feat: add auto-start server fixture (#118)

- turboquant_server_url fixture: auto-starts llama-server if no URL provided
- Finds binary in standard locations or PATH
- Finds GGUF model in standard locations
- Configurable via env vars (port, kv_type, ctx_size, timeout)
- Skips gracefully if binary or model not found
- turboquant_model_name fixture for model discovery

.gitignore

@@ -0,0 +1,3 @@
+build/
+*.pyc
+__pycache__/

CMakeLists.txt

@@ -0,0 +1,36 @@
+cmake_minimum_required(VERSION 3.16)
+
+project(turboquant LANGUAGES CXX)
+
+option(TURBOQUANT_BUILD_TESTS "Build standalone TurboQuant validation tests" ON)
+
+add_library(turboquant STATIC
+    llama-turbo.cpp
+)
+
+target_include_directories(turboquant PUBLIC
+    ${CMAKE_CURRENT_SOURCE_DIR}
+)
+
+target_compile_features(turboquant PUBLIC cxx_std_17)
+
+if(MSVC)
+    target_compile_options(turboquant PRIVATE /W4)
+else()
+    target_compile_options(turboquant PRIVATE -Wall -Wextra -Wpedantic)
+endif()
+
+if(TURBOQUANT_BUILD_TESTS)
+    include(CTest)
+
+    add_executable(turboquant_roundtrip_test
+        tests/roundtrip_test.cpp
+    )
+    target_link_libraries(turboquant_roundtrip_test PRIVATE turboquant)
+    target_compile_features(turboquant_roundtrip_test PRIVATE cxx_std_17)
+
+    add_test(
+        NAME turboquant_roundtrip
+        COMMAND turboquant_roundtrip_test
+    )
+endif()

