docs: record Qwen3.5-9B DFlash Metal timeout (refs #152, #154)

Merged PR #90: feat: integration test — turboquant compressed model

README.md

@@ -30,3 +30,4 @@ See [issues](https://forge.alexanderwhitestone.com/Timmy_Foundation/turboquant/i
 
 ## Docs
 - [Project Status](docs/PROJECT_STATUS.md) — Full project status and build specification
+- [DFlash on Apple Silicon](docs/DFLASH_APPLE_SILICON.md) — MLX benchmark planner, setup commands, and report workflow

benchmarks/dflash_apple_silicon.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+"""Apple Silicon DFlash planning helpers and CLI (issue #152)."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import platform
+import subprocess
+from dataclasses import asdict, dataclass
+from pathlib import Path
+from typing import Iterable, Optional
+
+
+@dataclass(frozen=True)
+class DFlashPair:
+    slug: str
+    base_model: str
+    draft_model: str
+    estimated_total_weights_gb: float
+    minimum_recommended_memory_gb: float
+    draft_sliding_window_size: int = 4096
+
+
+SUPPORTED_PAIRS: tuple[DFlashPair, ...] = (
+    DFlashPair(
+        slug="qwen35-4b",
+        base_model="Qwen/Qwen3.5-4B",
+        draft_model="z-lab/Qwen3.5-4B-DFlash",
+        estimated_total_weights_gb=9.68,
+        minimum_recommended_memory_gb=16.0,
+    ),
+    DFlashPair(
+        slug="qwen35-9b",
+        base_model="Qwen/Qwen3.5-9B",
+        draft_model="z-lab/Qwen3.5-9B-DFlash",
+        estimated_total_weights_gb=19.93,
+        minimum_recommended_memory_gb=28.0,
+    ),
+)
+
+
+def detect_total_memory_gb() -> float:
+    """Detect total system memory in GiB, rounded to a whole number for planning."""
+    system = platform.system()
+    if system == "Darwin":
+        mem_bytes = int(subprocess.check_output(["sysctl", "-n", "hw.memsize"]).strip())
+        return round(mem_bytes / (1024 ** 3), 1)
+    if system == "Linux":
+        with open("/proc/meminfo", "r", encoding="utf-8") as handle:
+            for line in handle:
+                if line.startswith("MemTotal:"):
+                    mem_kb = int(line.split()[1])
+                    return round(mem_kb / (1024 ** 2), 1)
+    raise RuntimeError(f"Unsupported platform for memory detection: {system}")
+
+
+def get_pair(slug: str) -> DFlashPair:
+    for pair in SUPPORTED_PAIRS:
+        if pair.slug == slug:
+            return pair
+    raise ValueError(f"Unknown DFlash pair: {slug}")
+
+
+def select_pair(total_memory_gb: float, preferred_slug: Optional[str] = None) -> DFlashPair:
+    """Pick the strongest upstream-supported pair likely to fit the machine."""
+    if preferred_slug:
+        return get_pair(preferred_slug)
+
+    fitting = [pair for pair in SUPPORTED_PAIRS if total_memory_gb >= pair.minimum_recommended_memory_gb]
+    if fitting:
+        return max(fitting, key=lambda pair: pair.minimum_recommended_memory_gb)
+    return SUPPORTED_PAIRS[0]
+
+
+def build_mlx_benchmark_command(
+    pair: DFlashPair,
+    *,
+    dataset: str = "gsm8k",
+    max_samples: int = 128,
+    enable_thinking: bool = True,
+) -> str:
+    """Build the upstream MLX benchmark command from the DFlash README."""
+    parts = [
+        "python -m dflash.benchmark --backend mlx",
+        f"--model {pair.base_model}",
+        f"--draft-model {pair.draft_model}",
+        f"--dataset {dataset}",
+        f"--max-samples {max_samples}",
+    ]
+    if enable_thinking:
+        parts.append("--enable-thinking")
+    parts.append(f"--draft-sliding-window-size {pair.draft_sliding_window_size}")
+    return " \\\n    ".join(parts)
+
+
+def build_setup_commands(pair: DFlashPair) -> list[str]:
+    return [
+        "python3 -m venv .venv-dflash",
+        "source .venv-dflash/bin/activate",
+        "git clone https://github.com/z-lab/dflash.git",
+        "cd dflash",
+        "pip install -e .[mlx]",
+        build_mlx_benchmark_command(pair),
+    ]
+
+
+def render_report_template(machine_label: str, pair: DFlashPair) -> str:
+    command = build_mlx_benchmark_command(pair)
+    return f"""# DFlash Apple Silicon Benchmark Report
+
+## Machine
+- Label: {machine_label}
+- Selected pair: {pair.slug}
+- Base model: {pair.base_model}
+- Draft model: {pair.draft_model}
+- Estimated total weight footprint: {pair.estimated_total_weights_gb:.2f} GB
+
+## Setup
+```bash
+python3 -m venv .venv-dflash
+source .venv-dflash/bin/activate
+git clone https://github.com/z-lab/dflash.git
+cd dflash
+pip install -e .[mlx]
+{command}
+```
+
+## Baseline comparison
+Compare against **plain MLX or llama.cpp speculative decoding** on the same prompt set.
+
+## Results
+- Throughput (tok/s):
+- Peak memory (GB):
+- Notes on acceptance / behavior:
+
+## Verdict
+Worth operationalizing locally?
+- [ ] Yes
+- [ ] No
+- [ ] Needs more data
+
+## Recommendation
+Explain whether this should become part of the local inference stack.
+"""
+
+
+def build_plan(total_memory_gb: float, preferred_slug: Optional[str] = None) -> dict:
+    pair = select_pair(total_memory_gb=total_memory_gb, preferred_slug=preferred_slug)
+    return {
+        "machine_memory_gb": total_memory_gb,
+        "selected_pair": asdict(pair),
+        "setup_commands": build_setup_commands(pair),
+        "benchmark_command": build_mlx_benchmark_command(pair),
+        "baseline_note": "Compare against plain MLX or llama.cpp speculative decoding on the same prompt set.",
+    }
+
+
+def write_output(path: Path, content: str) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(content, encoding="utf-8")
+
+
+def main(argv: Optional[Iterable[str]] = None) -> int:
+    parser = argparse.ArgumentParser(description="Plan Apple Silicon DFlash benchmarks")
+    parser.add_argument("--memory-gb", type=float, default=None, help="Override detected total memory")
+    parser.add_argument("--pair", choices=[pair.slug for pair in SUPPORTED_PAIRS], default=None)
+    parser.add_argument("--machine-label", default="Apple Silicon Mac")
+    parser.add_argument("--format", choices=["json", "markdown"], default="markdown")
+    parser.add_argument("--output", default=None, help="Write plan/report to file instead of stdout")
+    args = parser.parse_args(list(argv) if argv is not None else None)
+
+    memory_gb = args.memory_gb if args.memory_gb is not None else detect_total_memory_gb()
+    pair = select_pair(total_memory_gb=memory_gb, preferred_slug=args.pair)
+
+    if args.format == "json":
+        content = json.dumps(build_plan(memory_gb, preferred_slug=pair.slug), indent=2)
+    else:
+        content = render_report_template(args.machine_label, pair)
+
+    if args.output:
+        write_output(Path(args.output), content)
+    else:
+        print(content)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

benchmarks/reports/dflash_m3max_36gb.md

@@ -0,0 +1,41 @@
+# DFlash Apple Silicon Benchmark Report
+
+## Machine
+- Label: M3 Max 36GB
+- Selected pair: qwen35-9b
+- Base model: Qwen/Qwen3.5-9B
+- Draft model: z-lab/Qwen3.5-9B-DFlash
+- Estimated total weight footprint: 19.93 GB
+
+## Setup
+```bash
+python3 -m venv .venv-dflash
+source .venv-dflash/bin/activate
+git clone https://github.com/z-lab/dflash.git
+cd dflash
+pip install -e .[mlx]
+python -m dflash.benchmark --backend mlx \
+    --model Qwen/Qwen3.5-9B \
+    --draft-model z-lab/Qwen3.5-9B-DFlash \
+    --dataset gsm8k \
+    --max-samples 128 \
+    --enable-thinking \
+    --draft-sliding-window-size 4096
+```
+
+## Baseline comparison
+Compare against **plain MLX or llama.cpp speculative decoding** on the same prompt set.
+
+## Results
+- Throughput (tok/s):
+- Peak memory (GB):
+- Notes on acceptance / behavior:
+
+## Verdict
+Worth operationalizing locally?
+- [ ] Yes
+- [ ] No
+- [ ] Needs more data
+
+## Recommendation
+Explain whether this should become part of the local inference stack.

benchmarks/reports/dflash_m3max_36gb_qwen35_4b_pilot.md

@@ -0,0 +1,46 @@
+# DFlash Apple Silicon Pilot — Qwen3.5-4B on M3 Max 36GB
+
+Date: 2026-04-21
+Machine: Apple M3 Max, 36 GB unified memory
+Repo issue: #152
+
+## Command
+
+```bash
+source /tmp/dflash-venv/bin/activate
+cd /tmp/dflash-upstream
+python -m dflash.benchmark --backend mlx \
+    --model Qwen/Qwen3.5-4B \
+    --draft-model z-lab/Qwen3.5-4B-DFlash \
+    --dataset gsm8k \
+    --max-samples 1 \
+    --enable-thinking \
+    --draft-sliding-window-size 4096
+```
+
+## Result
+
+- Dataset: `gsm8k`
+- Samples: `1`
+- Baseline throughput: `22.35 tok/s`
+- DFlash throughput: `46.78 tok/s`
+- Decoding speedup: `2.09x`
+- Average acceptance length: `6.48`
+
+Acceptance length histogram:
+
+```text
+['0.3%', '11.1%', '12.7%', '10.4%', '11.7%', '7.6%', '7.0%', '3.8%', '5.1%', '6.3%', '2.8%', '3.8%', '2.2%', '1.9%', '0.9%', '2.5%', '9.8%']
+```
+
+## Caveats
+
+- This is a **pilot**, not a decision-grade benchmark.
+- Only `1` sample was run, so the throughput number is directional.
+- No apples-to-apples baseline against plain MLX or llama.cpp speculative decoding is included yet.
+- The planner still recommends trying `Qwen/Qwen3.5-9B + z-lab/Qwen3.5-9B-DFlash` on this machine for the more meaningful fit test.
+
+## Interim takeaway
+
+DFlash is **real on Apple Silicon** and already shows a meaningful local speedup on a small matched pair.
+A `2.09x` pilot speedup on `Qwen3.5-4B` is enough evidence to keep pushing toward a proper benchmark slice in this repo.

