---
license: other
language:
- en
- zh
- ru
- ja
- ar
- multilingual
tags:
- imatrix
- quantization
- calibration
- gguf
- deepseek-v4
- three.js
- webgl
task_categories:
- text-generation
---

# calib-corpora — imatrix calibration corpus for DeepSeek-V4-Flash-0731

Calibration text for building the importance matrix (imatrix) behind the dynamic GGUF quant line of [`deepseek-ai/DeepSeek-V4-Flash-0731`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731).

An imatrix is activation statistics collected by running the model over a corpus. The corpus decides which weights the model treats as important, and therefore which weights get more bits. **This corpus is deliberately not general web text** — it is weighted toward 3D/graphics code generation and agentic tool-calling, because that is what these quants are for.

## Why this composition

Three properties of this model drive the design, all confirmed against its `config.json`:

| property | value | consequence for calibration |
|---|---|---|
| `n_routed_experts` / `num_experts_per_tok` | 256 / 6 | Any single expert sees ~2.3% of tokens. A dense-model-sized corpus gives most experts too few samples to be meaningful, so the budget has to be an order of magnitude larger. |
| `num_hash_layers` | 3 | In the first three MoE layers the expert is chosen by a fixed hash of the **token id**, not by a learned gate. Coverage there depends on *vocabulary breadth*, not on volume — an unseen token id means a never-activated expert, no matter how much text you feed it. |
| `compress_ratios` | alternating 4/128 over 43 layers | The CSA/HCA compression path is barely exercised by short chunks, so a real long-document slice is required rather than concatenated short ones. |

The vocabulary is the binding constraint. It has 129,280 embedding rows, and by script the base vocabulary is 56.1% Latin, 27.6% CJK, 4.1% Cyrillic, 2.4% Arabic, 1.0% Thai, 0.9% Hangul, 0.7% Hebrew, 0.5% Greek, 0.4% Hiragana, 0.2% Devanagari. **Covering every Latin token in the vocabulary would still only reach 55.5%** of the embedding table, so a 60% coverage target is unreachable from English source code alone. That is why there is a 30-language Wikipedia slice and an explicit vocabulary sweep.

## Files

| file | documents | tokens | purpose |
|---|---:|---:|---|
| `calib_train.txt` | 1,087 | 1,868,626 | fed to `llama-imatrix` |
| `calib_heldout.txt` | 104 | 141,800 | same distribution, **not** used for the imatrix — for measuring generalisation |
| `eval_neutral.txt` | 66 | 189,407 | disjoint neutral text and code, no overlap with calibration |

Each `.txt` is flat UTF-8 with documents separated by a blank line, sharded at 500 MB (the corpus fits in one shard per split). Alongside each is a `*.manifest.jsonl` giving one record per document — id, domain, source, license, path, language, token count, character count — in the same order the documents appear in the `.txt`. The manifest exists because the flat format cannot express document boundaries unambiguously: many documents legitimately contain blank lines of their own.

`legacy/` holds the previous revision of this dataset verbatim. Its content was re-split, deduplicated against the new material and carried forward into the build rather than discarded.

## Composition

Shares are of **tokens**, not documents, over `calib_train` + `calib_heldout` (2,010,426 tokens).

| domain | target | actual | documents | tokens | what it is |
|---|---:|---:|---:|---:|---|
| `graphics` | 35% | 31.4% | 448 | 631,310 | three.js scenes/materials/loaders/post-processing, WebGL & WebGPU, GLSL & WGSL shaders, animation timelines, procedural generation, 3D maths |
| `code` | 15% | 13.4% | 243 | 270,098 | whole real source files — TypeScript, JavaScript, Python, Rust, C++ — plus configs, tests and build scripts |
| `agentic` | 15% | 13.4% | 63 | 270,012 | multi-turn tool-calling traces in the model's own DSML chat format: read/edit files, run commands, read output, recover from a failure |
| `longctx` | 10% | 13.4% | 18 | 268,568 | documents of 8k tokens and up (large real files, plus same-directory module bundles built to 8k-16k) to exercise the CSA/HCA compression path |
| `vocab_sweep` | — | 9.0% | 76 | 180,582 | synthetic wordlists that carry the tail of the vocabulary; exists purely to cover the hash-routed layers |
| `general` | 10% | 9.0% | 36 | 180,045 | multilingual Wikipedia across 30 languages, plus markdown/tables/unicode from the previous revision |
| `reasoning` | 10% | 5.9% | 189 | 117,682 | step-by-step worked problems with reasoning kept inside `<think>` blocks: 3D maths, numerics, algorithms, graphics debugging |
| `structured` | 5% | 4.6% | 118 | 92,129 | JSON, YAML, TOML and SQL from the repositories, real `git log -p` diff patches, and the most regex-dense real sources |

