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README + custom-quantization tutorial polish
README.md / README.ko.md: - Replace plain comparison table with visual ASCII bar chart of PPL degradation across all KV types (FP32 → 5b → 4bo → 3bo → 4b → uniform_4b → llama.cpp q4_0 → 3b) - Expanded quality table with bytes/block + compression columns - Pareto-optimal recommendations called out (4b default, 5b quality) docs/custom-quantization.md: - Updated reference types table with measured PPL deltas where known, bytes/block, and pattern descriptions - Added "How the production winners were found" section showing the 6-round Karpathy loop history that produced Variant F - Concrete iteration loop guide for contributors adding new types Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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‎README.ko.md‎

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@@ -43,17 +43,40 @@ LLM 메모리의 병목은 모델 가중치가 아니라 **KV 캐시**입니다.
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> **같은 하드웨어. 4–7배 긴 컨텍스트. PPL 측정 + 공개.**
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### Llama 3.2 3B Instruct, FP32 KV 베이스라인 = PPL 13.56
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### Llama 3.2 3B Instruct — WikiText PPL (FP32 베이스라인 = 13.56)
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| KV 설정 | bits/elem | PPL | Δ vs FP32 | 비고 |
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|:--------|----------:|----:|----------:|:------|
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| FP32 (베이스라인) | 32 | 13.56 | — | reference |
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| **`turbo_kv_4b`** ⭐ | 4 | **14.28** | **+5.3%** | RHT + 4-bit codebook, uniform_4b 능가 |
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| `uniform_4b` | 4 | 14.41 | +6.3% | per-block min-max |
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| `turbo_kv_3b` | 3 | 15.39 | +13.5% | RHT + 3-bit codebook |
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| llama.cpp `q4_0` KV | 4 | ~14.99 | +10.6% | 비교용 |
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```
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FP32 대비 PPL 저하 (낮을수록 좋음)
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llama.cpp Q4_0 KV │██████████████████████████ +10.6% (4-bit, RHT 없음)
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│
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uniform_4b │███████████████ +6.3% (4-bit, RHT 없음)
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│
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turbo_kv_4b ⭐ 기본 │█████████████ +5.3% (72B/블록)
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│
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turbo_kv_3bo 🧪 │█████████ +3.5% (80B/블록, +outliers)
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│
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turbo_kv_4bo 🧪 │█████ +2.2% (96B/블록, +outliers)
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│
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turbo_kv_5b 🏆 quality│█ +0.34% (88B/블록, 거의 무손실)
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│
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FP32 reference │ ← 0.0% (양자화 없음)
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└─────────────────────────────────────
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0% +5% +10%
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```
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`turbo_kv_4b`가 프로젝트 내 **최고 4-bit KV 양자화** — 같은 비트 예산에서 이전 production 베이스라인(`uniform_4b`)과 llama.cpp `q4_0` KV를 모두 능가합니다. Karpathy 루프로 도달한 과정은 [bench/results/turboquant_reproduction.md](bench/results/turboquant_reproduction.md).
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| KV 설정 | 블록 바이트 | 압축 | PPL | Δ vs FP32 |
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|:--------|----:|----:|----:|----:|
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| FP32 reference | — | 1× | 13.56 | — |
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| **`turbo_kv_5b`** 🏆 quality | 88 | 5.8× | **13.60** | **+0.34%** |
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| `turbo_kv_4bo` 🧪 | 96 | 5.3× | 13.86 | +2.2% |
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| `turbo_kv_3bo` 🧪 | 80 | 6.4× | 14.03 | +3.5% |
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| **`turbo_kv_4b`** ⭐ 기본 | 72 | 7.1× | 14.28 | +5.3% |
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| `uniform_4b` | 68 | 7.5× | 14.41 | +6.3% |
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| llama.cpp `q4_0` KV | ~70 | ~7.3× | ~14.99 | +10.6% |
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| `turbo_kv_3b` | 56 | 9.1× | 15.39 | +13.5% |
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`turbo_kv_4b` (기본)와 `turbo_kv_5b` (quality)가 Pareto-optimal 추천. 두 가지 모두 llama.cpp `q4_0` KV를 같거나 작은 블록 사이즈에서 능가. 전체 Karpathy 루프 이력은 [bench/results/turboquant_reproduction.md](bench/results/turboquant_reproduction.md).