benchmarks/reports/dflash_m3max_36gb_qwen35_9b_timeout.md

@@ -0,0 +1,59 @@
+# DFlash on Apple Silicon Failure Report — Qwen3.5-9B on M3 Max 36GB
+
+Date: 2026-04-21
+Machine: Apple M3 Max, 36 GB unified memory
+Repo issue: #152
+
+## Command
+
+```bash
+source /tmp/dflash-venv/bin/activate
+cd /tmp/dflash-upstream
+python -m dflash.benchmark --backend mlx \
+    --model Qwen/Qwen3.5-9B \
+    --draft-model z-lab/Qwen3.5-9B-DFlash \
+    --dataset gsm8k \
+    --max-samples 1 \
+    --enable-thinking \
+    --draft-sliding-window-size 4096
+```
+
+## Outcome
+
+The benchmark did **not** complete successfully on this machine.
+
+### Failure signature
+
+```text
+libc++abi: terminating due to uncaught exception of type std::runtime_error:
+[METAL] Command buffer execution failed:
+Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
+```
+
+Additional shutdown noise:
+
+```text
+bash: [11285: 1] tcsetattr: Inappropriate ioctl for device
+resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
+```
+
+## Interpretation
+
+This is strong evidence that the `Qwen/Qwen3.5-9B + z-lab/Qwen3.5-9B-DFlash` pair is **not currently stable** on an M3 Max 36GB Mac under the upstream MLX benchmark path, at least with the default settings used here.
+
+It may still be salvageable with:
+- smaller block size / different benchmark settings
+- a shorter generation target
+- a different prompt sample
+- upstream MLX / Metal fixes
+- newer Apple Silicon hardware
+
+But as of this run, it should be treated as **experimental / failing** on this exact machine.
+
+## Recommendation
+
+For this Mac, the working local proof path is still:
+- `Qwen/Qwen3.5-4B`
+- `z-lab/Qwen3.5-4B-DFlash`
+
+Use the 4B pair for reproducible local validation while the 9B Metal timeout is investigated separately.

docs/DFLASH_APPLE_SILICON.md

@@ -0,0 +1,125 @@
+# DFlash on Apple Silicon
+
+This repo now carries a **Gitea-first benchmark harness** for evaluating whether upstream **DFlash on MLX** is worth adding to the local Apple Silicon inference stack.
+
+## Why
+
+The headline `Kimi K2.6 + DFlash` benchmark was measured on `8x MI300X` with huge RAM and ROCm patches. That exact recipe is not a fit for a `36 GB` Apple Silicon Mac.
+
+What *is* relevant locally is the upstream `z-lab/dflash` MLX path, which can benchmark smaller matched target/draft pairs that fit on Apple Silicon.
+
+## Current repo entry point
+
+Use:
+
+```bash
+python3 benchmarks/dflash_apple_silicon.py --machine-label "M3 Max 36GB"
+```
+
+This prints a benchmark report template with:
+- the selected model/draft pair
+- exact setup commands
+- the upstream MLX benchmark command
+- baseline comparison guidance
+
+Write the template to a file:
+
+```bash
+python3 benchmarks/dflash_apple_silicon.py \
+  --machine-label "M3 Max 36GB" \
+  --output benchmarks/reports/dflash_m3max_36gb.md
+```
+
+Emit the underlying plan as JSON:
+
+```bash
+python3 benchmarks/dflash_apple_silicon.py --format json
+```
+
+## Selection logic
+
+Today the planner uses two upstream-supported MLX pairs:
+
+- `qwen35-9b`
+  - base: `Qwen/Qwen3.5-9B`
+  - draft: `z-lab/Qwen3.5-9B-DFlash`
+  - chosen for ~28 GB+ machines
+- `qwen35-4b`
+  - base: `Qwen/Qwen3.5-4B`
+  - draft: `z-lab/Qwen3.5-4B-DFlash`
+  - fallback for tighter-memory Macs
+
+On a `36 GB` Mac, the default recommendation is `qwen35-9b`.
+
+## Pilot result already landed
+
+A first live Apple Silicon run has already been captured in:
+
+- `benchmarks/reports/dflash_m3max_36gb_qwen35_4b_pilot.md`
+
+Pilot command:
+
+```bash
+python -m dflash.benchmark --backend mlx \
+    --model Qwen/Qwen3.5-4B \
+    --draft-model z-lab/Qwen3.5-4B-DFlash \
+    --dataset gsm8k \
+    --max-samples 1 \
+    --enable-thinking \
+    --draft-sliding-window-size 4096
+```
+
+Pilot outcome on this Mac:
+
+- baseline throughput: `22.35 tok/s`
+- DFlash throughput: `46.78 tok/s`
+- decoding speedup: `2.09x`
+
+Treat that as a **directional proof**, not a final decision benchmark. The next step is the fuller comparison slice against plain MLX or llama.cpp speculative decoding.
+
+## Known 9B failure on this machine
+
+A follow-up live run with:
+
+- `Qwen/Qwen3.5-9B`
+- `z-lab/Qwen3.5-9B-DFlash`
+
+failed on this same M3 Max 36GB Mac with:
+
+```text
+[METAL] Command buffer execution failed:
+Caused GPU Timeout Error (00000002:kIOGPUCommandBufferCallbackErrorTimeout)
+```
+
+That failure is recorded in:
+
+- `benchmarks/reports/dflash_m3max_36gb_qwen35_9b_timeout.md`
+
+So the current guidance is:
+- treat `qwen35-9b` as **experimental** on this machine
+- treat `qwen35-4b` as the current **known-working local proof path**
+- keep the issue open until we either stabilize the 9B path or clearly rule it out for this hardware tier
+
+## Upstream benchmark command
+
+The harness uses the upstream MLX benchmark syntax from `z-lab/dflash`:
+
+```bash
+python -m dflash.benchmark --backend mlx \
+    --model Qwen/Qwen3.5-9B \
+    --draft-model z-lab/Qwen3.5-9B-DFlash \
+    --dataset gsm8k \
+    --max-samples 128 \
+    --enable-thinking \
+    --draft-sliding-window-size 4096
+```
+
+## What remains
+
+This PR adds the **planner + report template** so the benchmark is reproducible from the repo.
+The issue remains open until a real Apple Silicon run lands with:
+
+- measured throughput
+- measured memory
+- a baseline comparison against plain MLX or llama.cpp speculative decoding
+- a recommendation on whether to operationalize DFlash locally

docs/PROJECT_STATUS.md

@@ -1,21 +1,6 @@
-# PROJECT STATUS — Living Tracker
-
-> **For current status, see [STATUS_TRACKER.md](./STATUS_TRACKER.md).**
-> Updated on each milestone. This file contains detailed phase reports.
->
-> Quick view:
-> - Phase 1: DONE
-> - Phase 2: IN PROGRESS
-> - Edge Crisis Detection: DONE
-> - Integration PR: NOT STARTED
-> - QJL: NOT STARTED
-> - Ollama: NOT STARTED
-
----
-
 # TurboQuant Project Status
 
-# TurboQuant Phase 1 Report — PolarQuant MVP
+# TurboQuant Phase 1 Report — PolarQuant MVP
 
 **Date:** 2026-03-30
 **Prepared by:** Timmy (execution) for Frankie's team (Strago, Cid, Locke, John)
@@ -31,13 +16,13 @@ Phase 1 is COMPLETE. TurboQuant KV cache compression works on Apple Silicon with
 
 ---
 
-## Gate Check (#2): PASSED ✅
+## Gate Check (#2): PASSED ✅
 
 Metal shaders exist and are comprehensive:
 - Full flash attention for turbo2/3/4 with dk32-dk576 variants
 - WHT rotation kernels (turbo_fwht_128, turbo_rotate_forward/inverse)
-- PolarQuant codebooks hardcoded (Lloyd-Max for N(0, 1/√128))
-- Asymmetric K/V support (q8_0 × turbo mixed pairs)
+- PolarQuant codebooks hardcoded (Lloyd-Max for N(0, 1/√128))
+- Asymmetric K/V support (q8_0 × turbo mixed pairs)
 - M4+ optimizations (4-mag LUT), sparse V dequant, profiling modes
 - Additional experiment branches: layer-adaptive, fused-centroid-decode, speed-optimization
 
@@ -45,7 +30,7 @@ Metal shaders exist and are comprehensive:
 
 ---
 
-## Fork Assessment (#3): PASSED ✅
+## Fork Assessment (#3): PASSED ✅
 
 - Branch: `feature/turboquant-kv-cache` (commit adac2c6)
 - Fork freshness: ADEQUATE (recent enough for direct build)
@@ -54,18 +39,18 @@ Metal shaders exist and are comprehensive:
 
 ---
 
-## PolarQuant Verification (#5): 5/6 PASS, 1 PARTIAL ✅
+## PolarQuant Verification (#5): 5/6 PASS, 1 PARTIAL ✅
 
 | Item | Verdict |
 |------|---------|
-| WHT rotation (structured orthogonal) | PARTIAL PASS — Metal GPU uses WHT ✅. CPU turbo4 ref uses dense random (legacy, not production) |
-| Same rotation quant/dequant | PASS — turbo_rotate_forward() ↔ turbo_rotate_inverse() identical sign arrays |
-| Lloyd-Max codebook (not uniform) | PASS — non-uniform centroids, "Lloyd-Max for N(0, 1/128)" |
-| Radius at FP16+ | PASS — ggml_half norm per 128-element group |
-| No per-vector normalization | PASS — one group norm only, static_asserts enforce block sizes |
-| Dequant matches quant in Metal | PASS — same centroids, signs, butterfly structure |
+| WHT rotation (structured orthogonal) | PARTIAL PASS — Metal GPU uses WHT ✅. CPU turbo4 ref uses dense random (legacy, not production) |
+| Same rotation quant/dequant | PASS — turbo_rotate_forward() ↔ turbo_rotate_inverse() identical sign arrays |
+| Lloyd-Max codebook (not uniform) | PASS — non-uniform centroids, "Lloyd-Max for N(0, 1/128)" |
+| Radius at FP16+ | PASS — ggml_half norm per 128-element group |
+| No per-vector normalization | PASS — one group norm only, static_asserts enforce block sizes |
+| Dequant matches quant in Metal | PASS — same centroids, signs, butterfly structure |
 
-**⚠️ Flag for Cid:** CPU turbo4 reference path is incompatible with Metal dequant. Only matters if CPU fallback is ever invoked for turbo4.
+**⚠️ Flag for Cid:** CPU turbo4 reference path is incompatible with Metal dequant. Only matters if CPU fallback is ever invoked for turbo4.
 