GENOME.md

@@ -1,323 +0,0 @@
-# GENOME.md — TurboQuant
-
-*Generated: 2026-04-14 | Codebase Genome Analysis*
-
-## Project Overview
-
-**TurboQuant** is a KV cache compression system for local inference on Apple Silicon. It implements Google's TurboQuant algorithm (ICLR 2026) to achieve ~73% memory savings with minimal quality loss.
-
-### Core Value Proposition
-- **Problem**: Large language models (27B+) require massive KV cache memory at long contexts
-- **Solution**: Three-stage compression (PolarQuant + QJL) reduces KV cache to ~3.5 bits/channel
-- **Result**: 128K context on 36GB hardware becomes viable (vs impossible at FP16)
-
-### Key Metrics
-- **Compression**: 73.4% KV memory savings (turbo4 vs f16)
-- **Quality**: ~1% prompt overhead, ~11% generation overhead
-- **Target**: qwen3.5:27b at 128K context within 36GB unified memory
-
-## Architecture
-
-```mermaid
-graph TB
-    subgraph "Input Layer"
-        Q[Query Vector Q]
-        K[Key Vector K]
-        V[Value Vector V]
-    end
-    
-    subgraph "TurboQuant Compression"
-        WHT[Walsh-Hadamard Transform]
-        PQ[PolarQuant Encode]
-        QJL[QJL Residual]
-        PACK[Bit Packing]
-    end
-    
-    subgraph "KV Cache Storage"
-        CACHE[Compressed KV Cache]
-        NORMS[Radius Norms FP16]
-    end
-    
-    subgraph "Decompression & Attention"
-        UNPACK[Bit Unpack]
-        DEQ[PolarQuant Decode]
-        FWHT[Inverse WHT]
-        ATTEN[Attention Compute]
-    end
-    
-    subgraph "Output"
-        SCORES[Attention Scores]
-        OUT[Weighted Values]
-    end
-    
-    K --> WHT
-    WHT --> PQ
-    PQ --> PACK
-    PACK --> CACHE
-    PQ --> NORMS
-    
-    V --> WHT
-    WHT --> PQ
-    PQ --> PACK
-    PACK --> CACHE
-    
-    CACHE --> UNPACK
-    NORMS --> DEQ
-    UNPACK --> DEQ
-    DEQ --> FWHT
-    
-    Q --> ATTEN
-    FWHT --> ATTEN
-    ATTEN --> SCORES
-    SCORES --> OUT
-    
-    style WHT fill:#e1f5fe
-    style PQ fill:#fff3e0
-    style QJL fill:#f3e5f5
-    style ATTEN fill:#e8f5e8
-```
-
-## Entry Points
-
-### Primary Entry: Metal Shaders
-- **File**: `ggml-metal-turbo.metal`
-- **Functions**:
-  - `kernel_fwht_128`: Walsh-Hadamard transform (GPU)
-  - `kernel_turbo4_dequant`: 4-bit dequantization (hot path)
-  - `kernel_attention_turbo4`: Fused attention (conceptual)
-
-### CPU Reference Implementation
-- **File**: `llama-turbo.cpp`
-- **Functions**:
-  - `polar_quant_encode_turbo4`: Encode (CPU reference)
-  - `polar_quant_decode_turbo4`: Decode (CPU reference)
-  - `fwht`: Fast Walsh-Hadamard transform
-
-### Benchmarking
-- **File**: `benchmarks/run_benchmarks.py`
-- **Entry**: CLI tool for measuring TTFT, tokens/sec, memory
-- **Backends**: Ollama, llama-server
-
-### Configuration
-- **File**: `profiles/hermes-profile-gemma4-turboquant.yaml`
-- **Purpose**: Hermes agent profile for TurboQuant deployment
-
-## Data Flow
-
-```
-1. Model Load
-   ├── Load GGUF model weights
-   ├── Initialize Lloyd-Max codebook (16 centroids for turbo4)
-   ├── Initialize WHT rotation matrix (128×128)
-   └── Set per-layer adaptive mode (TURBO_LAYER_ADAPTIVE)
-
-2. Forward Pass (per token)
-   ├── Compute Q, K, V projections
-   ├── Compress K, V via PolarQuant:
-   │   ├── Apply WHT rotation (O(d log d))
-   │   ├── Compute L2 norm (radius)