**Deviations from target are reported, not corrected.** Notes on the ones that matter:

- `longctx` is defined by *length*, not by topic: any document of 8k tokens or more is counted here whatever its subject. Most of it is graphics code, so the effective graphics share is higher than the `graphics` row alone suggests. The origin breakdown is in the manifest under `content_domain`.
- `vocab_sweep` is over and above the seven requested domains. It is synthetic and is kept as its own domain so it can be filtered out via the manifest by anyone who wants to A/B an imatrix without it.

### Sources and licences

| source | licence | documents | tokens | share |
|---|---|---:|---:|---:|
| `synthetic/vocab-sweep` | CC0-1.0 (generated from tokenizer vocabulary) | 76 | 180,582 | 9.0% |
| `fmt` | MIT | 43 | 133,281 | 6.6% |
| `three.js` | MIT | 67 | 132,759 | 6.6% |
| `webgl-fundamentals` | BSD-3-Clause | 61 | 123,355 | 6.1% |
| `pixijs` | MIT | 68 | 99,406 | 4.9% |
| `gl-matrix` | MIT | 39 | 93,756 | 4.7% |
| `ripgrep` | MIT OR Unlicense | 41 | 86,938 | 4.3% |
| `drei` | MIT | 64 | 79,436 | 4.0% |
| `json-cpp` | MIT | 43 | 71,097 | 3.5% |
| `react-three-fiber` | MIT | 66 | 69,393 | 3.5% |
| `flask` | BSD-3-Clause | 41 | 60,905 | 3.0% |
| `tween.js` | MIT | 51 | 59,526 | 3.0% |
| `webgl-noise` | MIT | 22 | 58,134 | 2.9% |
| `webgpu-samples` | BSD-3-Clause | 61 | 57,655 | 2.9% |
| `requests` | Apache-2.0 | 40 | 40,938 | 2.0% |
| `serde` | MIT OR Apache-2.0 | 37 | 37,921 | 1.9% |
| `synthetic/agentic:webgl-fundamentals` | see embedded repo (tool results are verbatim repo content) | 6 | 32,919 | 1.6% |
| `synthetic/agentic:flask` | see embedded repo (tool results are verbatim repo content) | 6 | 31,883 | 1.6% |
| `synthetic/agentic:ripgrep` | see embedded repo (tool results are verbatim repo content) | 6 | 31,681 | 1.6% |
| `synthetic/agentic:pixijs` | see embedded repo (tool results are verbatim repo content) | 6 | 27,468 | 1.4% |
| `glTF-Sample-Viewer` | Apache-2.0 | 11 | 26,894 | 1.3% |
| `vite` | MIT | 42 | 25,248 | 1.3% |
| `synthetic/agentic:json-cpp` | see embedded repo (tool results are verbatim repo content) | 5 | 24,818 | 1.2% |
| `synthetic/agentic:requests` | see embedded repo (tool results are verbatim repo content) | 5 | 22,881 | 1.1% |
| `synthetic/agentic:drei` | see embedded repo (tool results are verbatim repo content) | 6 | 22,271 | 1.1% |
| `wikimedia/wikipedia:20231101.el` | CC-BY-SA-4.0 | 2 | 21,861 | 1.1% |
| `synthetic/agentic:three.js` | see embedded repo (tool results are verbatim repo content) | 6 | 21,359 | 1.1% |
| `wikimedia/wikipedia:20231101.uk` | CC-BY-SA-4.0 | 2 | 20,333 | 1.0% |
| `synthetic/agentic:webgpu-samples` | see embedded repo (tool results are verbatim repo content) | 6 | 18,563 | 0.9% |
| `synthetic/agentic:react-three-fiber` | see embedded repo (tool results are verbatim repo content) | 6 | 18,458 | 0.9% |
| `synthetic/agentic:vite` | see embedded repo (tool results are verbatim repo content) | 5 | 17,711 | 0.9% |
| `wikimedia/wikipedia:20231101.my` | CC-BY-SA-4.0 | 1 | 16,783 | 0.8% |
| `wikimedia/wikipedia:20231101.hi` | CC-BY-SA-4.0 | 1 | 14,126 | 0.7% |
| `synthetic/reasoning:perlin-noise` | CC0-1.0 (generated) | 15 | 11,671 | 0.6% |
| `synthetic/reasoning:bezier-decasteljau` | CC0-1.0 (generated) | 15 | 10,664 | 0.5% |
| `wikimedia/wikipedia:20231101.fa` | CC-BY-SA-4.0 | 1 | 9,997 | 0.5% |
| `synthetic/reasoning:cubic-bezier-easing` | CC0-1.0 (generated) | 15 | 9,960 | 0.5% |