 ---
 
@@ -77,9 +62,9 @@ Metal shaders exist and are comprehensive:
 
 ### Throughput (3-run averages)
 
-| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
+| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
 |:-------------|:---------------|:--|:-------------------|:--|
-| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
+| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
 | **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
 | turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
 | q8_0/turbo4 (asym) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
@@ -93,17 +78,17 @@ Metal shaders exist and are comprehensive:
 | 32K | 5,120 MiB | 1,360 MiB | 73.4% |
 | 65K | 10,240 MiB | 2,720 MiB | 73.4% |
 
-Measured matches calculated exactly — zero fragmentation overhead.
+Measured matches calculated exactly — zero fragmentation overhead.
 
 ### Pass Criteria Assessment
 
 | Criteria | Threshold | Result | Verdict |
 |:---------|:----------|:-------|:--------|
-| PPL delta ≤ 0.5 | ≤ 0.5 | ⏭️ Not tested (no wikitext corpus) | DEFERRED |
-| tok/s ≥ 90% baseline (prompt) | ≥ 274 t/s | 300.00 t/s (98.9%) | **PASS** |
-| tok/s ≥ 90% baseline (gen) | ≥ 24.7 t/s | 22.45 t/s (89%) | **BORDERLINE** |
+| PPL delta ≤ 0.5 | ≤ 0.5 | ⏭️ Not tested (no wikitext corpus) | DEFERRED |
+| tok/s ≥ 90% baseline (prompt) | ≥ 274 t/s | 300.00 t/s (98.9%) | **PASS** |
+| tok/s ≥ 90% baseline (gen) | ≥ 24.7 t/s | 22.45 t/s (89%) | **BORDERLINE** |
 | No OOM at 32K | No crash | Runs clean | **PASS** |
-| Memory consistent with theory | ±15% | 0% delta | **PASS** |
+| Memory consistent with theory | ±15% | 0% delta | **PASS** |
 
 ---
 
@@ -111,10 +96,10 @@ Measured matches calculated exactly — zero fragmentation overhead.
 
 | Scenario | Total Memory | Fits in 31GB? |
 |:---------|:-------------|:--------------|
-| 27B Q4_K_M + f16 KV @ 64K | ~26 GB | ⚠️ Tight |
-| 27B Q4_K_M + f16 KV @ 128K | ~38 GB | ❌ No |
-| 27B Q4_K_M + **turbo4 KV @ 64K** | ~20.5 GB | ✅ Comfortable |
-| 27B Q4_K_M + **turbo4 KV @ 128K** | ~23.4 GB | ✅ Fits (7.6GB headroom) |
+| 27B Q4_K_M + f16 KV @ 64K | ~26 GB | ⚠️ Tight |
+| 27B Q4_K_M + f16 KV @ 128K | ~38 GB | ❌ No |
+| 27B Q4_K_M + **turbo4 KV @ 64K** | ~20.5 GB | ✅ Comfortable |
+| 27B Q4_K_M + **turbo4 KV @ 128K** | ~23.4 GB | ✅ Fits (7.6GB headroom) |
 
 **TurboQuant turns 128K context from impossible to comfortable.**
 
@@ -122,11 +107,11 @@ Measured matches calculated exactly — zero fragmentation overhead.
 
 ## Open Items for Phase 2
 
-1. **Perplexity test** — Need wikitext-2-raw corpus downloaded. PPL is the most important quality metric and we don't have it yet.
-2. **Ollama integration** — CLI is a broken symlink. Need to fix Ollama install, then build custom Ollama with our fork as submodule.
-3. **qwen3.5:27b model** — Need to download the actual target model (only have Hermes-4-14B on disk currently).
-4. **10 test prompts** — Need to be written before Phase 2 quality comparison.
-5. **Generation speed borderline** — tg128 at 89% is just below the 90% threshold. May improve with the speed-optimization branch. Worth testing.
+1. **Perplexity test** — Need wikitext-2-raw corpus downloaded. PPL is the most important quality metric and we don't have it yet.
+2. **Ollama integration** — CLI is a broken symlink. Need to fix Ollama install, then build custom Ollama with our fork as submodule.
+3. **qwen3.5:27b model** — Need to download the actual target model (only have Hermes-4-14B on disk currently).
+4. **10 test prompts** — Need to be written before Phase 2 quality comparison.
+5. **Generation speed borderline** — tg128 at 89% is just below the 90% threshold. May improve with the speed-optimization branch. Worth testing.
 
 ---
 
@@ -134,21 +119,21 @@ Measured matches calculated exactly — zero fragmentation overhead.
 
 **PROCEED TO PHASE 2.**
 
-turbo4 delivers the goods: 73% KV memory savings, near-zero prompt overhead, acceptable generation overhead. The verification checklist confirms the implementation is algorithmically sound. The only gap is PPL testing, which is a corpus download away — not a fundamental risk.
+turbo4 delivers the goods: 73% KV memory savings, near-zero prompt overhead, acceptable generation overhead. The verification checklist confirms the implementation is algorithmically sound. The only gap is PPL testing, which is a corpus download away — not a fundamental risk.
 
-The real unlock — 128K context on 36GB hardware — is within reach. Phase 2 is Ollama integration and production deployment.
+The real unlock — 128K context on 36GB hardware — is within reach. Phase 2 is Ollama integration and production deployment.
 
 ---
 
 ## Issues Closed
 
-- [x] #2 Metal kernel check — PASSED
-- [x] #3 Fork assessment — PASSED
-- [x] #4 Build llama.cpp fork — COMPLETE
-- [x] #5 PolarQuant verification — 5/6 PASS
-- [x] #6 FP16 baseline benchmarks — RECORDED
-- [x] #7 TurboQuant benchmarks — RECORDED
-- [x] #8 Memory profiling — COMPLETE
+- [x] #2 Metal kernel check — PASSED
+- [x] #3 Fork assessment — PASSED
+- [x] #4 Build llama.cpp fork — COMPLETE
+- [x] #5 PolarQuant verification — 5/6 PASS
+- [x] #6 FP16 baseline benchmarks — RECORDED
+- [x] #7 TurboQuant benchmarks — RECORDED
+- [x] #8 Memory profiling — COMPLETE
 
 ---
 
@@ -158,7 +143,7 @@ The real unlock — 128K context on 36GB hardware — is within reach. Pha
 
 ---
 
-# TurboQuant — Full Knowledge Transfer Report
+# TurboQuant — Full Knowledge Transfer Report
 
 **Date:** 2026-03-30
 **Prepared for:** Frankie's Team (Strago, Cid, Locke, John)
@@ -168,7 +153,7 @@ The real unlock — 128K context on 36GB hardware — is within reach. Pha
 
 ## TL;DR
 
-TurboQuant works. PolarQuant KV cache compression delivers **73% memory savings with 1% prompt overhead**. 128K context on the MacBook becomes viable. Custom Ollama build is deferred (multi-day effort), but the fork's `llama-server` is a ready drop-in. Per-layer adaptive quantization is already implemented. QJL is infrastructure-only — not needed at current compression targets.
+TurboQuant works. PolarQuant KV cache compression delivers **73% memory savings with 1% prompt overhead**. 128K context on the MacBook becomes viable. Custom Ollama build is deferred (multi-day effort), but the fork's `llama-server` is a ready drop-in. Per-layer adaptive quantization is already implemented. QJL is infrastructure-only — not needed at current compression targets.
 
 ---
 
@@ -181,14 +166,14 @@ Impact: Memory budget **increases** from ~27GB to ~31GB usable. Model ceiling im
 
 ---
 
-## Phase 1 — PolarQuant MVP: COMPLETE ✅
+## Phase 1 — PolarQuant MVP: COMPLETE ✅
 
 ### Gate Check (#2): Metal Shaders EXIST
 The `feature/turboquant-kv-cache` branch has production-quality Metal support:
 - Flash attention for turbo2/3/4 (all dk variants)
 - WHT rotation kernels (turbo_fwht_128)
 - Lloyd-Max codebooks (hardcoded, non-uniform)
-- Asymmetric K/V (q8_0 × turbo mixed)
+- Asymmetric K/V (q8_0 × turbo mixed)
 - Runtime optimizations: 4-mag LUT (M4+), sparse V dequant, profiling
 
 **Note:** Allegro's analysis (checking only `master` branch) incorrectly concluded "NO TurboQuant." The implementation lives on the feature branch.
@@ -212,9 +197,9 @@ The `feature/turboquant-kv-cache` branch has production-quality Metal support:
 
 #### Throughput
 
-| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
+| Config (K/V) | Prompt (pp512) | Δ | Generation (tg128) | Δ |
 |:-------------|:---------------|:--|:-------------------|:--|
-| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
+| f16/f16 (baseline) | 304.28 t/s | — | 27.47 t/s | — |
 | **turbo4/turbo4** | **300.00 t/s** | **-1.1%** | **22.45 t/s** | **-11.1%** |
 | turbo3/turbo3 | 271.07 t/s | -10.7% | 21.07 t/s | -16.6% |
 | q8_0/turbo4 (asymmetric) | 260.57 t/s | -14.1% | 23.75 t/s | -5.9% |
@@ -234,12 +219,12 @@ Measured matches calculated exactly. Zero fragmentation overhead.
 
 | Scenario | Total Memory | Fits 31GB? |
 |:---------|:-------------|:-----------|
-| 27B + f16 KV @ 128K | ~38 GB | ❌ No |
-| 27B + **turbo4 KV @ 128K** | **~23.4 GB** | **✅ Yes (7.6GB headroom)** |
+| 27B + f16 KV @ 128K | ~38 GB | ❌ No |
+| 27B + **turbo4 KV @ 128K** | **~23.4 GB** | **✅ Yes (7.6GB headroom)** |
 
 ---
 
-## Phase 2 — Ollama Integration: PARTIALLY COMPLETE
+## Phase 2 — Ollama Integration: PARTIALLY COMPLETE
 
 ### What Works
 - Ollama installation fixed (v0.17.7, running on :11434)
@@ -271,7 +256,7 @@ The fork's `llama-server` binary is **already built and working**:
 - Streaming SSE support
 - All TurboQuant KV types supported
 - Per-layer adaptive via TURBO_LAYER_ADAPTIVE env var
-- Same port/protocol as Ollama — clients don't need to change
+- Same port/protocol as Ollama — clients don't need to change
 
 ### Outstanding Phase 2 Items for Cid
 - [ ] Download qwen3.5:27b Q4_K_M model
@@ -282,7 +267,7 @@ The fork's `llama-server` binary is **already built and working**:
 
 ---
 
-## Phase 2.5 — Per-Layer Quantization: ALREADY IMPLEMENTED ✅
+## Phase 2.5 — Per-Layer Quantization: ALREADY IMPLEMENTED ✅
 
 Found in the fork. No additional work needed.
 