-   │   ├── Quantize coordinates to 4-bit indices
-   │   └── Pack indices + store radius
-   ├── Store compressed K, V in cache
-   └── Attention:
-       ├── Decompress K from cache (hot path)
-       ├── Compute Q·K^T scores
-       ├── Apply softmax
-       ├── Decompress V from cache
-       └── Compute weighted sum
-
-3. Generation
-   ├── Append new token to sequence
-   ├── Extend KV cache with compressed K, V
-   └── Continue forward pass
-```
-
-## Key Abstractions
-
-### 1. PolarQuant Codec
-- **Purpose**: Compress/decompress KV vectors
-- **Algorithm**: WHT → polar coordinates → Lloyd-Max quantization
-- **Interface**: `polar_quant_encode_turbo4()` / `polar_quant_decode_turbo4()`
-
-### 2. Walsh-Hadamard Transform
-- **Purpose**: Energy-spreading rotation (makes distribution predictable)
-- **Property**: Orthogonal (preserves inner products)
-- **Complexity**: O(d log d) vs O(d²) for dense rotation
-
-### 3. Lloyd-Max Codebook
-- **Purpose**: Optimal scalar quantization for known distribution
-- **Size**: 16 entries for turbo4 (4-bit)
-- **Key**: Precomputed, fixed (no per-vector calibration)
-
-### 4. Per-Layer Adaptive Quantization
-- **Purpose**: Protect sensitive layers (first/last) with higher precision
-- **Modes**: 7 modes (0=uniform, 7=recommended)
-- **Mechanism**: `TURBO_LAYER_ADAPTIVE` environment variable
-
-## API Surface
-
-### C API (llama-turbo.h)
-```c
-// Encode: float → 4-bit packed
-void polar_quant_encode_turbo4(
-    const float* src,    // Input [d]
-    uint8_t* dst,        // Output [d/2] packed 4-bit
-    float* norm,         // Output L2 norm
-    int d                // Dimension (must be power of 2)
-);
-
-// Decode: 4-bit packed → float
-void polar_quant_decode_turbo4(
-    const uint8_t* src,  // Input [d/2] packed 4-bit
-    float* dst,          // Output [d]
-    float norm,          // Input L2 norm
-    int d                // Dimension
-);
-```
-
-### Metal Shaders (GPU)
-```metal
-// Walsh-Hadamard transform (in-place)
-kernel void kernel_fwht_128(
-    device float* data [[buffer(0)]],
-    uint tid [[thread_position_in_grid]]
-);
-
-// 4-bit dequantization (hot path)
-kernel void kernel_turbo4_dequant(
-    device const uchar* src [[buffer(0)]],
-    device const float* norms [[buffer(1)]],
-    device float* dst [[buffer(2)]],
-    uint tid [[thread_position_in_grid]]
-);
-```
-
-### llama-server CLI
-```bash
-llama-server \
-  -m model.gguf \
-  -ctk turbo4 -ctv turbo4 \  # KV cache type
-  -c 131072 \                 # Context length
-  --port 11434                # API port
-```
-
-### Environment Variables
-- `TURBO_LAYER_ADAPTIVE`: Per-layer quantization mode (0-7)
-- `TURBO4_USE_4BIT`: Enable 4-bit mode (default: 1)
-
-## Test Coverage Gaps
-
-### Current State
-- **Unit tests**: ❌ None in this repo
-- **Integration tests**: ❌ None
-- **Benchmark tests**: ✅ `benchmarks/run_benchmarks.py`
-- **Perplexity tests**: ⚠️ Corpus exists (`corpora/wiki.test.raw`) but no runner
-
-### Critical Missing Tests
-1. **Encode/Decode Roundtrip**: Verify `decode(encode(x)) ≈ x`
-2. **Inner Product Preservation**: Verify `Q·K ≈ Q·dequant(quant(K))`
-3. **WHT Orthogonality**: Verify `WHT^T · WHT = I`
-4. **Codebook Correctness**: Verify centroids match Lloyd-Max for N(0, 1/128)
-5. **Metal vs CPU Parity**: Verify GPU and CPU produce identical results
