| --- |
| license: mit |
| language: |
| - en |
| - fr |
| tags: |
| - benchmark |
| - llama.cpp |
| - gguf |
| - quantization |
| - evaluation |
| - speculative-decoding |
| --- |
| |
| # 🔬 quant-vs-api-parity-harness |
|
|
| > **Is your local quant actually as good as the full-precision API?** This toolkit answered |
| > that question for a 284B MoE compressed to 2.9 bpw — verdict: **indistinguishable on the |
| > real serving path** (90.8% token-identical, 240/240 paired-QA parity, deep-derivation |
| > parity). 🎯 But the *real* product is the method: it caught **9 bugs in our own |
| > instruments** before they could lie to us — including one that had us convinced the quant |
| > had lost factual recall when it hadn't. 🪤 |
|
|
| Used to produce the results in |
| [DeepSeek-V4-Flash-0731-StrixHalo-Verified-GGUF](https://huggingface.co/Kevletesteur/DeepSeek-V4-Flash-0731-StrixHalo-Verified-GGUF). |
| Model-agnostic in spirit: bring any llama.cpp server + any OpenAI-compatible reference API. |
|
|
| ## 🚀 Install |
|
|
| ```bash |
| python3 -m venv venv && . venv/bin/activate |
| pip install sympy jinja2 # that's all — stdlib otherwise |
| echo "sk-..." > deepseek_api_key.txt # your reference-API key (chmod 600!) |
| # a llama.cpp server on 127.0.0.1:8080 serving the quant you want to test |
| ``` |
|
|
| ## 🧭 The method, in the order that matters |
|
|
| ### 1️⃣ Validate your instruments FIRST (`scorers.py`) — non-negotiable |
|
|
| ```bash |
| python3 scorers.py # 67 synthetic cases — must print 67/67 before ANY model run |
| ``` |
|
|
| Answer extractors, SymPy equivalence oracle (with convention lists + decimal tolerance), |
| degeneration detector, tool-call deep-equal. Each shipped with synthetic test cases: |
| correct answers in hostile formats (LaTeX `\pm`, `\boxed{}`, thousand separators, mid-line), |
| plausible wrongs, truncations, repetition loops. **This step caught 2 real bugs in our own |
| scorers on day one.** If you skip it, your benchmark will eventually lie to you. 🐛 |
|
|
| ### 2️⃣ Run the gates (G2-G6) — they decide what your data can mean |
|
|
| | gate | question it answers | what we found | |
| |---|---|---| |
| | **G2** `reference/g2g3_drafter_reference.sh` | is speculative decoding bit-exact on YOUR stack? | ❌ 0/10 — bench paired arms drafter-OFF | |
| | **G3** (same script) | is your server deterministic? | ✅ 10/10 | |
| | **G4** `g4g5g6_api.py` | what's the API's self-disagreement at temp 0? | short tasks 7/7 identical; long derivations **6/7 different** → vote 3 runs | |
| | **G5** (same) | can you even prove template parity? | often no (no published template) → treat as documented confound, drop boundary tokens | |
| | **G6** (same) | real API context ceiling? | ⚠️ disable thinking for the probe or your reply budget dies in the reasoning channel | |
|
|
| ### 3️⃣ Measure, cheapest-strongest first |
|
|
| **🧬 P1 — token-level teacher forcing** (`p1_*.py`) — *the best instrument here.* |
| Generate greedy reference texts via the API (with `top_logprobs`), then force that exact |
| token sequence through your local model one token at a time (`cache_prompt=true`, |
| `n_probs=20` → linear cost). Record top-1 agreement, the reference token's local rank, NLL. |
| No judge, no regex — the oracle is the distribution itself. **Bootstrap over TEXTS, never |
| tokens** (autocorrelation). Compare token *ids*, not strings. |
|
|
| **📐 T2 — multi-step derivations, SymPy oracle** (`t2_*.py`) — the compounding-error regime. |
| Parametric bank (truths *computed*, never typed — our own self-test caught a hand-typed |
| constant that was wrong 🙃), fixed `FINAL: <expr>` line, equivalence by |
| `simplify(candidate − truth) == 0`. Truncated ≠ wrong. McNemar on paired outcomes. Log |
| reasoning-trace lengths both sides and report them first. |
|
|
| **🎯 T1/T1b — paired exact-answer bank + calibration** (`t1_items.py`, `t1b_passe.py`). |
| 240 generated integer-answer items, two conditions (forced / UNSURE-allowed), answer-token |
| logprob both sides → AUROC(confidence → correctness) and the metric that actually matters |
| for routing: **P(answers ∧ wrong)**. |
|
|
| **⏱️ Speed** (`reference/nmax_depth_bench_reference.sh`): warm-up excluded, medians of ≥3, |
| report `t/s` AND `ms/eval` (under speculation, t/s is not machine speed), nested prompts + |
| prompt cache to pay prefill once. Our headline finding: **the speculative `n_max` optimum |
| inverts with context depth** (3 at short ctx → 2 from ~16k). |
| |
| ## 🥇 The cardinal rule (it cost us our biggest false finding) |
| |
| **Evaluate QUALITY only through the real serving path — `/v1/chat/completions`.** |
| Raw `/completion` with an auto-injected `<think>` can close the reasoning instantly at |
| temp 0 (10-character traces!) and answer reflexively. We published an entire "the quant lost |
| factual recall" conclusion, then replayed the failed items through the chat endpoint: |
| **4/4 correct**. The deficit was our benchmark's serving path, not the quant. Full story and |
| eight more traps in **[`NEGATIVE_RESULTS.md`](./NEGATIVE_RESULTS.md)** — read it before you |
| optimize anything. 🪤 |
| |
| ## 📁 Files |
| |
| | file | role | |
| |---|---| |
| | `scorers.py` | extractors + oracles + **67 self-tests** (run first, always) | |
| | `g4g5g6_api.py` | API noise floor · template probe · context ceiling | |
| | `p1_gen_api.py` / `p1_force_local.py` / `p1_depouille.py` | teacher forcing pipeline (resumable) + text-level bootstrap | |
| | `t2_items.py` / `t2_passe.py` | derivation bank (self-tested truths) + paired passes + McNemar | |
| | `t1_items.py` / `t1b_passe.py` | 240-item bank + two-condition calibration passes | |
| | `reference/*.sh` | speed & drafter gates — **reference implementations**, adapt to your service manager | |
| | `NEGATIVE_RESULTS.md` | 🧨 what did NOT work, with numbers — the most useful file here | |
| |
| MIT. Benchmarked on AMD Strix Halo (gfx1151, ROCm 7.1, 115 GB unified) against the official |
| DeepSeek API — but the method owes nothing to that hardware. *Validate your instruments, |
| equalize your serving paths, and let the distributions be the judge.* 🔍 |
| |