@@ -306,10 +291,10 @@ Mode benchmarks queued. Uniform turbo4 baseline established. Per-layer modes exp
 
 ---
 
-## Phase 3 — QJL: ASSESSED, NOT NEEDED ✅
+## Phase 3 — QJL: ASSESSED, NOT NEEDED ✅
 
 ### Finding
-**turbo4 is pure 4-bit PolarQuant** — QJL is NOT active.
+**turbo4 is pure 4-bit PolarQuant** — QJL is NOT active.
 
 `TURBO4_USE_4BIT` defaults to 1 in `ggml-common.h`. The legacy 3-bit+QJL path exists but is disabled. QJL infrastructure (sign arrays, WHT transforms, 128x128 projection matrices) is embedded in Metal but referenced by no active kernel.
 
@@ -322,7 +307,7 @@ Mode benchmarks queued. Uniform turbo4 baseline established. Per-layer modes exp
 
 | Repo | Status | Value |
 |:-----|:-------|:------|
-| TheTom/llama-cpp-turboquant | **PRIMARY** — production Metal shaders on feature branch | Build from this |
+| TheTom/llama-cpp-turboquant | **PRIMARY** — production Metal shaders on feature branch | Build from this |
 | TheTom/turboquant_plus | Python reference + 511 tests | Algorithm verification |
 | rachittshah/mlx-turboquant | Complete MLX PoC, 2-5x slower (no Metal fusion) | Quality validation reference |
 | amirzandieh/QJL | Author CUDA (~1500 lines) | Future QJL Metal port reference |
@@ -333,12 +318,12 @@ Mode benchmarks queued. Uniform turbo4 baseline established. Per-layer modes exp
 
 | Risk | Status | Mitigation |
 |:-----|:-------|:-----------|
-| Metal shaders missing | ✅ RESOLVED — they exist | — |
-| Fork too stale | ✅ RESOLVED — builds clean | — |
-| Ollama integration blocked | ⚠️ ACTIVE — multi-day effort | Use llama-server instead |
-| PPL regression | ⏸️ UNTESTED — needs wikitext corpus | Download and test in prod |
-| tg128 borderline (89% vs 90% threshold) | ⚠️ MINOR — within measurement noise | speed-optimization branch may help |
-| CPU turbo4 incompatible with Metal | ℹ️ LOW — only matters if Metal unavailable | Document; Metal is production path |
+| Metal shaders missing | ✅ RESOLVED — they exist | — |
+| Fork too stale | ✅ RESOLVED — builds clean | — |
+| Ollama integration blocked | ⚠️ ACTIVE — multi-day effort | Use llama-server instead |
+| PPL regression | ⏸️ UNTESTED — needs wikitext corpus | Download and test in prod |
+| tg128 borderline (89% vs 90% threshold) | ⚠️ MINOR — within measurement noise | speed-optimization branch may help |
+| CPU turbo4 incompatible with Metal | ℹ️ LOW — only matters if Metal unavailable | Document; Metal is production path |
 
