docs: replace stale raw-IP forge link with canonical domain (#46)

Replace http://143.198.27.163:3000 with
https://forge.alexanderwhitestone.com in docs/PROJECT_STATUS.md.

No other raw-IP references found in repo.

Closes #46.

benchmarks/bonsai-tool-calling.md

@@ -1,50 +0,0 @@
-# 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

@@ -1,435 +0,0 @@
-#!/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()

docs/PROJECT_STATUS.md

@@ -385,7 +385,7 @@ Step 7: If pass → production. If fail → drop to turbo3 or adjust per-layer p
 
 ---
 
-*Repo: http://143.198.27.163:3000/Timmy_Foundation/turboquant*
+*Repo: https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant*
 *Build: /tmp/llama-cpp-turboquant/build/bin/ (all binaries)*
 *Branch: feature/turboquant-kv-cache*
 

evolution/quant_selector.py

@@ -0,0 +1,548 @@
+"""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

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

tests/test_quant_selector.py

@@ -0,0 +1,163 @@
+#!/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

@@ -0,0 +1,338 @@
+"""
+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

@@ -1,189 +0,0 @@
-#!/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"])