(cls):
-        prompts_path = BENCHMARKS_DIR / "test_prompts.json"
-        cls.prompts = json.loads(prompts_path.read_text())
-
-    def test_has_tool_call_test_prompt(self):
-        """Benchmark prompts must include a tool-call format test."""
-        categories = [p.get("category") for p in self.prompts]
-        assert "tool_call_format" in categories, (
-            "Benchmark must include a tool_call_format test case"
-        )
-
-    def test_tool_call_prompt_expects_json(self):
-        """Tool call test prompt must expect JSON in the response."""
-        tool_prompt = next(
-            p for p in self.prompts if p.get("category") == "tool_call_format"
-        )
-        pattern = tool_prompt.get("expected_pattern", "")
-        assert "json" in pattern.lower() or "\\{" in pattern, (
-            "Tool call prompt must expect JSON-formatted response"
-        )
-
-
-@pytest.mark.skipif(
-    not os.environ.get("TURBOQUANT_SERVER_URL"),
-    reason="No TurboQuant server available (set TURBOQUANT_SERVER_URL to run)",
-)
-class TestLiveToolCallIntegration:
-    """Live integration test -- requires running llama-server with TurboQuant."""
-
-    def test_server_health(self):
-        """Server must respond to /v1/models endpoint."""
-        import requests
-        url = os.environ["TURBOQUANT_SERVER_URL"]
-        resp = requests.get(f"{url}/v1/models", timeout=10)
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "data" in data
-        assert len(data["data"]) > 0
-
-    def test_tool_call_completion(self):
-        """Model must return a valid tool_call for a read_file prompt."""
-        import requests
-        url = os.environ["TURBOQUANT_SERVER_URL"]
-        tools = [
-            {
-                "type": "function",
-                "function": {
-                    "name": "read_file",
-                    "description": "Read a file",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"path": {"type": "string"}},
-                        "required": ["path"],
-                    },
-                },
-            }
-        ]
-        resp = requests.post(
-            f"{url}/v1/chat/completions",
-            json={
-                "model": "gemma-4",
-                "messages": [
-                    {"role": "user", "content": "Read the file at /tmp/test.txt"}
-                ],
-                "tools": tools,
-                "tool_choice": "auto",
-            },
-            timeout=120,
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        choice = data["choices"][0]
-        msg = choice["message"]
-        if "tool_calls" in msg and msg["tool_calls"]:
-            tc = msg["tool_calls"][0]
-            assert tc["type"] == "function"
-            assert tc["function"]["name"] == "read_file"
-            args = json.loads(tc["function"]["arguments"])
-            assert "path" in args
-        else:
-            assert len(msg.get("content", "")) > 0
-
-    def test_tool_call_with_multiple_tools(self):
-        """Model must handle multiple available tools."""
-        import requests
-        url = os.environ["TURBOQUANT_SERVER_URL"]
-        tools = [
-            {
-                "type": "function",
-                "function": {
-                    "name": "read_file",
-                    "description": "Read a file",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"path": {"type": "string"}},
-                        "required": ["path"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "web_search",
-                    "description": "Search the web",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"query": {"type": "string"}},
-                        "required": ["query"],
-                    },
-                },
-            },
-            {
-                "type": "function",
-                "function": {
-                    "name": "execute_code",
-                    "description": "Run Python code",
-                    "parameters": {
-                        "type": "object",
-                        "properties": {"code": {"type": "string"}},
-                        "required": ["code"],
-                    },
-                },
-            },
-        ]
-        resp = requests.post(
-            f"{url}/v1/chat/completions",
-            json={
-                "model": "gemma-4",
-                "messages": [
-                    {"role": "user", "content": "Search the web for 'bitcoin price'"}
-                ],
-                "tools": tools,
-                "tool_choice": "auto",
-            },
-            timeout=120,
-        )
-        assert resp.status_code == 200
-        data = resp.json()
-        assert "choices" in data
-        assert len(data["choices"]) > 0
-
-
-if __name__ == "__main__":
-    unittest.main()

tests/test_tool_calling.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+"""
+Unit tests for benchmarks/test_tool_calling.py
+
+Tests the validation logic and report generation without
+requiring a live model backend.
+"""
+
+import json
+import sys
+from pathlib import Path
+
+import pytest
+
+sys.path.insert(0, str(Path(__file__).parent.parent / "benchmarks"))
+import test_tool_calling as tc
+
+
+# ── JSON Extraction ───────────────────────────────────────────────────
+
+class TestExtractJson:
+    def test_direct_json(self):
+        obj = tc._extract_json('{"name": "read_file", "arguments": {"path": "/etc/hostname"}}')
+        assert obj["name"] == "read_file"
+
+    def test_json_in_code_block(self):
+        text = 'Here is the call:\n```json\n{"name": "terminal", "arguments": {"command": "ls"}}\n```'
+        obj = tc._extract_json(text)
+        assert obj["name"] == "terminal"
+
+    def test_json_without_lang(self):
+        text = '```\n{"name": "web_search", "arguments": {"query": "test"}}\n```'
+        obj = tc._extract_json(text)
+        assert obj["name"] == "web_search"
+
+    def test_no_json(self):
+        obj = tc._extract_json("I can't help with that.")
+        assert obj is None
+
+    def test_bare_json_object(self):
+        text = 'Sure, here: {"name": "read_file", "arguments": {"path": "/tmp/x"}} for you.'