 ---
 
@@ -370,7 +355,7 @@ Step 4: Validate
 
 Step 5: Run quality matrix (prompts on issue #16)
 Step 6: John reviews output quality
-Step 7: If pass → production. If fail → drop to turbo3 or adjust per-layer profile.
+Step 7: If pass → production. If fail → drop to turbo3 or adjust per-layer profile.
 ```
 
 ---
@@ -380,21 +365,21 @@ Step 7: If pass → production. If fail → drop to turbo3 or adjust per-l
 | # | Title | Status |
 |:--|:------|:-------|
 | 1 | Epic: TurboQuant KV Cache Compression | Open (tracker) |
-| 2 | Metal kernel check | ✅ Closed — PASS |
-| 3 | Fork assessment | ✅ Closed — PASS, M3 Max 36GB |
-| 4 | Build llama.cpp fork | ✅ Closed — clean build |
-| 5 | PolarQuant verification | ✅ Closed — 5/6 PASS |
-| 6 | Baseline benchmarks | ✅ Closed — recorded |
-| 7 | TurboQuant benchmarks | ✅ Closed — 73% savings |
-| 8 | Memory profiling | ✅ Closed — 0% fragmentation |
-| 9 | Ollama API check | ✅ Closed — additive, but diverged |
-| 10 | Custom Ollama build | ✅ Closed — deferred, llama-server instead |
-| 11 | Full test matrix | Open — awaiting production deploy |
-| 12 | Long-session test | Open — awaiting production deploy |
-| 13 | Per-layer profiles | ✅ Closed — already implemented |
-| 14 | QJL assessment | ✅ Closed — not needed |
-| 15 | Upstream watch | Open — ongoing |
-| 16 | Test prompts | Open — Allegro contributed prompts |
+| 2 | Metal kernel check | ✅ Closed — PASS |
+| 3 | Fork assessment | ✅ Closed — PASS, M3 Max 36GB |
+| 4 | Build llama.cpp fork | ✅ Closed — clean build |
+| 5 | PolarQuant verification | ✅ Closed — 5/6 PASS |
+| 6 | Baseline benchmarks | ✅ Closed — recorded |
+| 7 | TurboQuant benchmarks | ✅ Closed — 73% savings |
+| 8 | Memory profiling | ✅ Closed — 0% fragmentation |
+| 9 | Ollama API check | ✅ Closed — additive, but diverged |
+| 10 | Custom Ollama build | ✅ Closed — deferred, llama-server instead |
+| 11 | Full test matrix | Open — awaiting production deploy |
+| 12 | Long-session test | Open — awaiting production deploy |
+| 13 | Per-layer profiles | ✅ Closed — already implemented |
+| 14 | QJL assessment | ✅ Closed — not needed |
+| 15 | Upstream watch | Open — ongoing |
+| 16 | Test prompts | Open — Allegro contributed prompts |
 
 **12/16 issues resolved. 4 remaining are production validation tasks for Cid.**
 
@@ -407,8 +392,8 @@ Step 7: If pass → production. If fail → drop to turbo3 or adjust per-l
 
 ---
 
-# TurboQuant Implementation — Build Spec (v2)
-**Prepared by:** Strago | **Date:** 2026-03-30 | **Updated:** 2026-03-30 (v2 — external review fixes)
+# TurboQuant Implementation — Build Spec (v2)
+**Prepared by:** Strago | **Date:** 2026-03-30 | **Updated:** 2026-03-30 (v2 — external review fixes)
 **Task:** STR-2026-03-30-01 | **For:** Cid (build) + Frankie (coordination)
 **Inputs read:** turboquant-2026-03-25.md (Google brief), turboquant-2026-03-30-recon-update.md (Locke recon), infra-bulletin.md, MEMORY.md, external Opus review
 
@@ -419,64 +404,64 @@ Step 7: If pass → production. If fail → drop to turbo3 or adjust per-l
 John wants maximum local inference quality on the MacBook Pro (M4 Max, 32GB unified memory) using TurboQuant-level KV cache compression. Currently running `qwen3.5:27b` via Ollama at `10.0.0.133:11434`. The goal: run a larger or better model within the same 32GB memory envelope by compressing the KV cache during inference.
 
 TurboQuant (Google, ICLR 2026) is a three-stage KV cache compression method:
-1. **PolarQuant** — random rotation + polar coordinates + fixed scalar codebook. No normalization constants. ~4.2× compression.
-2. **QJL** — 1-bit quantized Johnson-Lindenstrauss on the residual. Zero-overhead bias correction.
-3. **TurboQuant** — PolarQuant for main signal + QJL for residual = unbiased inner product quantizer at ~3.5 bits/channel with zero accuracy loss.
+1. **PolarQuant** — random rotation + polar coordinates + fixed scalar codebook. No normalization constants. ~4.2× compression.
+2. **QJL** — 1-bit quantized Johnson-Lindenstrauss on the residual. Zero-overhead bias correction.
+3. **TurboQuant** — PolarQuant for main signal + QJL for residual = unbiased inner product quantizer at ~3.5 bits/channel with zero accuracy loss.
 
 Community status: multiple `llama.cpp` forks, MLX proof-of-concepts, and a vLLM plugin exist. Nothing upstreamed to official `llama.cpp`, MLX, or Ollama yet. Author QJL code is public. Enough is public to build from.
 
 ---
 
-## 1a. PolarQuant Technical Detail — What Cid Needs to Verify
+## 1a. PolarQuant Technical Detail — What Cid Needs to Verify
 
-This section specifies the PolarQuant algorithm concretely so Cid can verify that the community fork implements it correctly. A fork that gets the rotation wrong or uses the wrong codebook boundaries will compress successfully but degrade quality in ways that short PPL benchmarks may not catch — the damage surfaces during long production sessions with sustained context pressure.
+This section specifies the PolarQuant algorithm concretely so Cid can verify that the community fork implements it correctly. A fork that gets the rotation wrong or uses the wrong codebook boundaries will compress successfully but degrade quality in ways that short PPL benchmarks may not catch — the damage surfaces during long production sessions with sustained context pressure.
 
 ### The Algorithm (per KV vector)
 
-**Step 1 — Random Rotation (Preconditioning):**
+**Step 1 — Random Rotation (Preconditioning):**
 - Apply a fixed random orthogonal rotation to each KV vector before quantization.
-- The paper uses a **Walsh-Hadamard transform** (WHT) — a structured orthogonal matrix that's O(d log d) to apply, not O(d²) like a dense random matrix.
+- The paper uses a **Walsh-Hadamard transform** (WHT) — a structured orthogonal matrix that's O(d log d) to apply, not O(d²) like a dense random matrix.
 - **Why:** Raw KV vectors have non-uniform coordinate distributions (some dimensions carry more energy). WHT spreads energy uniformly across all coordinates, making the post-rotation distribution predictable and concentrated. This is what eliminates the need for per-vector normalization constants.
-- **Cid verification:** The fork must use a fixed WHT (or equivalent structured orthogonal rotation), not a learned or per-layer rotation. The rotation matrix must be identical at quantization and dequantization. If the fork uses a dense random matrix instead of WHT, it's functionally correct but slower — flag it.
+- **Cid verification:** The fork must use a fixed WHT (or equivalent structured orthogonal rotation), not a learned or per-layer rotation. The rotation matrix must be identical at quantization and dequantization. If the fork uses a dense random matrix instead of WHT, it's functionally correct but slower — flag it.
 
-**Step 2 — Polar Coordinate Transform:**
+**Step 2 — Polar Coordinate Transform:**
 - After rotation, decompose each vector into **radius** (L2 norm / signal strength) and **angle** (direction on the unit sphere).
-- The radius is stored at higher precision (FP16 or FP32) — it's one scalar per vector, negligible overhead.
+- The radius is stored at higher precision (FP16 or FP32) — it's one scalar per vector, negligible overhead.
 - The angle coordinates are what get quantized. Because WHT made their distribution predictable, you can use a fixed codebook without per-vector calibration.
 
-**Step 3 — Lloyd-Max Scalar Quantization:**
+**Step 3 — Lloyd-Max Scalar Quantization:**
 - Each angle coordinate is independently quantized using a **Lloyd-Max optimal scalar quantizer**.
 - Lloyd-Max minimizes mean squared error for a known distribution. Because WHT makes the distribution analytically computable, the codebook boundaries are **precomputed once** and fixed for all vectors.
 - **Codebook sizes by compression target:**
   - `turbo4` = 4 bits per coordinate = 16 codebook entries per dimension
   - `turbo3` = 3 bits = 8 entries
   - `turbo2` = 2 bits = 4 entries
-- **Cid verification:** Check that the fork's codebook boundaries match what the paper/PolarQuant paper specifies for the target distribution. If the fork uses uniform quantization instead of Lloyd-Max, that's a quality regression — uniform is simpler but wastes bits on low-probability regions.
+- **Cid verification:** Check that the fork's codebook boundaries match what the paper/PolarQuant paper specifies for the target distribution. If the fork uses uniform quantization instead of Lloyd-Max, that's a quality regression — uniform is simpler but wastes bits on low-probability regions.
 
-**Step 4 — Bit Packing + Storage:**
+**Step 4 — Bit Packing + Storage:**
 - Quantized indices are packed into the KV cache format (turbo2/3/4 nibble-packed).
-- Radius stored separately. No normalization constants, no scale factors, no zero-points — this is the key advantage over standard quantization.
+- Radius stored separately. No normalization constants, no scale factors, no zero-points — this is the key advantage over standard quantization.
 
 ### Dequantization During Attention
 
-When the model computes attention scores (Q·K^T) and weighted values (softmax·V):
+When the model computes attention scores (Q·K^T) and weighted values (softmax·V):
 1. Read packed indices from cache
 2. Look up codebook values (single table lookup per coordinate)
 3. Reconstruct angle coordinates
 4. Scale by stored radius
 5. Compute dot product in reconstructed space
 
-**Critical property:** The inner product between a full-precision query Q and a PolarQuant-compressed K must be an unbiased estimator of the true Q·K dot product. The WHT rotation preserves this because orthogonal transforms preserve inner products. If the fork adds any non-orthogonal transformation (e.g., learned projection, PCA), the unbiasedness guarantee breaks.
+**Critical property:** The inner product between a full-precision query Q and a PolarQuant-compressed K must be an unbiased estimator of the true Q·K dot product. The WHT rotation preserves this because orthogonal transforms preserve inner products. If the fork adds any non-orthogonal transformation (e.g., learned projection, PCA), the unbiasedness guarantee breaks.
 
-### PolarQuant Initialization — Codebook + Rotation Matrix Setup
+### PolarQuant Initialization — Codebook + Rotation Matrix Setup
 
 PolarQuant requires two things to be initialized before inference can start:
 
-1. **Walsh-Hadamard rotation matrix:** This is deterministic — a WHT of size d (model head dimension, typically 128) is computed from the recursive Hadamard construction. It's the same for every session, every model. Compute once at model load, store in memory. Cost: O(d log d) per head dimension — microseconds. No impact on model load time.
+1. **Walsh-Hadamard rotation matrix:** This is deterministic — a WHT of size d (model head dimension, typically 128) is computed from the recursive Hadamard construction. It's the same for every session, every model. Compute once at model load, store in memory. Cost: O(d log d) per head dimension — microseconds. No impact on model load time.
 
 2. **Lloyd-Max codebook:** The quantization boundaries are precomputed for the known post-WHT distribution. For a given bit width (turbo4 = 4 bits = 16 entries), the codebook is a fixed lookup table of 16 boundary values + 16 reconstruction values. This is identical across sessions and models of the same head dimension. Can be hardcoded as a constant array or computed once at load time from the analytical distribution formula.
 
-**Expected initialization overhead:** Negligible — both are small deterministic computations. But **measure it during Phase 1**: time the gap between Ollama receiving a request and the first token appearing, with and without TurboQuant. If initialization adds >1 second to cold model load, investigate caching the tables to disk alongside the model file.
+**Expected initialization overhead:** Negligible — both are small deterministic computations. But **measure it during Phase 1**: time the gap between Ollama receiving a request and the first token appearing, with and without TurboQuant. If initialization adds >1 second to cold model load, investigate caching the tables to disk alongside the model file.
 
 **Cid measurement target:** Report model load time (cold start) with and without TurboQuant. If >5 second delta, flag as UX issue.
 
@@ -490,9 +475,9 @@ PolarQuant requires two things to be initialized before inference can start:
 
 ---
 
-## 1. Model Targeting — What Can We Run?
+## 1. Model Targeting — What Can We Run?
 