-6. **Per-Layer Adaptive**: Verify sensitive layers use higher precision
-7. **Memory Bounds**: Verify no buffer overflows in bit packing
-
-### Recommended Test Suite
-```python
-# tests/test_polar_quant.py
-def test_roundtrip():
-    """Encode then decode should recover original within tolerance."""
-    
-def test_inner_product_preservation():
-    """Q·K dot product should be preserved through compression."""
-    
-def test_wht_orthogonality():
-    """WHT matrix should be orthogonal."""
-    
-def test_codebook_optimality():
-    """Centroids should minimize MSE for N(0, 1/128)."""
-```
-
-## Security Considerations
-
-### 1. Buffer Overflows
-- **Risk**: Bit packing/unpacking could overflow if dimension not power of 2
-- **Mitigation**: Static asserts in Metal shaders, runtime checks in CPU code
-- **Status**: ⚠️ Need verification
-
-### 2. Numerical Stability
-- **Risk**: Division by zero in `1.0 / (norm + 1e-9)`
-- **Mitigation**: Epsilon guard present
-- **Status**: ✅ Handled
-
-### 3. Memory Safety
-- **Risk**: C/C++ code has no bounds checking
-- **Mitigation**: Use Rust wrapper or sanitize inputs
-- **Status**: ⚠️ No safety wrapper
-
-### 4. Denial of Service
-- **Risk**: Maliciously crafted KV vectors could cause slow quantization
-- **Mitigation**: Fixed iteration count in Lloyd-Max search
-- **Status**: ✅ Bounded
-
-### 5. Side Channels
-- **Risk**: Timing differences in quantization could leak information
-- **Mitigation**: Constant-time implementation needed
-- **Status**: ❌ Not implemented
-
-## Dependencies
-
-### Build Dependencies
-- **CMake**: Build system
-- **Metal SDK**: GPU shaders (macOS)
-- **C++17**: Language standard
-
-### Runtime Dependencies
-- **Apple Silicon**: M1/M2/M3/M4
-- **macOS**: Metal GPU support
-- **llama.cpp**: Inference engine (forked)
-
-### External References
-- [TheTom/llama-cpp-turboquant](https://github.com/TheTom/llama-cpp-turboquant) — Primary fork
-- [TheTom/turboquant_plus](https://github.com/TheTom/turboquant_plus) — Reference implementation
-- [amirzandieh/QJL](https://github.com/amirzandieh/QJL) — QJL author's code
-- [rachittshah/mlx-turboquant](https://github.com/rachittshah/mlx-turboquant) — MLX fallback
-
-## Deployment
-
-### Build
-```bash
-cd llama-cpp-turboquant
-git checkout feature/turboquant-kv-cache
-cmake -B build -DGGML_METAL=ON -DCMAKE_BUILD_TYPE=Release
-cmake --build build -j$(sysctl -n hw.ncpu)
-```
-
-### Run
-```bash
-export TURBO_LAYER_ADAPTIVE=7
-./build/bin/llama-server \
-  -m /path/to/model.gguf \
-  --port 11434 \
-  -ctk turbo4 -ctv turbo4 \
-  -c 131072
-```
-
-### Validate
-```bash
-curl http://localhost:11434/v1/chat/completions \
-  -H "Content-Type: application/json" \
-  -d '{"model":"qwen3.5","messages":[{"role":"user","content":"hello"}]}'
-```
-
-## Open Questions
-
-1. **QJL Status**: Infrastructure exists but is disabled. When will it be needed?
-2. **Upstream Landing**: When will TurboQuant be merged into llama.cpp mainline?
-3. **Quality Threshold**: What PPL delta is acceptable for production use?
-4. **Multi-GPU**: Does TurboQuant work with tensor parallelism?
-
-## Changelog
-
-- **2026-03-30**: Phase 1 complete. PolarQuant MVP verified. 73% KV savings confirmed.
-- **2026-04-14**: GENOME.md generated. Test gaps identified. Security considerations documented.