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### 컨텍스트 길이 증가 (`turbo_kv_4b` + `q4` value cache)
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‎README.md‎

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@@ -43,17 +43,41 @@ LLM memory is dominated by the **KV cache**, not model weights. At 32K context,
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> **Same hardware. 4–7x longer context. PPL measured and disclosed.**
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### Llama 3.2 3B Instruct, FP32 KV baseline = PPL 13.56
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### Llama 3.2 3B Instruct — PPL on WikiText (FP32 baseline = 13.56)
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| KV Config | Bits/elem | PPL | Δ vs FP32 | Notes |
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|:----------|----------:|----:|----------:|:------|
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| FP32 (baseline) | 32 | 13.56 | — | reference |
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| **`turbo_kv_4b`** ⭐ | 4 | **14.28** | **+5.3%** | RHT + 4-bit codebook, beats uniform_4b |
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| `uniform_4b` | 4 | 14.41 | +6.3% | per-block min-max |
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| `turbo_kv_3b` | 3 | 15.39 | +13.5% | RHT + 3-bit codebook |
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| llama.cpp `q4_0` KV | 4 | ~14.99 | +10.6% | for comparison |
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```
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PPL Degradation vs FP32
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(lower is better)
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llama.cpp Q4_0 KV │██████████████████████████ +10.6% (4-bit, no RHT)
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│
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uniform_4b │███████████████ +6.3% (4-bit, no RHT)
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│
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turbo_kv_4b ⭐ default│█████████████ +5.3% (72B/block)
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│
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turbo_kv_3bo 🧪 │█████████ +3.5% (80B/block, +outliers)
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│
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turbo_kv_4bo 🧪 │█████ +2.2% (96B/block, +outliers)
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│
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turbo_kv_5b 🏆 quality│█ +0.34% (88B/block, near-lossless)
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│
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FP32 reference │ ← 0.0% (no quantization)
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└─────────────────────────────────────
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0% +5% +10%
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```
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`turbo_kv_4b` is currently the **best 4-bit KV cache quantization in the project** — it beats both our previous production baseline (`uniform_4b`) and llama.cpp's `q4_0` KV at the same bit budget. The Karpathy-loop history that produced it is in [bench/results/turboquant_reproduction.md](bench/results/turboquant_reproduction.md).
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| KV Config | Bytes/block | Compression | PPL | Δ vs FP32 |
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|:----------|------------:|------------:|----:|----------:|
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| FP32 reference | — | 1× | 13.56 | — |
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| **`turbo_kv_5b`** 🏆 quality | 88 | 5.8× | **13.60** | **+0.34%** |
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| `turbo_kv_4bo` 🧪 | 96 | 5.3× | 13.86 | +2.2% |
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| `turbo_kv_3bo` 🧪 | 80 | 6.4× | 14.03 | +3.5% |
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| **`turbo_kv_4b`** ⭐ default | 72 | 7.1× | 14.28 | +5.3% |
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| `uniform_4b` | 68 | 7.5× | 14.41 | +6.3% |
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| llama.cpp `q4_0` KV | ~70 | ~7.3× | ~14.99 | +10.6% |
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| `turbo_kv_3b` | 56 | 9.1× | 15.39 | +13.5% |
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`turbo_kv_4b` (default) and `turbo_kv_5b` (quality) are the recommended Pareto-optimal choices. Both beat llama.cpp's `q4_0` KV at the same or smaller block size on Llama 3.2 3B perplexity. The full Karpathy-loop optimization history is in [bench/results/turboquant_reproduction.md](bench/results/turboquant_reproduction.md).