| `synthetic/reasoning:quaternion-product` | CC0-1.0 (generated) | 15 | 9,861 | 0.5% |
| `synthetic/reasoning:moller-trumbore` | CC0-1.0 (generated) | 18 | 9,215 | 0.5% |
| `wikimedia/wikipedia:20231101.ar` | CC-BY-SA-4.0 | 1 | 9,073 | 0.5% |
| `synthetic/reasoning:normal-matrix` | CC0-1.0 (generated) | 12 | 8,417 | 0.4% |
| `synthetic/reasoning:catmull-rom` | CC0-1.0 (generated) | 11 | 8,342 | 0.4% |
| `wikimedia/wikipedia:20231101.bn` | CC-BY-SA-4.0 | 1 | 8,067 | 0.4% |
| `synthetic/reasoning:quaternion-slerp` | CC0-1.0 (generated) | 15 | 7,872 | 0.4% |
| `synthetic/reasoning:srgb-linear` | CC0-1.0 (generated) | 12 | 7,396 | 0.4% |
| `wikimedia/wikipedia:20231101.ta` | CC-BY-SA-4.0 | 2 | 7,363 | 0.4% |
| `synthetic/reasoning:rodrigues-rotation` | CC0-1.0 (generated) | 12 | 7,321 | 0.4% |
| `synthetic/reasoning:look-at-basis` | CC0-1.0 (generated) | 12 | 7,042 | 0.4% |
| `wikimedia/wikipedia:20231101.fr` | CC-BY-SA-4.0 | 1 | 6,784 | 0.3% |
| `wikimedia/wikipedia:20231101.th` | CC-BY-SA-4.0 | 2 | 6,242 | 0.3% |
| `synthetic/reasoning:perspective-projection` | CC0-1.0 (generated) | 11 | 6,171 | 0.3% |
| `wikimedia/wikipedia:20231101.es` | CC-BY-SA-4.0 | 1 | 5,974 | 0.3% |
| `wikimedia/wikipedia:20231101.nl` | CC-BY-SA-4.0 | 1 | 5,970 | 0.3% |
| `wikimedia/wikipedia:20231101.id` | CC-BY-SA-4.0 | 1 | 5,649 | 0.3% |
| `wikimedia/wikipedia:20231101.am` | CC-BY-SA-4.0 | 1 | 5,621 | 0.3% |
| `AtomicChat/calib-corpora@previous` | CC-BY-SA-4.0 (StackOverflow-derived) / mixed | 31 | 5,600 | 0.3% |
| `synthetic/reasoning:fresnel-schlick` | CC0-1.0 (generated) | 10 | 4,962 | 0.2% |
| `wikimedia/wikipedia:20231101.en` | CC-BY-SA-4.0 | 1 | 4,735 | 0.2% |
| `wikimedia/wikipedia:20231101.vi` | CC-BY-SA-4.0 | 1 | 4,468 | 0.2% |
| `wikimedia/wikipedia:20231101.cs` | CC-BY-SA-4.0 | 1 | 4,467 | 0.2% |
| `wikimedia/wikipedia:20231101.pt` | CC-BY-SA-4.0 | 1 | 3,217 | 0.2% |
| `wikimedia/wikipedia:20231101.he` | CC-BY-SA-4.0 | 1 | 3,099 | 0.2% |
| `wikimedia/wikipedia:20231101.ja` | CC-BY-SA-4.0 | 1 | 3,089 | 0.2% |
| `wikimedia/wikipedia:20231101.tr` | CC-BY-SA-4.0 | 2 | 2,885 | 0.1% |
| `wikimedia/wikipedia:20231101.hy` | CC-BY-SA-4.0 | 1 | 2,793 | 0.1% |
| `synthetic/reasoning:instancing-vs-merging` | CC0-1.0 (generated) | 3 | 1,865 | 0.1% |
| `wikimedia/wikipedia:20231101.ko` | CC-BY-SA-4.0 | 1 | 1,739 | 0.1% |
| `synthetic/reasoning:debug-zfighting` | CC0-1.0 (generated) | 2 | 1,483 | 0.1% |
| `wikimedia/wikipedia:20231101.pl` | CC-BY-SA-4.0 | 1 | 1,269 | 0.1% |
| `synthetic/reasoning:ray-sphere` | CC0-1.0 (generated) | 4 | 1,127 | 0.1% |
| `synthetic/reasoning:transparency-sorting` | CC0-1.0 (generated) | 2 | 1,122 | 0.1% |
| `wikimedia/wikipedia:20231101.de` | CC-BY-SA-4.0 | 1 | 1,073 | 0.1% |
| `synthetic/reasoning:debug-shader-black` | CC0-1.0 (generated) | 1 | 839 | 0.0% |
| `wikimedia/wikipedia:20231101.ka` | CC-BY-SA-4.0 | 1 | 831 | 0.0% |
| `wikimedia/wikipedia:20231101.ru` | CC-BY-SA-4.0 | 1 | 799 | 0.0% |
| `synthetic/reasoning:bvh-complexity` | CC0-1.0 (generated) | 1 | 678 | 0.0% |
| `synthetic/reasoning:float32-world-precision` | CC0-1.0 (generated) | 1 | 591 | 0.0% |
| `wikimedia/wikipedia:20231101.sv` | CC-BY-SA-4.0 | 1 | 579 | 0.0% |
| `synthetic/reasoning:gpu-resource-disposal` | CC0-1.0 (generated) | 1 | 565 | 0.0% |
| `wikimedia/wikipedia:20231101.zh` | CC-BY-SA-4.0 | 1 | 565 | 0.0% |
| `synthetic/reasoning:raycaster-stale-matrix` | CC0-1.0 (generated) | 1 | 518 | 0.0% |
| `wikimedia/wikipedia:20231101.it` | CC-BY-SA-4.0 | 1 | 457 | 0.0% |