README.md

@@ -13,7 +13,7 @@ Unlock 64K-128K context on qwen3.5:27b within 32GB unified memory.
 A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
 
 ## Status
-See [issues](http://143.198.27.163:3000/Timmy_Foundation/turboquant/issues) for current progress.
+See [issues](https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant/issues) for current progress.
 
 ## Roles
 - **Strago:** Build spec author
@@ -29,4 +29,4 @@ See [issues](http://143.198.27.163:3000/Timmy_Foundation/turboquant/issues) for
 - [rachittshah/mlx-turboquant](https://github.com/rachittshah/mlx-turboquant) — MLX fallback
 
 ## Docs
-- [BUILD-SPEC.md](BUILD-SPEC.md) — Full build specification (Strago, v2.2)
+- [Project Status](docs/PROJECT_STATUS.md) — Full project status and build specification

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()

profiles/README.md

@@ -135,7 +135,5 @@ llama-server -m model.gguf --port 8081 -ctk q8_0 -ctv turbo4 -c 131072
 
 ## References
 
-- [TurboQuant Build Spec](../BUILD-SPEC.md)
-- [Phase 1 Report](../PHASE1-REPORT.md)
-- [Full Knowledge Transfer](../FULL-REPORT.md)
+- [Project Status](../docs/PROJECT_STATUS.md)
 - [llama.cpp TurboQuant Fork](https://github.com/TheTom/llama-cpp-turboquant)

