:ledger: docs(readme): Write bench documentation

README.md

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+---
+license: mit
+pretty_name: DeepSeek-V4-Flash-0731 quantization measurements
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- evaluation
+- perplexity
+- kl-divergence
+- gguf
+- quantization
+- llama.cpp
+- deepseek-v4
+- reproducibility
+size_categories:
+- n<1K
+---
+
+# DeepSeek-V4-Flash-0731 — quantization measurements
+
+Everything needed to reproduce, audit or extend the numbers published in
+[AtomicChat/DeepSeek-V4-Flash-0731-GGUF](https://huggingface.co/AtomicChat/DeepSeek-V4-Flash-0731-GGUF):
+the reference logits, the evaluation corpus, the raw tool output for every quant we
+measured, and the parsed results.
+
+Every GGUF of this model that we could find on the Hub was measured here — ours,
+unsloth's, bartowski's, ggml-org's, antirez's and others — on one machine, against one
+reference, with one command. Publishers normally report numbers from their own harness,
+which makes cross-vendor comparison meaningless. These files exist so that anyone can
+check ours instead of trusting them.
+
+> [!IMPORTANT]
+> Measured using `8x5090`
+
+## Files
+
+| File | Size | What it is |
+|---|---:|---|
+| `wiki-alt.txt` | 1.29 MB | Evaluation corpus: wikitext-2 test split from `Salesforce/wikitext`, parquet rows concatenated |
+| `ref5632.kld` | 37.1 GB | Reference logits from the lossless `AD-BF16` quant over that corpus at ctx 5632 |
+| `RESULTS-0731.jsonl` | small | Parsed results, AtomicChat and unsloth ladders |
+| `RIVALS-B1.jsonl`, `RIVALS-B2.jsonl`, `RIVALS-B3.jsonl` | small | Parsed results, other publishers, split by the machine that produced them |
+| `logs/*.log` | few MB | Full unedited `llama-perplexity` output for every quant, nothing filtered |
+
+The `.kld` file stores the reference model's full probability distribution at every scored
+token position — roughly 258 KB per token at this vocabulary size. It is what makes the
+KL-divergence numbers comparable: every quant is compared against these exact logits.
+
+## Result schema
+
+```json
+{"repo": "bartowski/DeepSeek-V4-Flash-0731-GGUF",
+ "name": "MXFP4",
+ "bytes": 145678901234,
+ "ppl": "4.5446",
+ "kld": "0.156403",
+ "rms": "12.686",
+ "top1": "87.369"}
+```
+
+`ppl` is `Mean PPL(Q)` from the KL-divergence block, `kld` is `Mean KLD`, `rms` is
+`RMS Δp`, `top1` is `Same top p` — the share of positions where the quant picks the same
+next token as the reference. Note that `Mean PPL(Q)` and the standalone `Final estimate:
+PPL` printed by the same tool are different aggregations and do not match; the logs
+contain both.
+
+## Measurement setup
+
+| | |
+|---|---|
+| Reference | `AtomicChat/DeepSeek-V4-Flash-0731-GGUF` → `AD-BF16` (bit-exact with the official weights) |
+| Corpus | `Salesforce/wikitext`, `wikitext-2-raw-v1`, test split, rows concatenated |
+| Context | 5632, batch 5632, 51 chunks |
+| llama.cpp | PR [#24162](https://github.com/ggml-org/llama.cpp/pull/24162), commit `f180ae8b2`, built with `-DCMAKE_CUDA_ARCHITECTURES=120` |
+| GPU | 8× RTX 5090 |
+
+## Hardware matters here, and it is not optional
+
+The routed experts of this model are 96% of its weights and they are stored in MXFP4.
+llama.cpp has two paths for that format — unpack to BF16 and use a normal tensor-core
+matmul, or feed the packed 4-bit data into block-scaled instructions. The second is gated
+on compute capability ≥ 12.0, which covers consumer Blackwell only. H100 and H200 are 9.0,
+B200 is 10.0, B300 is 10.3; all take the first path despite having FP4 hardware.
+
+Same file, same corpus, same commit, reference model:
+
+| GPU | ctx 512 | ctx 5632 |
+|---|---:|---:|
+| RTX 5090 | 5.4312 | 4.5381 |
+| H100 | 5.1554 | 4.3406 |
+
+A 4–5% difference from the GPU alone. Reproducing these numbers requires consumer
+Blackwell **and** a build that targets it — compiling for `sm_90` on a 5090 gives the
+H100 numbers, because the native kernel never lands in the binary.
+
+## Reproducing
+
+```bash
+git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
+git fetch origin pull/24162/head:dsv4 && git checkout dsv4
+cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES=120
+cmake --build build -j --target llama-perplexity
+```
+
+```bash
+hf download AtomicChat/dsv4-eval-artifacts --repo-type dataset --local-dir .
+
+./build/bin/llama-perplexity \
+  -m <any-quant>-00001-of-*.gguf \
+  -f wiki-alt.txt --kl-divergence-base ref5632.kld --kl-divergence \
+  -ngl 99 -c 5632 -b 5632
+```
+
+To rebuild the reference from scratch instead of downloading it, run the same command
+against `AD-BF16` with only `--kl-divergence-base` and no `--kl-divergence`. Takes about
+ten minutes and should print `Final estimate: PPL = 4.5381`.
+
+## Caveats
+
+- Absolute values are not comparable to numbers published elsewhere. Other publishers use
+  different corpora, context lengths and hardware. Compare within one table.
+- 51 chunks at ctx 5632 gives roughly ±0.003 on mean KLD. Differences smaller than that
+  are noise.
+- Quants of derived models — expert-pruned, abliterated, distilled — are deliberately
+  excluded. KL-divergence against this reference would measure the difference between
+  models, not the cost of quantization.
+
+## License
+
+MIT. Derived from `deepseek-ai/DeepSeek-V4-Flash-0731`. Produced by
+[Atomic Chat](https://huggingface.co/AtomicChat).