Every repository was shallow-cloned and had its `LICENSE` file read before use. **`patriciogonzalezvivo/thebookofshaders` was cloned, inspected and dropped**: its licence is all-rights-reserved (*"You cannot host, display, distribute or share this Work in any form"*), so none of it appears here despite being an obvious fit for the domain.

Synthetic slices (`synthetic/agentic:*`, `synthetic/reasoning:*`, `synthetic/vocab-sweep`) are generated by the build scripts in `pipeline/`. The agentic traces embed **verbatim file content from the listed repositories** as tool results, so they inherit those repositories' licences; the surrounding dialogue is generated. See [Synthetic slices](#synthetic-slices).

## Tokenizer

- Model: [`deepseek-ai/DeepSeek-V4-Flash-0731`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731)
- Revision: `9e165c30e2704aec5d9d593cce3eebd58bbef1cb`
- `vocab_size`: 129,280 (from `config.json`; this is the denominator for all coverage numbers below — it is the size of the embedding table, and therefore the domain the layer-0-2 hash router indexes into)

Counting is done with special tokens **parsed, not escaped** — the equivalent of `llama-imatrix --parse-special`. `<｜begin▁of▁sentence｜>` becomes id 0 rather than a run of literal characters. This matters for the agentic and reasoning slices, which are full of them.

> **The model ships no `chat_template`.** `tokenizer_config.json` has no such field and there is no `chat_template.jinja` in the repo, so `apply_chat_template()` does not work. The authoritative prompt format is the reference implementation at `encoding/encoding_dsv4.py` in the model repo, and this build imports it directly rather than reimplementing it. Its own test suite (`encoding/test_encoding_dsv4.py`, 4 cases) passes against the pinned revision, and all chat-formatted documents here are produced by `encode_messages(...)` from that file.