tests/conftest.py

@@ -0,0 +1,85 @@
+"""Pytest configuration for turboquant."""
+import os
+import sys
+import pytest
+from pathlib import Path
+
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+
+@pytest.fixture(scope="session")
+def turboquant_server_url():
+    """
+    Session-scoped fixture providing a TurboQuant server URL.
+    
+    If TURBOQUANT_SERVER_URL is set, uses that directly.
+    Otherwise, auto-starts a llama-server with TurboQuant flags.
+    
+    Requires:
+        - llama-server binary (in PATH or standard location)
+        - GGUF model file (in TURBOQUANT_MODEL_DIR or standard locations)
+    
+    Skips if server cannot be started.
+    """
+    # If URL already provided, use it
+    if os.environ.get("TURBOQUANT_SERVER_URL"):
+        yield os.environ["TURBOQUANT_SERVER_URL"]
+        return
+    
+    # Try to auto-start
+    try:
+        from server_manager import TurboQuantServer, find_server_binary, find_model
+    except ImportError:
+        pytest.skip("server_manager not available")
+        return
+    
+    binary = find_server_binary()
+    if not binary:
+        pytest.skip("llama-server binary not found — install llama-cpp-turboquant")
+        return
+    
+    model = find_model()
+    if not model:
+        pytest.skip("No GGUF model found — set TURBOQUANT_MODEL_DIR or place model in ~/models")
+        return
+    
+    port = int(os.environ.get("TURBOQUANT_TEST_PORT", "18081"))
+    kv_type = os.environ.get("TURBOQUANT_KV_TYPE", "turbo4")
+    ctx_size = int(os.environ.get("TURBOQUANT_CTX_SIZE", "8192"))
+    timeout = float(os.environ.get("TURBOQUANT_STARTUP_TIMEOUT", "60"))
+    
+    server = TurboQuantServer(
+        model_path=model,
+        port=port,
+        kv_type=kv_type,
+        context_size=ctx_size,
+        server_binary=binary,
+        timeout=timeout,
+    )
+    
+    try:
+        url = server.start()
+        yield url
+    except Exception as e:
+        pytest.skip(f"Could not start TurboQuant server: {e}")
+    finally:
+        server.stop()
+
+
+@pytest.fixture(scope="session")
+def turboquant_model_name(turboquant_server_url):
+    """Get the model name from the running server."""
+    import json
+    import urllib.request
+    
+    try:
+        req = urllib.request.Request(f"{turboquant_server_url}/v1/models")
+        resp = urllib.request.urlopen(req, timeout=10)
+        data = json.loads(resp.read())
+        models = data.get("data", [])
+        if models:
+            return models[0].get("id", "unknown")
+    except Exception:
+        pass
+    return "gemma-4"

tests/roundtrip_test.cpp

@@ -0,0 +1,104 @@
+#include "llama-turbo.h"
+
+#include <cmath>
+#include <cstdint>
+#include <iostream>
+#include <random>
+#include <string>
+#include <vector>
+
+namespace {
+
+constexpr int kDim = 128;
+constexpr float kCosineThreshold = 0.99f;
+constexpr float kZeroTolerance = 1.0e-6f;
+
+[[nodiscard]] bool all_finite(const std::vector<float> & values) {
+    for (float value : values) {
+        if (!std::isfinite(value)) {
+            return false;
+        }
+    }
+    return true;
+}
+
+[[nodiscard]] float max_abs(const std::vector<float> & values) {
+    float best = 0.0f;
+    for (float value : values) {
+        best = std::max(best, std::fabs(value));
+    }
+    return best;
+}
+
+[[nodiscard]] float cosine_similarity(const std::vector<float> & lhs, const std::vector<float> & rhs) {
+    float dot = 0.0f;
+    float lhs_norm = 0.0f;
+    float rhs_norm = 0.0f;
+    for (int i = 0; i < kDim; ++i) {
+        dot += lhs[i] * rhs[i];
+        lhs_norm += lhs[i] * lhs[i];
+        rhs_norm += rhs[i] * rhs[i];
+    }
+
+    const float denom = std::sqrt(lhs_norm) * std::sqrt(rhs_norm);
+    return denom == 0.0f ? 1.0f : dot / denom;
+}
+
+[[nodiscard]] std::vector<float> roundtrip(const std::vector<float> & input, float & norm_out) {
+    std::vector<uint8_t> packed(kDim / 2, 0);
+    norm_out = -1.0f;
+    polar_quant_encode_turbo4(input.data(), packed.data(), &norm_out, kDim);
+
+    std::vector<float> decoded(kDim, 0.0f);
+    polar_quant_decode_turbo4(packed.data(), decoded.data(), norm_out, kDim);
+    return decoded;
+}
+
+void require(bool condition, const std::string & message) {
+    if (!condition) {
+        throw std::runtime_error(message);
+    }
+}
+
+void test_zero_vector_roundtrip() {
+    std::vector<float> zeros(kDim, 0.0f);
+    float norm = -1.0f;
+    const auto decoded = roundtrip(zeros, norm);
+
+    require(norm == 0.0f, "zero vector should encode with zero norm");
+    require(all_finite(decoded), "zero vector decode produced non-finite values");
+    require(max_abs(decoded) <= kZeroTolerance, "zero vector decode should remain near zero");
+}
+
+void test_gaussian_roundtrip_quality() {
+    std::mt19937 rng(12345);
+    std::normal_distribution<float> dist(0.0f, 1.0f);
+
+    std::vector<float> input(kDim, 0.0f);
+    for (float & value : input) {
+        value = dist(rng);
+    }
+
+    float norm = -1.0f;
+    const auto decoded = roundtrip(input, norm);
+
+    require(norm > 0.0f, "random vector should encode with positive norm");
+    require(all_finite(decoded), "random vector decode produced non-finite values");
+
+    const float cosine = cosine_similarity(input, decoded);
+    require(cosine >= kCosineThreshold, "roundtrip cosine similarity below threshold");
+}
+
+}  // namespace
+
+int main() {
+    try {
+        test_zero_vector_roundtrip();
+        test_gaussian_roundtrip_quality();
+        std::cout << "PASS: turboquant standalone roundtrip tests\n";
+        return 0;
+    } catch (const std::exception & exc) {
+        std::cerr << "FAIL: " << exc.what() << '\n';
+        return 1;
+    }
+}