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### Context length gains (`turbo_kv_4b` + `q4` value cache)
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‎docs/custom-quantization.md‎

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Study these files for implementation patterns:
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| Type Family | Source File | Complexity | Good Starting Point? |
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|-------------|------------|-----------|---------------------|
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| Uniform 4-bit | `src/core/tq_uniform.c` | Simple | Yes -- closest to the example above |
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| Uniform 2-bit | `src/core/tq_uniform.c` | Medium | Yes -- shows sub-block scales |
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| Uniform 3-bit | `src/core/tq_uniform.c` | Medium | Shows 3-bit packing (non-power-of-2) |
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| Polar | `src/core/tq_polar.c` | Complex | Advanced -- polar coordinate encoding |
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| QJL | `src/core/tq_qjl.c` | Complex | Advanced -- random projection hashing |
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| Turbo | `src/core/tq_turbo.c` | Complex | Advanced -- composite (Polar + QJL residual) |
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| TurboKV | `src/core/tq_turbo.c` | Complex | Advanced -- RHT + codebook + QJL |
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| Mixed | `src/core/tq_uniform.c` | Medium | Shows outlier handling |
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| Type Family | Source File | Bytes/block | Llama 3.2 3B PPL Δ | Complexity | Pattern |
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|-------------|------------|------------:|-------------------:|-----------|---------|
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| `uniform_4b` | `src/core/tq_uniform.c` | 68 | +6.3% | Simple | Per-block min/max linear |
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| `uniform_2b` | `src/core/tq_uniform.c` | 36 | — | Medium | Per-sub-block scales |
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| `uniform_3b` | `src/core/tq_uniform.c` | 52 | — | Medium | Non-power-of-2 packing |
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| `polar_3b` / `polar_4b` | `src/core/tq_polar.c` | 72 | — | Complex | Polar coordinates `(r, θ)` |
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| `qjl_1b` | `src/core/tq_qjl.c` | 36 | — | Complex | Sign-hash random projection |
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| `turbo_3b` / `turbo_4b` | `src/core/tq_turbo.c` | 96 | — | Complex | Composite (Polar + QJL residual, legacy) |
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| **`turbo_kv_4b` ⭐** | `src/core/tq_turbo_kv.c` | 72 | **+5.3%** | Medium | **RHT + 4-bit Lloyd-Max codebook (Variant F)** |
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| **`turbo_kv_5b` 🏆** | `src/core/tq_turbo_kv.c` | 88 | **+0.34%** | Medium | RHT + 5-bit Lloyd-Max codebook |
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| `turbo_kv_3b` | `src/core/tq_turbo_kv.c` | 56 | +13.5% | Medium | RHT + 3-bit Lloyd-Max codebook |
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| `turbo_kv_4bo` 🧪 | `src/core/tq_turbo_kv.c` | 96 | +2.2% | Medium | 4b base + 8 per-block FP16 outliers |
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| `turbo_kv_3bo` 🧪 | `src/core/tq_turbo_kv.c` | 80 | +3.5% | Medium | 3b base + 8 per-block FP16 outliers |
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| `turbo_kv_1b` | `src/core/tq_turbo_kv.c` | 24 | — | Medium | 1-bit sign hash (Hamming attention) |
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| `mixed_4b8` | `src/core/tq_uniform.c` | — | — | Medium | 4-bit base + FP16 outlier table |
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### How the production winners were found
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`turbo_kv_4b` and `turbo_kv_5b` are not just hand-designed types — they're the **outputs of a 6-round Karpathy loop** of empirical iteration on Llama 3.2 3B perplexity:
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| Round | Variant | turbo_kv_4b PPL | Decision |
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|---:|---|---:|---|
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| 0 | Literal port (RHT + Lloyd-Max + 1-bit QJL residual) | 16.03 | baseline |
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| 1 | empirical std rescale | 15.87 | keep |
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| 2 | max-abs no-clip rescale | 15.39 | keep |
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| 3 | 99th percentile clipping | 17.24 | revert |
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| 4 | K·std sweep (K ∈ {1.5..4}) | 15.53 (best K=2) | keep |
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| 5 | uniform 8-level linear | 16.28 | revert |
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| **6** | **drop QJL, double codebook size (Variant F)** | **14.28** ✅ | **shipped** |
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The full ablation history with measurement methodology is in [bench/results/turboquant_reproduction.md](../bench/results/turboquant_reproduction.md).
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If you're adding a new type, you'll likely follow the same loop:
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1. Implement a literal version of your idea
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2. Run `./build/quant model.gguf --ppl bench/data/ppl_1k.txt -k yourtype` to measure
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3. Compare against `turbo_kv_4b` (default)
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4. Iterate one variable at a time, accept improvements, revert regressions
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5. Add a regression test that pins your final quality threshold
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The codebase is structured to make this loop fast (build < 30s, PPL test < 2 min on a 3B model).
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### File Checklist
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