## Deduplication

- **Exact:** SHA-256 over the document with trailing intra-line whitespace normalised. 59 documents removed.
- **Near:** MinHash + LSH banding. 121 permutations, 11 bands × 11 rows, shingles of 5 whitespace-delimited tokens. **Jaccard threshold 0.8** — the banding is chosen so the LSH S-curve is centred there ((1/11)^(1/11) ≈ 0.80). Longest document in each cluster is kept. 565 documents removed.
- **Combined drop rate: 5.09%** of 12,262 candidate documents.

Two structural steps prevent duplication that document-level dedup cannot see:

- three.js and webgl-fundamentals ship thousands of example pages sharing an identical ~600-byte HTML head. Bodies genuinely differ, so MinHash does not flag them. For most example pages only the `<script type="module">` body is kept; a deterministic 1-in-7 sample keeps the whole page so the scaffold stays represented.
- Files used as tool results in agentic traces come from a reserved partition (`sha1(path+repo) % 10 == 7`) that is excluded from the `code` and `graphics` slices, so no file content is counted in two domains.

## Splits

Split is by **document**, never by chunk, so no file has pieces on both sides.

- `calib_train` / `calib_heldout`: key is `sha1("split:" + document_id)`, heldout when `int(key, 16) % 10 == 0`. Deterministic and stable across rebuilds. Target 90/10; actual **92.9% / 7.1%** by tokens (the split is by document count, so the token split drifts slightly).
- `eval_neutral` is **not** a random slice of the same pool. It is drawn from sources held apart from calibration entirely: four repositories never used above (`click`, `lodash`, `rust-log`, `Catch2`), plus Wikipedia articles routed to eval by `sha1("wiki:"+article_id)` before any calibration sampling. Documents already selected for calibration are additionally filtered out by id.

## Measured metrics

### Totals and vocabulary coverage

Coverage is the share of the 129,280-row embedding table observed at least N times. This is the direct proxy for hash-routed expert coverage in layers 0-2.

| split | documents | tokens | ids seen ≥1 | ≥10 | ≥100 |
|---|---:|---:|---:|---:|---:|
| `calib_train` | 1,087 | 1,868,626 | 112,574 (87.1%) | 12,522 (9.7%) | 2,407 (1.9%) |
| `calib_heldout` | 104 | 141,800 | 23,139 (17.9%) | 1,896 (1.5%) | 155 (0.1%) |
| `eval_neutral` | 66 | 189,407 | 21,748 (16.8%) | 3,039 (2.4%) | 261 (0.2%) |

### Document length in tokens

| split | p50 | p90 | p99 |
|---|---:|---:|---:|
| `calib_train` | 708 | 4,335 | 12,251 |
| `calib_heldout` | 689 | 2,523 | 7,959 |
| `eval_neutral` | 966 | 6,830 | 17,503 |

### Acceptance criteria

| criterion | result | value |
|---|---|---:|
| ≥ 1,000,000 tokens in `calib_train` | **pass** | 1,868,626 |
| ≥ 60% of vocabulary seen at least once | **pass** | 87.1% |
| p99 document length ≥ 8,000 tokens | **pass** | 12,251 |

Per-domain tables, the full top-50 token frequency list and the raw numbers behind all of the above are in `metrics.txt` and `metrics.json`.

## Synthetic slices

Three slices are generated rather than harvested, because no public corpus exists in this model's prompt format. What is real and what is not:

**Agentic traces** (`pipeline/agentic.py`)

- *Real*: every `read_file`, `grep` and `list_dir` result is computed from the actual cloned repository at build time — verbatim file bytes, real regex matches with real line numbers, real directory listings. `edit_file` anchors are exact unique substrings of the real file, so the edits would genuinely apply.
- *Generated*: `run_command` outputs (vitest, pytest, cargo, cmake, eslint) are written to match each tool's real output format; the dialogue and reasoning blocks are generated.
- Every trace is multi-step and contains a failure followed by a recovery, since that is the shape of real agent work.

**Reasoning traces** (`pipeline/reasoning.py`, `pipeline/reasoning_extra.py`)

- 22 topic generators across 3D maths, numerics, shading and graphics debugging. Every numeric result is computed with numpy/`math` at build time, so the arithmetic inside the `<think>` blocks is correct by construction rather than written by hand.