-### Memory Budget — Realistic, Not Theoretical
+### Memory Budget — Realistic, Not Theoretical
 
 On a 32GB M4 Max running macOS, you do NOT have 32GB for inference. Realistic budget:
 
@@ -504,18 +489,18 @@ On a 32GB M4 Max running macOS, you do NOT have 32GB for inference. Realistic bu
 | Activation memory (intermediate tensors during forward pass) | ~1-3GB (varies by model/batch) |
 | **Available for model weights + KV cache** | **~26-28GB** |
 
-**Use 27GB as the planning ceiling.** The v1 spec said "leaves 2GB for OS" at 30GB peak — that's too tight. All memory calculations below use 27GB available.
+**Use 27GB as the planning ceiling.** The v1 spec said "leaves 2GB for OS" at 30GB peak — that's too tight. All memory calculations below use 27GB available.
 
 ### Current State (No TurboQuant)
-- **qwen3.5:27b** at Q4_K_M (~16GB model weights) — fits within 27GB budget with room for KV cache
-- At 32K context, KV cache for a 27B model at FP16 ≈ 4-6GB → total ~20-22GB. Comfortable.
-- At 64K context, KV cache ≈ 8-12GB → total ~24-28GB. Marginal — may swap.
-- At 128K context, KV cache grows to ~16-24GB → doesn't fit. Context-limited.
+- **qwen3.5:27b** at Q4_K_M (~16GB model weights) — fits within 27GB budget with room for KV cache
+- At 32K context, KV cache for a 27B model at FP16 ≈ 4-6GB → total ~20-22GB. Comfortable.
+- At 64K context, KV cache ≈ 8-12GB → total ~24-28GB. Marginal — may swap.
+- At 128K context, KV cache grows to ~16-24GB → doesn't fit. Context-limited.
 
-### With TurboQuant (4× KV Compression)
-- KV cache at 32K drops from ~5GB → ~1.2GB
-- KV cache at 64K drops from ~10GB → ~2.5GB
-- KV cache at 128K drops from ~20GB → ~5GB
+### With TurboQuant (4× KV Compression)
+- KV cache at 32K drops from ~5GB → ~1.2GB
+- KV cache at 64K drops from ~10GB → ~2.5GB
+- KV cache at 128K drops from ~20GB → ~5GB
 - This frees 4-15GB of headroom depending on context length
 
 **Important:** These are calculated estimates, not measured values. Actual memory consumption can exceed theoretical due to fragmentation, allocation overhead, and implementation-specific buffering. Phase 1 **must** include actual peak memory measurement (see validation section). If measured exceeds calculated by >15%, the context ceiling drops accordingly.
@@ -524,31 +509,31 @@ On a 32GB M4 Max running macOS, you do NOT have 32GB for inference. Realistic bu
 
 **Primary target: qwen3.5:27b at Q4_K_M with extended context**
 - Model weights: ~16GB at Q4_K_M
-- With TurboQuant KV cache at 64K context: ~2.5GB cache + ~2GB activations → ~20-21GB total. Comfortable within 27GB budget.
-- With TurboQuant at 128K: ~5GB cache + ~2GB activations → ~23GB total. Fits, but tight — **needs measured validation.**
-- Without TurboQuant: 64K context KV cache ≈ 10GB → ~28GB total. OOM risk.
+- With TurboQuant KV cache at 64K context: ~2.5GB cache + ~2GB activations → ~20-21GB total. Comfortable within 27GB budget.
+- With TurboQuant at 128K: ~5GB cache + ~2GB activations → ~23GB total. Fits, but tight — **needs measured validation.**
+- Without TurboQuant: 64K context KV cache ≈ 10GB → ~28GB total. OOM risk.
 - **Win: 64K context becomes reliable, 128K becomes possible.** This is the real unlock.
 
 **Stretch target: Qwen 3.5 32B (Q4_K_M)**
 - Model weights: ~18-19GB at Q4_K_M
-- With TurboQuant at 64K: ~2.5GB cache + ~2.5GB activations → ~23-24GB. Fits within 27GB but leaves only ~3GB headroom.
+- With TurboQuant at 64K: ~2.5GB cache + ~2.5GB activations → ~23-24GB. Fits within 27GB but leaves only ~3GB headroom.
 - **Verdict: worth testing in Phase 1 benchmarks alongside 27B.** If it fits, marginally better quality. If it's marginal, stay on 27B.
 
 **Not recommended: Qwen 3.5 72B (Q2_K or IQ3_XXS)**
 - Model weights at Q2_K: ~27GB. Leaves ~0GB for anything else.
-- **Verdict: does not fit.** Even with TurboQuant, no room for KV cache + activations + Metal overhead. And quality at Q2_K is poor — weight quantization damage cancels the parameter count advantage.
+- **Verdict: does not fit.** Even with TurboQuant, no room for KV cache + activations + Metal overhead. And quality at Q2_K is poor — weight quantization damage cancels the parameter count advantage.
 
 **Recommended path: Stay on 27B class, use TurboQuant to unlock longer context (64K-128K) rather than a bigger model.** The real win on 32GB unified is context length, not parameter count. A 27B model at 128K context with TurboQuant beats a 72B at Q2 with 8K context.
 
-**Alternative worth testing: Mistral/Codestral 25B-class models** at Q5_K_M (~18GB) with TurboQuant. Locke's research notes TurboQuant was benchmarked on Mistral — community results may be more reproducible.
+**Alternative worth testing: Mistral/Codestral 25B-class models** at Q5_K_M (~18GB) with TurboQuant. Locke's research notes TurboQuant was benchmarked on Mistral — community results may be more reproducible.
 
 ---
 
-## 2. Implementation Path — PolarQuant First, Then Full TurboQuant
+## 2. Implementation Path — PolarQuant First, Then Full TurboQuant
 
 **Recommendation: PolarQuant (Stage 1) first.** Matches Locke's recommendation. Rationale:
 
-- PolarQuant alone delivers ~4.2× compression — that's the bulk of the win
+- PolarQuant alone delivers ~4.2× compression — that's the bulk of the win
 - Full TurboQuant adds QJL residual correction for marginal quality improvement at extreme compression (2.5 bits)
 - At 3.5+ bits/channel, PolarQuant is sufficient for zero accuracy loss
 - QJL adds kernel complexity for small incremental gain at our target compression ratio
@@ -558,36 +543,36 @@ On a 32GB M4 Max running macOS, you do NOT have 32GB for inference. Realistic bu
 
 | Repo | What | Why | Risk |
 |------|------|-----|------|
-| **`TheTom/llama-cpp-turboquant`** | `llama.cpp` fork with Metal support | Most directly useful — same stack as Ollama. Reports PPL numbers on M-series. | Community fork, not upstream. May lag `llama.cpp` HEAD. |
+| **`TheTom/llama-cpp-turboquant`** | `llama.cpp` fork with Metal support | Most directly useful — same stack as Ollama. Reports PPL numbers on M-series. | Community fork, not upstream. May lag `llama.cpp` HEAD. |
 | **`TheTom/turboquant_plus`** | Standalone C implementation + Python tests | Most detailed reverse-engineering. 511+ tests. PolarQuant + Walsh-Hadamard + turbo2/3/4 formats. | Extends beyond paper ("Plus"). May include non-paper innovations. |
-| **`amirzandieh/QJL`** | Author's QJL CUDA implementation | Official author code. CUDA kernels, eval scripts, LongBench commands. | CUDA only — needs Metal port for MacBook. Phase 2 dependency. |
+| **`amirzandieh/QJL`** | Author's QJL CUDA implementation | Official author code. CUDA kernels, eval scripts, LongBench commands. | CUDA only — needs Metal port for MacBook. Phase 2 dependency. |
 | **`rachittshah/mlx-turboquant`** | MLX proof-of-concept | Native Apple Silicon. Correct module layout (codebooks, polar_quant, qjl). | May be partial implementation. Naming drift noted. |
 
 **Start from:** `TheTom/llama-cpp-turboquant` (for Ollama integration path) + `TheTom/turboquant_plus` (for reference/tests).
 
 ### Community Fork Risk Assessment
 
-The v1 spec understated this. Community `llama.cpp` forks can diverge significantly from HEAD, especially in the Metal backend where Apple Silicon optimizations change frequently. The risk isn't "it doesn't build" — it's "it builds fine on the fork's base commit but breaks when cherry-picked onto current HEAD."
+The v1 spec understated this. Community `llama.cpp` forks can diverge significantly from HEAD, especially in the Metal backend where Apple Silicon optimizations change frequently. The risk isn't "it doesn't build" — it's "it builds fine on the fork's base commit but breaks when cherry-picked onto current HEAD."
 
 **Specific risk areas:**
 - **KV cache layer:** `llama.cpp` has refactored KV cache internals multiple times in 2026. A fork based on a 4-week-old commit may touch structs/functions that have been renamed or restructured upstream.
 - **Metal shaders:** Apple Silicon Metal optimizations are actively changing. Custom Metal kernels for TurboQuant dequant may conflict with upstream shader refactors.
 - **Memory management:** `ggml` memory allocation has evolved. The fork's cache allocation assumptions may not match current `ggml` memory pools.
 
-**Mitigation plan (Phase 1 Step 0 — before any benchmarking):**
+**Mitigation plan (Phase 1 Step 0 — before any benchmarking):**
 
 1. **Check fork freshness:** `git log --oneline -1` on the fork. Compare base commit date against `llama.cpp` HEAD. If >4 weeks stale, flag as HIGH risk.
 2. **If fresh (< 2 weeks from HEAD):** Build directly. Likely works.
-3. **If stale (2-4 weeks):** Attempt cherry-pick of TurboQuant-specific commits onto current HEAD. If merge conflicts are limited to TurboQuant files → resolve manually. If conflicts touch core KV cache / Metal code → stop, evaluate effort.
-4. **If very stale (> 4 weeks) or conflicts are extensive:** Switch to **clean-room approach** — use `TheTom/turboquant_plus` as the algorithm reference and implement the KV cache types directly into current `llama.cpp` HEAD. This is more work (~60-90 min instead of ~20-40 min) but avoids the merge conflict maze.
+3. **If stale (2-4 weeks):** Attempt cherry-pick of TurboQuant-specific commits onto current HEAD. If merge conflicts are limited to TurboQuant files → resolve manually. If conflicts touch core KV cache / Metal code → stop, evaluate effort.
+4. **If very stale (> 4 weeks) or conflicts are extensive:** Switch to **clean-room approach** — use `TheTom/turboquant_plus` as the algorithm reference and implement the KV cache types directly into current `llama.cpp` HEAD. This is more work (~60-90 min instead of ~20-40 min) but avoids the merge conflict maze.
 5. **Escape hatch:** If `llama.cpp` path is blocked, fall back to `rachittshah/mlx-turboquant` (MLX native, no fork divergence risk, but requires API proxy for Ollama compatibility).
 
-**Cid decision point:** After Step 0, report fork age + conflict assessment before proceeding. If clean-room is needed, update the time estimate and Frankie adjusts the schedule. Don't spend more than 15 minutes fighting merge conflicts — switch to clean-room.
+**Cid decision point:** After Step 0, report fork age + conflict assessment before proceeding. If clean-room is needed, update the time estimate and Frankie adjusts the schedule. Don't spend more than 15 minutes fighting merge conflicts — switch to clean-room.
 
-### Metal Kernel Risk — The Single Highest-Risk Assumption
+### Metal Kernel Risk — The Single Highest-Risk Assumption
 
-The spec assumes the `llama.cpp` fork has working **Metal shaders** for PolarQuant KV dequantization. KV dequant happens in the attention computation hot path — every token, every layer, every head. If the fork only has CPU or CUDA dequant kernels and no Metal implementation, the MacBook will either:
-- Fall back to CPU dequant → **catastrophic** performance loss (10-50× slower attention)
+The spec assumes the `llama.cpp` fork has working **Metal shaders** for PolarQuant KV dequantization. KV dequant happens in the attention computation hot path — every token, every layer, every head. If the fork only has CPU or CUDA dequant kernels and no Metal implementation, the MacBook will either:
+- Fall back to CPU dequant → **catastrophic** performance loss (10-50× slower attention)
 - Fail to build entirely for Metal backend
 
 **Cid's actual first action** (before building, before benchmarking, before anything):
@@ -612,12 +597,12 @@ This check takes 2 minutes and determines the entire build strategy. Do it first
 
 ---
 
-## 3. Integration Target — llama.cpp → Ollama
+## 3. Integration Target — llama.cpp → Ollama
 
-**Primary: `llama.cpp` fork → custom Ollama build.**
+**Primary: `llama.cpp` fork → custom Ollama build.**
 
 Why not MLX:
-- Our entire fleet uses Ollama. Model management, API compatibility, endpoint routing — all built around Ollama.
+- Our entire fleet uses Ollama. Model management, API compatibility, endpoint routing — all built around Ollama.
 - MLX would require a separate inference server, separate model format, separate API integration.
 - Ollama is built on `llama.cpp`/`ggml`. KV cache changes in `llama.cpp` propagate to Ollama.
 
@@ -626,13 +611,13 @@ Why not MLX:
 2. Validate quality + performance
 3. Build custom Ollama from our `llama.cpp` fork (Ollama builds `llama.cpp` as a submodule)
 4. Deploy to MacBook as replacement Ollama binary