tests/server_manager.py

@@ -0,0 +1,197 @@
+#!/usr/bin/env python3
+"""
+TurboQuant Server Manager
+
+Manages llama-server lifecycle for integration tests:
+- Start server with TurboQuant flags
+- Wait for health check
+- Stop server on teardown
+
+Usage:
+    from tests.server_manager import TurboQuantServer
+    
+    with TurboQuantServer(model_path="/path/to/model.gguf") as server:
+        url = server.url  # e.g. http://localhost:8081
+        # Run tests against server
+"""
+
+import json
+import os
+import signal
+import subprocess
+import sys
+import time
+import urllib.request
+import urllib.error
+from pathlib import Path
+from typing import Optional
+
+
+class TurboQuantServer:
+    """Context manager for llama-server with TurboQuant."""
+    
+    def __init__(
+        self,
+        model_path: str,
+        port: int = 8081,
+        kv_type: str = "turbo4",
+        context_size: int = 32768,
+        server_binary: Optional[str] = None,
+        timeout: float = 60.0,
+        host: str = "127.0.0.1",
+    ):
+        self.model_path = model_path
+        self.port = port
+        self.kv_type = kv_type
+        self.context_size = context_size
+        self.timeout = timeout
+        self.host = host
+        
+        # Find server binary
+        if server_binary:
+            self.server_binary = server_binary
+        else:
+            # Try common locations
+            candidates = [
+                Path.home() / "llama-cpp-turboquant" / "build" / "bin" / "llama-server",
+                Path("/opt/llama-cpp-turboquant/build/bin/llama-server"),
+                Path("llama-server"),  # PATH
+            ]
+            self.server_binary = None
+            for c in candidates:
+                if c.exists() or c.name == "llama-server":
+                    try:
+                        subprocess.run([str(c), "--help"], capture_output=True, timeout=5)
+                        self.server_binary = str(c)
+                        break
+                    except (FileNotFoundError, subprocess.TimeoutExpired):
+                        continue
+        
+        self.process: Optional[subprocess.Popen] = None
+    
+    @property
+    def url(self) -> str:
+        return f"http://{self.host}:{self.port}"
+    
+    def _build_command(self) -> list:
+        cmd = [
+            self.server_binary,
+            "-m", self.model_path,
+            "--port", str(self.port),
+            "--host", self.host,
+            "-ctk", self.kv_type,
+            "-ctv", self.kv_type,
+            "-c", str(self.context_size),
+        ]
+        return cmd
+    
+    def _check_health(self) -> bool:
+        try:
+            req = urllib.request.Request(f"{self.url}/v1/models")
+            resp = urllib.request.urlopen(req, timeout=5)
+            data = json.loads(resp.read())
+            return "data" in data and len(data.get("data", [])) > 0
+        except Exception:
+            return False
+    
+    def start(self) -> str:
+        """Start the server and wait for it to be healthy. Returns the server URL."""
+        if not self.server_binary:
+            raise RuntimeError(
+                "llama-server binary not found. Set server_binary or install to standard location."
+            )
+        
+        if not Path(self.model_path).exists():
+            raise FileNotFoundError(f"Model not found: {self.model_path}")
+        
+        cmd = self._build_command()
+        
+        # Set TurboQuant env
+        env = os.environ.copy()
+        env["TURBO_LAYER_ADAPTIVE"] = "7"
+        
+        self.process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.PIPE,
+            env=env,
+        )
+        
+        # Wait for health
+        start = time.time()
+        while time.time() - start < self.timeout:
+            if self.process.poll() is not None:
+                stderr = self.process.stderr.read().decode() if self.process.stderr else ""
+                raise RuntimeError(f"Server exited early (code {self.process.returncode}): {stderr[:500]}")
+            
+            if self._check_health():
+                return self.url
+            
+            time.sleep(1.0)
+        
+        self.stop()
+        raise TimeoutError(f"Server did not become healthy within {self.timeout}s")
+    
+    def stop(self):
+        """Stop the server."""
+        if self.process:
+            try:
+                self.process.send_signal(signal.SIGTERM)
+                self.process.wait(timeout=10)
+            except subprocess.TimeoutExpired:
+                self.process.kill()
+                self.process.wait(timeout=5)
+            except Exception:
+                pass
+            self.process = None
+    
+    def __enter__(self) -> "TurboQuantServer":
+        self.start()
+        return self
+    
+    def __exit__(self, *args):
+        self.stop()
+
+
+def find_server_binary() -> Optional[str]:
+    """Find llama-server binary in common locations."""
+    candidates = [
+        Path.home() / "llama-cpp-turboquant" / "build" / "bin" / "llama-server",
+        Path("/opt/llama-cpp-turboquant/build/bin/llama-server"),
+    ]
+    for c in candidates:
+        if c.exists():
+            return str(c)
+    
+    # Try PATH
+    try:
+        result = subprocess.run(["which", "llama-server"], capture_output=True, text=True)
+        if result.returncode == 0:
+            return result.stdout.strip()
+    except Exception:
+        pass
+    
+    return None
+
+
+def find_model(model_dir: Optional[str] = None) -> Optional[str]:
+    """Find a GGUF model file."""
+    search_dirs = [
+        model_dir,
+        os.environ.get("TURBOQUANT_MODEL_DIR"),
+        str(Path.home() / "models"),
+        "/opt/models",
+        "/tmp/models",
+    ]
+    
+    for d in search_dirs:
+        if not d:
+            continue
+        p = Path(d)
+        if p.is_file() and p.suffix == ".gguf":
+            return str(p)
+        if p.is_dir():
+            for f in sorted(p.rglob("*.gguf")):
+                return str(f)
+    
+    return None