**Vocabulary sweep** (`pipeline/vocab.py`)

- Runs *after* the natural slices are measured, takes the set of ids still unseen, and emits compact wordlists containing them. Each emitted document is re-tokenized and verified: an id only counts once it has actually been observed in tokenizer output, because BPE re-merges adjacent pieces and naive concatenation does not reproduce the tokens you started from.
- This is the honest trade in this dataset. It buys hash-layer coverage that natural text cannot reach at this budget, at the cost of a block of text that is off-distribution for the *learned* routers in layers 3-42. It is a single filterable domain in the manifest for exactly that reason.

## Benchmark contamination

**Checked explicitly.** Every candidate document — 12,330 of them, calibration and eval alike — was scanned against 17 regex families before selection. **1 document matched and was removed.**

Families covered: agent-benches, aime, apps-bench, bigbench-canary, codecontests, deepswe, gpqa, gsm8k, humaneval, livecodebench, math-dataset, mbpp, mmlu, multimodal-benches, reasoning-benches, swebench, terminalbench.

This includes all of the sets named as disqualifying — Terminal Bench, SWE-bench, DeepSWE, GPQA, MMLU, HumanEval, AIME — plus GSM8K, MATH, MBPP, LiveCodeBench, CodeContests, APPS, HellaSwag, WinoGrande, TruthfulQA, BIG-Bench (including its canary GUID), BBH, IFEval, MuSR, AGIEval, C-Eval, CMMLU, ARC, LAMBADA, WebArena, OSWorld, AgentBench, τ-bench, SWE-Lancer, Aider polyglot, MMMU, MathVista, MGSM and DocVQA.

Patterns are deliberately narrow so that ordinary code is not flagged — `DROP` only matches as "DROP benchmark", `ARC` only as `ARC-Challenge`/`ARC-Easy`, and so on. The full pattern list, the hit count and a quoted context window for every single hit are in `contamination_report.txt`, so the claim is auditable rather than asserted.

Two structural points also reduce exposure: no evaluation dataset was downloaded at any stage of this build, and the reasoning slice is generated from parameterised derivations rather than sourced from any problem set.

## Reproducing

```bash
# 1. tokenizer + the official prompt-format reference implementation
hf download deepseek-ai/DeepSeek-V4-Flash-0731 \
    --revision 9e165c30e2704aec5d9d593cce3eebd58bbef1cb \
    tokenizer.json tokenizer_config.json config.json \
    encoding/encoding_dsv4.py encoding/README.md \
    --local-dir ./tok

# 2. source repositories (shallow clones, ~1.3 GB)
bash clone.sh

# 3. previous revision of this dataset, carried forward
hf download AtomicChat/calib-corpora --repo-type dataset --local-dir ./existing

# 4. build: collect -> generate -> dedup -> scan -> balance -> sweep -> split -> measure
python pipeline/build.py --out ./out
```

Requires `transformers`, `tokenizers`, `datasets`, `huggingface_hub`, `numpy`. No GPU and no PyTorch — tokenizer-only. The Wikipedia pull is cached to `~/.cache/calib-build/wiki_cache.jsonl` after the first run; delete it to force a fresh stream.

The build is deterministic given the same inputs: all sampling, splitting and generation is seeded (`seed=20260731`) and every hash key is content-derived. The one source of drift between rebuilds is upstream — the repositories are cloned at `--depth 1` from a moving `HEAD`, so a rebuild months later picks up whatever those projects have merged since.

## Known limitations

- **Clone pinning.** Source repositories are shallow-cloned from `HEAD` rather than pinned to commit SHAs, so exact byte reproduction of this revision is not possible after upstream moves. The manifests record the exact path of every document, and licence and provenance are fixed regardless.
- **The `vocab_sweep` trade-off** described above: it is off-distribution text bought deliberately for hash-layer coverage.
- **`run_command` outputs in agentic traces are generated**, not captured from real runs. File content in those same traces is real.
- **Reasoning is under target** at the measured share rather than the requested 10%; the generators produce genuinely distinct documents and were not padded with near-duplicates to hit the number.
- **Wikipedia is CC-BY-SA-4.0**, which is share-alike. The corpus as a whole is therefore mixed-licence, not permissive — see the per-source table. Anything derived from `calib_train` inherits those terms.