-5. Existing model files, API, and endpoint (`10.0.0.133:11434`) remain identical — only the inference engine changes
+5. Existing model files, API, and endpoint (`10.0.0.133:11434`) remain identical — only the inference engine changes
 
 **Fallback: MLX standalone** if `llama.cpp` Metal integration proves too complex. `rachittshah/mlx-turboquant` as starting point. Would require a small proxy server to maintain API compatibility with our Ollama endpoint.
 
 ---
 
-## 4. Validation Plan — How We Know It Works
+## 4. Validation Plan — How We Know It Works
 
 ### Quality Validation
 
@@ -640,24 +625,24 @@ Why not MLX:
 
 | Test | What It Measures | Tool | Pass Criteria |
 |------|-----------------|------|--------------|
-| Perplexity (PPL) | Overall language modeling quality | `llama-perplexity` on WikiText-2 | PPL delta ≤ 0.5 from baseline (FP16 KV) |
+| Perplexity (PPL) | Overall language modeling quality | `llama-perplexity` on WikiText-2 | PPL delta ≤ 0.5 from baseline (FP16 KV) |
 | Needle-in-Haystack | Long context retrieval | Custom prompt at 8K/16K/32K/64K/128K | 100% retrieval at all lengths where baseline passes |
-| Practical generation | Subjective quality | 10 predefined prompts (see test suite below) | Human review: no degradation on ≥9/10 |
-| Attention score accuracy | Inner product preservation | Cosine similarity between TurboQuant and FP16 attention weights | cosine sim ≥ 0.995 |
+| Practical generation | Subjective quality | 10 predefined prompts (see test suite below) | Human review: no degradation on ≥9/10 |
+| Attention score accuracy | Inner product preservation | Cosine similarity between TurboQuant and FP16 attention weights | cosine sim ≥ 0.995 |
 
 **Predefined Test Prompts (10 prompts, run identically on TurboQuant and FP16 KV baseline):**
 
 | # | Category | Prompt Description | What It Tests |
 |---|----------|-------------------|---------------|
-| 1 | Long-context summarization | Feed 20K tokens of a research paper, ask for structured summary with citations | KV cache quality at length — compressed K/V must retain source detail |
+| 1 | Long-context summarization | Feed 20K tokens of a research paper, ask for structured summary with citations | KV cache quality at length — compressed K/V must retain source detail |
 | 2 | Multi-step reasoning | 5-step math word problem requiring chain-of-thought | Whether compressed KV degrades intermediate reasoning steps |
-| 3 | Code generation | Write a Python script with 3 functions, error handling, type hints | Precise token prediction — code is unforgiving of subtle quality drops |
-| 4 | Code debugging | Provide buggy code (3 bugs), ask to identify and fix all three | Attention to detail across context — must reference earlier code correctly |
+| 3 | Code generation | Write a Python script with 3 functions, error handling, type hints | Precise token prediction — code is unforgiving of subtle quality drops |
+| 4 | Code debugging | Provide buggy code (3 bugs), ask to identify and fix all three | Attention to detail across context — must reference earlier code correctly |
 | 5 | Factual recall (early context) | Provide 10 facts in the first 1K tokens, continue for 8K tokens of filler, ask about fact #3 | Retrieval from early context through compressed KV |
 | 6 | Creative writing | Write a 500-word short story with specific constraints (setting, character, twist) | Compression artifacts surface as repetition or coherence loss |
 | 7 | Multi-turn conversation | 10-turn technical Q&A where later questions reference earlier answers | Cross-turn coherence through accumulated compressed KV |
-| 8 | Structured output | Generate a JSON schema with 15+ fields, nested objects, and validation rules | Format precision — compressed KV must maintain structural consistency |
-| 9 | Translation + analysis | Translate a paragraph EN→ES, then analyze the translation choices | Tests both generation quality and meta-reasoning about own output |
+| 8 | Structured output | Generate a JSON schema with 15+ fields, nested objects, and validation rules | Format precision — compressed KV must maintain structural consistency |
+| 9 | Translation + analysis | Translate a paragraph EN→ES, then analyze the translation choices | Tests both generation quality and meta-reasoning about own output |
 | 10 | Instruction following | Complex prompt with 8 specific formatting requirements (headers, bullet style, word limits, etc.) | Whether compression causes the model to "forget" constraints mid-generation |
 
 **Prompts must be written and saved to `projects/sovereign-stack/turboquant-test-prompts.md` before Phase 2 benchmarks run.** Same prompts, same order, both configurations. This prevents unconscious cherry-picking.
@@ -665,7 +650,7 @@ Why not MLX:
 **Asymmetric K/V test:** Run K at Q8_0, V at turbo4. Community reports this works well on sensitive models. Compare PPL vs symmetric turbo4 K+V.
 
 **Long-session quality test (Phase 2 only):** Short-context PPL benchmarks can miss quality degradation that surfaces during sustained context pressure. During Phase 2, run one extended production simulation:
-- Generate a 50-turn multi-step reasoning conversation (code gen → debug → refactor → test → iterate)
+- Generate a 50-turn multi-step reasoning conversation (code gen → debug → refactor → test → iterate)
 - Compare output quality vs same conversation on FP16 KV baseline
 - Specifically watch for: coherence drift after turn 30+, hallucinated references to earlier context, attention score softmax concentration (if measurable)
 - This catches the case where codebook boundary errors accumulate over many KV cache writes in a single session
@@ -674,16 +659,16 @@ Why not MLX:
 
 | Metric | Measure | Pass Criteria |
 |--------|---------|--------------|
-| Tokens/second (generation) | `llama-bench` | ≥90% of baseline tok/s (small decode overhead acceptable) |
-| Time to first token (TTFT) | Timed prompt eval | ≤110% of baseline |
+| Tokens/second (generation) | `llama-bench` | ≥90% of baseline tok/s (small decode overhead acceptable) |
+| Time to first token (TTFT) | Timed prompt eval | ≤110% of baseline |
 | Peak resident memory | `footprint -p <pid>` or `vmmap --summary` at each context length | Stays under 27GB at target context length |
-| Memory vs theoretical | Compare measured peak to calculated estimate | If measured exceeds calculated by >15% → reduce context ceiling |
+| Memory vs theoretical | Compare measured peak to calculated estimate | If measured exceeds calculated by >15% → reduce context ceiling |
 | Context length ceiling | Binary search: max context before OOM or swap pressure | 64K minimum (vs ~32K baseline for 27B) |
 
 ### Kill Criteria
-- PPL regression > 1.0 at any compression level → abort that compression level
-- OOM at 32K context (baseline capability) → regression, abort
-- tok/s drops > 25% → dequant overhead too high, need kernel optimization before deploy
+- PPL regression > 1.0 at any compression level → abort that compression level
+- OOM at 32K context (baseline capability) → regression, abort
+- tok/s drops > 25% → dequant overhead too high, need kernel optimization before deploy
 
 ---
 
@@ -691,7 +676,7 @@ Why not MLX:
 
 | Role | Owner | What |
 |------|-------|------|
-| Build spec | Strago | This document ✅ |
+| Build spec | Strago | This document ✅ |
 | Implementation | Cid | Fork `llama.cpp`, integrate PolarQuant KV cache, Metal kernels, build custom Ollama |
 | Validation | Cid | Run test matrix, report PPL/performance numbers |
 | Model selection | Cid | Test qwen3.5:27b + one Mistral variant, recommend best config |
@@ -703,48 +688,48 @@ Why not MLX:
 
 ## 6. Phasing
 
-### Phase 1 — PolarQuant MVP (Target: this week)
+### Phase 1 — PolarQuant MVP (Target: this week)
 
 **Scope:**
 
-**Step 0 — Fork Assessment (do this FIRST, report before proceeding):**
+**Step 0 — Fork Assessment (do this FIRST, report before proceeding):**
 - Clone `TheTom/llama-cpp-turboquant`
 - Check base commit age vs `llama.cpp` HEAD (`git log --oneline -1`)
 - Check `sysctl hw.memsize` on MacBook (resolve the 32/36/48GB question)
-- If fork < 2 weeks stale → proceed to build
-- If 2-4 weeks stale → attempt cherry-pick, report conflict scope
-- If > 4 weeks or conflicts extensive → switch to clean-room (see Fork Risk Assessment above)
+- If fork < 2 weeks stale → proceed to build
+- If 2-4 weeks stale → attempt cherry-pick, report conflict scope
+- If > 4 weeks or conflicts extensive → switch to clean-room (see Fork Risk Assessment above)
 - Report: fork age, conflict assessment, MacBook actual RAM, estimated build path time
 
-**Step 1 — Build + Verify:**
+**Step 1 — Build + Verify:**
 - Build `llama.cpp` fork (or clean-room) with Metal backend on MacBook (M4 Max)
 - Run the Section 1a verification checklist against the fork's implementation before trusting any benchmarks
 - Run FP16 KV baseline: `llama-perplexity` on WikiText-2 with `qwen3.5:27b` at 8K context (this is the number we're comparing against)
 
-**Step 2 — Benchmark PolarQuant:**
+**Step 2 — Benchmark PolarQuant:**
 - Run perplexity test with PolarQuant KV (turbo4 format) vs FP16 KV baseline
 - Run `llama-bench` for tok/s comparison
 - Test at 8K, 32K, and 64K context lengths
 - Run asymmetric test: K at Q8_0, V at turbo4
-- **Measure actual peak resident memory** at each context length (`footprint -p <pid>` or `vmmap --summary`). Compare measured vs calculated. If measured exceeds calculated by >15%, note the delta — it reduces the achievable context ceiling.
+- **Measure actual peak resident memory** at each context length (`footprint -p <pid>` or `vmmap --summary`). Compare measured vs calculated. If measured exceeds calculated by >15%, note the delta — it reduces the achievable context ceiling.
 - Report: PPL delta per context length, tok/s delta, **measured peak memory per context length**, max context before OOM/swap, asymmetric vs symmetric results
 
 **Deliverable:** Working `llama.cpp` build on MacBook with PolarQuant KV cache. PPL + performance numbers. Section 1a verification checklist completed.
 
 **Estimated Cid time (honest range):**
-- **Best case** — fork is fresh, builds clean on first try, Metal shaders work: 20-40 min
-- **Typical case** — fork needs CMake flag tweaks, Xcode SDK adjustments, minor Metal fixes: 1-2 hours
-- **Worst case** — fork is stale, conflicts extensive, or Metal shaders missing: clean-room build 2-4 hours, or MLX pivot
+- **Best case** — fork is fresh, builds clean on first try, Metal shaders work: 20-40 min
+- **Typical case** — fork needs CMake flag tweaks, Xcode SDK adjustments, minor Metal fixes: 1-2 hours
+- **Worst case** — fork is stale, conflicts extensive, or Metal shaders missing: clean-room build 2-4 hours, or MLX pivot
 
-**2-hour build troubleshooting cap:** If the `llama.cpp` fork doesn't compile and pass a basic smoke test (load model, generate 10 tokens) within 2 hours of troubleshooting, stop. Pivot to MLX path. Don't sink more time into Xcode/CMake/Metal debug loops when a working MLX PoC exists. Report what broke — the information is useful even if the path is abandoned.
+**2-hour build troubleshooting cap:** If the `llama.cpp` fork doesn't compile and pass a basic smoke test (load model, generate 10 tokens) within 2 hours of troubleshooting, stop. Pivot to MLX path. Don't sink more time into Xcode/CMake/Metal debug loops when a working MLX PoC exists. Report what broke — the information is useful even if the path is abandoned.
 
-**Decision gate:** If PPL delta ≤ 0.5 and tok/s ≥ 90% baseline AND Section 1a checklist passes → proceed to Phase 2. If PPL fails but checklist passes → try asymmetric K/V or lower compression (turbo3 instead of turbo4). If checklist fails → fix implementation before trusting benchmarks.
+**Decision gate:** If PPL delta ≤ 0.5 and tok/s ≥ 90% baseline AND Section 1a checklist passes → proceed to Phase 2. If PPL fails but checklist passes → try asymmetric K/V or lower compression (turbo3 instead of turbo4). If checklist fails → fix implementation before trusting benchmarks.
 
-### Phase 2 — Ollama Integration + Production Deploy
+### Phase 2 — Ollama Integration + Production Deploy
 
 **Scope:**
 
-**Step 0 — Ollama API Compatibility Check (before building):**
+**Step 0 — Ollama API Compatibility Check (before building):**
 Ollama pins a specific `llama.cpp` commit and calls it through CGo bindings in `llm/`. If our fork changes any function signatures, struct layouts, or enum values that Ollama's Go code references, the build will either fail or produce subtle runtime bugs.
 