+        obj = tc._extract_json(text)
+        assert obj is not None
+        assert obj["name"] == "read_file"
+
+
+# ── Tool Call Validation ──────────────────────────────────────────────
+
+class TestToolCallValidation:
+    def test_exact_match(self):
+        resp = '{"name": "read_file", "arguments": {"path": "/etc/hostname"}}'
+        result = tc._has_json_tool_call(resp, "read_file", {"path": "/etc/hostname"})
+        assert result["passed"] is True
+
+    def test_wrong_tool_name(self):
+        resp = '{"name": "write_file", "arguments": {"path": "/etc/hostname"}}'
+        result = tc._has_json_tool_call(resp, "read_file", {"path": "/etc/hostname"})
+        assert result["passed"] is False
+        assert "wrong tool name" in result["reason"]
+
+    def test_missing_argument(self):
+        resp = '{"name": "read_file", "arguments": {}}'
+        result = tc._has_json_tool_call(resp, "read_file", {"path": "/etc/hostname"})
+        assert result["passed"] is False
+        assert "missing argument" in result["reason"]
+
+    def test_wrong_argument_value(self):
+        resp = '{"name": "read_file", "arguments": {"path": "/etc/passwd"}}'
+        result = tc._has_json_tool_call(resp, "read_file", {"path": "/etc/hostname"})
+        assert result["passed"] is False
+        assert "argument mismatch" in result["reason"]
+
+    def test_no_json_response(self):
+        result = tc._has_json_tool_call("Sorry, I can't do that.", "read_file", {"path": "/etc/hostname"})
+        assert result["passed"] is False
+        assert "no JSON" in result["reason"]
+
+    def test_nested_function_format(self):
+        resp = '{"function": {"name": "terminal", "arguments": {"command": "echo hello"}}}'
+        result = tc._has_json_tool_call(resp, "terminal", {"command": "echo hello"})
+        assert result["passed"] is True
+
+
+# ── Nested Schema Validation ──────────────────────────────────────────
+
+class TestNestedSchemaValidation:
+    def test_valid_nested(self):
+        resp = json.dumps({
+            "name": "deploy_service",
+            "arguments": {
+                "name": "api-gateway",
+                "replicas": 3,
+                "env": {"PORT": 8080, "NODE_ENV": "production"},
+                "resources": {"cpu": "500m", "memory": "256Mi"}
+            }
+        })
+        result = tc._has_nested_tool_call(resp)
+        assert result["passed"] is True
+
+    def test_missing_nested_key(self):
+        resp = '{"name": "deploy_service", "arguments": {"name": "api-gateway", "replicas": 3}}'
+        result = tc._has_nested_tool_call(resp)
+        assert result["passed"] is False
+        assert "missing nested key" in result["reason"]
+
+    def test_wrong_type(self):
+        resp = '{"name": "deploy_service", "arguments": {"name": "api-gateway", "replicas": "three", "env": {}, "resources": {}}}'
+        result = tc._has_nested_tool_call(resp)
+        assert result["passed"] is False
+        assert "should be int" in result["reason"]
+
+    def test_missing_env_port(self):
+        resp = json.dumps({
+            "name": "deploy_service",
+            "arguments": {"name": "api", "replicas": 1, "env": {"NODE_ENV": "dev"}, "resources": {}}
+        })
+        result = tc._has_nested_tool_call(resp)
+        assert result["passed"] is False
+        assert "PORT" in result["reason"]
+
+
+# ── Markdown Report Generation ────────────────────────────────────────
+
+class TestMarkdownReport:
+    def test_report_structure(self):
+        results = {
+            "model": "test-model",
+            "backend": "ollama",
+            "url": "http://localhost:11434",
+            "timestamp": "2026-04-15T00:00:00Z",
+            "tests": [
+                {"id": "t1", "category": "Simple", "description": "Test 1",
+                 "passed": True, "reason": "ok", "response": "{}", "latency_s": 1.0, "tokens": 10},
+                {"id": "t2", "category": "Complex", "description": "Test 2",
+                 "passed": False, "reason": "wrong name", "response": "oops", "latency_s": 2.0, "tokens": 20},
+            ],
+            "summary": {"total": 2, "passed": 1, "failed": 1, "errors": 0},
+        }
+        md = tc.to_markdown(results)
+        assert "test-model" in md
+        assert "1/2 passed" in md
+        assert "PASS" in md
+        assert "FAIL" in md
+        assert "Failure Analysis" in md
+
+    def test_perfect_score(self):
+        results = {
+            "model": "perfect", "backend": "ollama", "url": "http://x",
+            "timestamp": "2026-01-01T00:00:00Z",
+            "tests": [
+                {"id": "t1", "category": "C", "description": "D",
+                 "passed": True, "reason": "ok", "response": "{}", "latency_s": 1, "tokens": 5},
+            ],
+            "summary": {"total": 1, "passed": 1, "failed": 0, "errors": 0},
+        }
+        md = tc.to_markdown(results)
+        assert "FULLY VIABLE" in md
+
+    def test_all_failed(self):
+        results = {
+            "model": "bad", "backend": "ollama", "url": "http://x",
+            "timestamp": "2026-01-01T00:00:00Z",
+            "tests": [
+                {"id": "t1", "category": "C", "description": "D",
+                 "passed": False, "reason": "broken", "response": "nope", "latency_s": 1, "tokens": 0},
+            ],
+            "summary": {"total": 1, "passed": 0, "failed": 1, "errors": 0},
+        }
+        md = tc.to_markdown(results)
+        assert "NOT VIABLE" in md
+
+
+# ── Test Definitions ──────────────────────────────────────────────────
+
+class TestTestDefinitions:
+    def test_all_tests_have_validators(self):
+        for test in tc.TESTS:
+            assert callable(test["validate"]), f"{test['id']} missing validate"
+            assert "id" in test
+            assert "category" in test
+            assert "prompt" in test
+
+    def test_five_test_categories(self):
+        categories = {t["category"] for t in tc.TESTS}
+        assert len(categories) >= 4, f"Expected 4+ categories, got {categories}"
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v"])