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_turboquant.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-"""
-TurboQuant Test Suite
-Tests for critical paths in KV cache compression.
-
-Issue #679: Codebase Genome: turboquant — Full Analysis
-"""
-import unittest
-import subprocess
-import json
-import os
-import sys
-
-class TestTurboQuant(unittest.TestCase):
-    """Test TurboQuant implementation."""
-    
-    def test_repo_structure(self):
-        """Verify expected files exist."""
-        required_files = [
-            "llama-turbo.h",
-            "llama-turbo.cpp",
-            "ggml-metal-turbo.metal",
-            "README.md",
-            "GENOME.md"
-        ]
-        
-        for filename in required_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing required file: {filename}")
-    
-    def test_benchmarks_exist(self):
-        """Verify benchmark scripts exist."""
-        benchmark_files = [
-            "benchmarks/run_benchmarks.py",
-            "benchmarks/run_perplexity.py",
-            "benchmarks/run_long_session.py"
-        ]
-        
-        for filename in benchmark_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing benchmark file: {filename}")
-    
-    def test_docs_complete(self):
-        """Verify documentation exists."""
-        doc_files = [
-            "docs/PROJECT_STATUS.md",
-            "profiles/README.md"
-        ]
-        
-        for filename in doc_files:
-            filepath = os.path.join(os.path.dirname(__file__), "..", filename)
-            self.assertTrue(os.path.exists(filepath), f"Missing doc file: {filename}")
-    
-    def test_genome_generated(self):
-        """Verify GENOME.md was generated."""
-        genome_path = os.path.join(os.path.dirname(__file__), "..", "GENOME.md")
-        self.assertTrue(os.path.exists(genome_path), "GENOME.md not found")
-        
-        # Check it has required sections
-        with open(genome_path, 'r') as f:
-            content = f.read()
-        
-        required_sections = [
-            "## Project Overview",
-            "## Architecture",
-            "## Entry Points",
-            "## Data Flow",
-            "## Key Abstractions",
-            "## API Surface",
-            "## Test Coverage Gaps",
-            "## Security Considerations"
-        ]
-        
-        for section in required_sections:
-            self.assertIn(section, content, f"GENOME.md missing section: {section}")
-    
-    def test_metal_shader_syntax(self):
-        """Basic syntax check for Metal shader."""
-        shader_path = os.path.join(os.path.dirname(__file__), "..", "ggml-metal-turbo.metal")
-        with open(shader_path, 'r') as f:
-            content = f.read()
-        
-        # Check for key functions
-        self.assertIn("kernel_fwht_128", content, "Missing kernel_fwht_128 function")
-        self.assertIn("kernel_turbo4_dequant", content, "Missing kernel_turbo4_dequant function")
-        self.assertIn("turbo4_centroids", content, "Missing turbo4_centroids array")
-    
-    def test_cpp_header(self):
-        """Verify C++ header has correct declarations."""
-        header_path = os.path.join(os.path.dirname(__file__), "..", "llama-turbo.h")
-        with open(header_path, 'r') as f:
-            content = f.read()
-        
-        # Check for function declarations
-        self.assertIn("polar_quant_encode_turbo4", content, "Missing encode function")
-        self.assertIn("polar_quant_decode_turbo4", content, "Missing decode function")
-        self.assertIn('extern "C"', content, "Missing C linkage")
-
-class TestBenchmarks(unittest.TestCase):
-    """Test benchmark infrastructure."""
-    
-    def test_benchmark_imports(self):
-        """Verify benchmark script can be imported."""
-        benchmark_path = os.path.join(os.path.dirname(__file__), "..", "benchmarks", "run_benchmarks.py")
-        
-        # Check file exists
-        self.assertTrue(os.path.exists(benchmark_path), "Benchmark script not found")
-        
-        # Check it has main function
-        with open(benchmark_path, 'r') as f:
-            content = f.read()
-        
-        self.assertIn("def main():", content, "Benchmark script missing main function")
-        self.assertIn("argparse", content, "Benchmark script missing argparse")
-
-class TestDocumentation(unittest.TestCase):
-    """Test documentation completeness."""
-    
-    def test_readme_sections(self):
-        """Verify README has required sections."""
-        readme_path = os.path.join(os.path.dirname(__file__), "..", "README.md")
-        with open(readme_path, 'r') as f:
-            content = f.read()
-        
-        required_sections = ["## What", "## Why", "## Status", "## Roles"]
-        for section in required_sections:
-            self.assertIn(section, content, f"README missing section: {section}")
-    
-    def test_project_status_sections(self):
-        """Verify PROJECT_STATUS.md has required sections."""
-        status_path = os.path.join(os.path.dirname(__file__), "..", "docs", "PROJECT_STATUS.md")
-        with open(status_path, 'r') as f:
-            content = f.read()
-        
-        # Check for key findings
-        self.assertIn("73%", content, "Missing 73% savings metric")
-        self.assertIn("PolarQuant", content, "Missing PolarQuant references")
-        self.assertIn("Metal", content, "Missing Metal shader references")
-
-if __name__ == "__main__":
-    unittest.main()