 ```bash
@@ -776,7 +761,7 @@ If API surface differs: check if TurboQuant changes are additive (new functions/
 
 **Estimated Cid time:** 15-25 min (Ollama build is straightforward once `llama.cpp` fork is validated).
 
-### Phase 2.5 — Per-Layer Quantization Profiles (Optimization, Optional)
+### Phase 2.5 — Per-Layer Quantization Profiles (Optimization, Optional)
 
 Not all transformer layers have equal sensitivity to KV cache quantization. Research and community experimentation show early layers (first 2-4) and late layers (last 2-4) tend to be more sensitive than middle layers. If the fork supports per-layer KV cache type configuration:
 
@@ -789,19 +774,19 @@ This gives the same average compression ratio as uniform turbo4 but concentrates
 
 **Cid note:** During Phase 1, check whether the fork exposes per-layer KV type config. If it does, note it for later. Don't implement it yet.
 
-### Phase 3 — QJL Residual Correction (Optional)
+### Phase 3 — QJL Residual Correction (Optional)
 
 **Scope:** Add QJL 1-bit residual correction for full TurboQuant behavior. Only pursue if:
 - Phase 1/2 PolarQuant shows quality gaps at extreme compression (< 3 bits/channel)
 - We want to push to 2.5 bits/channel for even more context headroom
 
-**Source:** `amirzandieh/QJL` repo (CUDA → Metal port needed)
+**Source:** `amirzandieh/QJL` repo (CUDA → Metal port needed)
 
 **Estimated Cid time:** 30-60 min (Metal port of QJL kernels is real engineering work)
 
 **Decision gate:** Only proceed if PolarQuant alone doesn't meet quality bar at target compression.
 
-### Phase 4 — Upstream Watch
+### Phase 4 — Upstream Watch
 
 **Scope:** Monitor `llama.cpp` upstream and Ollama for official TurboQuant support. When it lands:
 - Evaluate upstream implementation vs our fork
@@ -814,10 +799,10 @@ This gives the same average compression ratio as uniform turbo4 but concentrates
 
 ## What This Spec Does NOT Cover
 
-- **Weight quantization** — TurboQuant is KV cache compression only. Model weight quantization (GGUF Q4_K_M etc.) is a separate concern and already handled by Ollama.
-- **Predator (desktop) deployment** — this spec targets MacBook only. Predator runs NVIDIA (CUDA) which is a different kernel backend. Can extend later.
-- **Multi-model serving** — TurboQuant helps with single-model memory but doesn't change Ollama's single-model-at-a-time constraint.
-- **Ollama upstream contribution** — out of scope for now. We build for ourselves first.
+- **Weight quantization** — TurboQuant is KV cache compression only. Model weight quantization (GGUF Q4_K_M etc.) is a separate concern and already handled by Ollama.
+- **Predator (desktop) deployment** — this spec targets MacBook only. Predator runs NVIDIA (CUDA) which is a different kernel backend. Can extend later.
+- **Multi-model serving** — TurboQuant helps with single-model memory but doesn't change Ollama's single-model-at-a-time constraint.
+- **Ollama upstream contribution** — out of scope for now. We build for ourselves first.
 
 ---
 
@@ -825,7 +810,7 @@ This gives the same average compression ratio as uniform turbo4 but concentrates
 
 **None blocking.** One informational:
 
-- **MacBook Pro memory:** Confirmed M4 Max 32GB from memory/2026-03-14.md. If it's actually 36GB or 48GB (M4 Max comes in 36/48/128 configs), that changes the model ceiling. Can Cid check `sysctl hw.memsize` on the MacBook during Phase 1? Non-blocking — doesn't change the approach, just the model size ceiling.
+- **MacBook Pro memory:** Confirmed M4 Max 32GB from memory/2026-03-14.md. If it's actually 36GB or 48GB (M4 Max comes in 36/48/128 configs), that changes the model ceiling. Can Cid check `sysctl hw.memsize` on the MacBook during Phase 1? Non-blocking — doesn't change the approach, just the model size ceiling.
 
 ---
 
@@ -850,8 +835,8 @@ This gives the same average compression ratio as uniform turbo4 but concentrates
 
 - **v1 (2026-03-30 12:26 ET):** Initial spec.
 - **v2 (2026-03-30 12:55 ET):** Added Section 1a (PolarQuant technical detail + Cid verification checklist), expanded fork risk assessment with mitigation plan, added Phase 1 Step 0 (fork assessment before benchmarking), added long-session quality test for Phase 2, updated Phase 1 time estimate for clean-room path. Changes driven by external Opus review round 1.
-- **v2.1 (2026-03-30 13:00 ET):** Added Metal kernel risk check (grep before build — determines llama.cpp vs MLX primary path), corrected memory budget (27GB available, not 30GB — accounts for OS + Metal driver + activations), added measured memory profiling requirement to Phase 1, added Ollama CGo API compatibility check to Phase 2 Step 0, tightened model ceiling estimates. Changes driven by external Opus review round 2.
-- **v2.2 (2026-03-30 13:05 ET):** Added honest time estimate range (20 min best → 2-4 hr worst), 2-hour build troubleshooting cap before MLX pivot, PolarQuant initialization detail (WHT + Lloyd-Max codebook setup + cold-start measurement target), 10 predefined test prompts with rationale (prevents cherry-picking), per-layer quantization profiles as Phase 2.5 optimization path. Changes driven by external Opus review round 3.
+- **v2.1 (2026-03-30 13:00 ET):** Added Metal kernel risk check (grep before build — determines llama.cpp vs MLX primary path), corrected memory budget (27GB available, not 30GB — accounts for OS + Metal driver + activations), added measured memory profiling requirement to Phase 1, added Ollama CGo API compatibility check to Phase 2 Step 0, tightened model ceiling estimates. Changes driven by external Opus review round 2.
+- **v2.2 (2026-03-30 13:05 ET):** Added honest time estimate range (20 min best → 2-4 hr worst), 2-hour build troubleshooting cap before MLX pivot, PolarQuant initialization detail (WHT + Lloyd-Max codebook setup + cold-start measurement target), 10 predefined test prompts with rationale (prevents cherry-picking), per-layer quantization profiles as Phase 2.5 optimization path. Changes driven by external Opus review round 3.
 
 ---
 

docs/STATUS_TRACKER.md

@@ -1,60 +0,0 @@
-# TurboQuant Living Status Tracker
-
-Updated on each milestone. See PROJECT_STATUS.md for detailed phase reports.
-
-## Quick Status
-
-| Phase | Status | Last Updated | Issue |
-|-------|--------|-------------|-------|
-| Phase 1: PolarQuant MVP | DONE | 2026-03-30 | #17 |
-| Phase 2: KV Cache Compression | IN PROGRESS | 2026-04-15 | #99 |
-| Edge Crisis Detection | DONE | 2026-04-15 | #102 |
-| Integration PR (upstream llama.cpp) | NOT STARTED | — | — |
-| QJL Quantization | NOT STARTED | — | — |
-| Ollama Integration | NOT STARTED | — | — |
-| Benchmark Suite | IN PROGRESS | 2026-04-13 | #12 |
-
-## Phase Details
-
-### Phase 1: PolarQuant MVP — COMPLETE
-- PolarQuant KV cache compression working on Apple Silicon
-- 73% KV memory savings, 1% prompt overhead, 11% generation overhead
-- Metal shaders: flash attention, WHT rotation, codebooks
-- Hardware: M3 Max 36GB (corrected from spec)
-- Gate Check #2: PASSED
-
-### Phase 2: Edge Deployment — COMPLETE
-- Crisis detection on edge devices (Pi 4, old phones)
-- Keyword + model (gemma2:2b) + offline resources
-- Deployment guide, model selection, resource cache
-- See docs/edge-crisis-deployment.md
-
-### Phase 3: Upstream Integration — NOT STARTED
-- PR to llama.cpp for turbo quantization
-- Depends on Phase 2 benchmarks
-
-### Phase 4: QJL — NOT STARTED
-- Johnson-Lindenstrauss quantization
-- Lower memory than PolarQuant
-- Research phase
-
-## Recent Milestones
-
-| Date | Milestone | PR/Issue |
-|------|-----------|----------|
-| 2026-04-15 | Edge crisis detection deployed | #102 / PR #111 |
-| 2026-04-14 | KV cache compression profiles | PR #68 |
-| 2026-04-13 | Benchmark suite expanded | #12 / #39 |
-| 2026-03-30 | Phase 1 complete: PolarQuant MVP | #17 |
-
-## Open Blockers
-
-| Blocker | Impact | Issue |
-|---------|--------|-------|
-| None currently | — | — |
-
----
-
-*Last auto-updated: 2026-04-15*
-*This file is the single source of truth for project status.*
-*Update it on every milestone merge.*

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, not the q4_0 fallback.
+        chosen = max(QUANT_LEVELS, key=lambda level: level.compression_ratio)
+        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_dflash_apple_silicon.py

@@ -0,0 +1,58 @@
+#!/usr/bin/env python3
+"""Tests for Apple Silicon DFlash benchmark planning helpers (issue #152)."""
+
+import os
+import sys
+from unittest.mock import patch
+
+sys.path.insert(0, os.path.dirname(os.path.dirname(__file__)))
+
+from benchmarks.dflash_apple_silicon import (  # noqa: E402
+    build_mlx_benchmark_command,
+    detect_total_memory_gb,
+    render_report_template,
+    select_pair,
+)
+
+
+class TestPairSelection:
+    def test_prefers_qwen35_9b_on_36gb_mac(self):
+        pair = select_pair(total_memory_gb=36)
+        assert pair.slug == "qwen35-9b"
+        assert pair.base_model == "Qwen/Qwen3.5-9B"
+        assert pair.draft_model == "z-lab/Qwen3.5-9B-DFlash"
+
+    def test_falls_back_to_4b_when_memory_is_tight(self):
+        pair = select_pair(total_memory_gb=20)
+        assert pair.slug == "qwen35-4b"
+        assert pair.base_model == "Qwen/Qwen3.5-4B"
+
+
+class TestCommandGeneration:
+    def test_builds_upstream_mlx_benchmark_command(self):
+        pair = select_pair(total_memory_gb=36)
+        command = build_mlx_benchmark_command(pair, dataset="gsm8k", max_samples=64)
+        assert "python -m dflash.benchmark --backend mlx" in command
+        assert "--model Qwen/Qwen3.5-9B" in command
+        assert "--draft-model z-lab/Qwen3.5-9B-DFlash" in command
+        assert "--dataset gsm8k" in command
+        assert "--max-samples 64" in command
+        assert "--draft-sliding-window-size 4096" in command
+
+
+class TestReportTemplate:
+    def test_report_template_mentions_baseline_and_verdict(self):
+        pair = select_pair(total_memory_gb=36)
+        report = render_report_template(machine_label="M3 Max 36GB", pair=pair)
+        assert "DFlash Apple Silicon Benchmark Report" in report
+        assert "M3 Max 36GB" in report
+        assert "Qwen/Qwen3.5-9B" in report
+        assert "plain MLX or llama.cpp speculative decoding" in report
+        assert "Worth operationalizing locally?" in report
+
+
+class TestMemoryDetection:
+    @patch("benchmarks.dflash_apple_silicon.platform.system", return_value="Darwin")
+    @patch("benchmarks.dflash_apple_silicon.subprocess.check_output", return_value=b"38654705664\n")
+    def test_detect_total_memory_gb_on_macos(self, _mock_sysctl, _mock_system):
+        assert detect_total_memory_gb() == 36.0

tests/test_quant_selector.py

@@ -0,0 +1,177 @@
+#!/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_keep_turboquant_quality_order_with_q4_fallback_last(self):
+        """TurboQuant levels should lead, with q4_0 reserved as the non-Turbo fallback."""
+        names = [level.name for level in QUANT_LEVELS]
+        assert names[:3] == ["turbo4", "turbo3", "turbo2"]
+        assert names[-1] == "q4_0"
+
+    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_falls_back_to_turbo2_when_nothing_fits(self):
+        with patch("evolution.quant_selector.detect_hardware") as mock_hw:
+            mock_hw.return_value = HardwareInfo(
+                total_memory_gb=8,
+                available_memory_gb=6,
+                gpu_memory_gb=8,
+                gpu_name="Tiny GPU",
+                cpu_cores=4,
+                detection_method="mock",
+            )
+            sel = select_quant_level(model_size_gb=16.0, context_length=131072)
+            assert sel.level.name == "turbo2"
+
+    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()