Initial Leanstral calibration corpora release

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README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+language:
+  - en
+tags:
+  - calibration
+  - quantization
+  - lean
+  - mathlib
+  - mxfp4
+  - fp8
+  - datasets
+configs:
+  - config_name: selected
+    default: true
+    data_files:
+      - split: calibration
+        path: data/selected/calibration_samples.jsonl
+  - config_name: variant-1
+    data_files:
+      - split: calibration
+        path: data/variant-1/calibration_samples.jsonl
+  - config_name: variant-2
+    data_files:
+      - split: calibration
+        path: data/variant-2/calibration_samples.jsonl
+  - config_name: variant-3
+    data_files:
+      - split: calibration
+        path: data/variant-3/calibration_samples.jsonl
+  - config_name: variant-4
+    data_files:
+      - split: calibration
+        path: data/variant-4/calibration_samples.jsonl
+  - config_name: variant-5
+    data_files:
+      - split: calibration
+        path: data/variant-5/calibration_samples.jsonl
+  - config_name: variant-6
+    data_files:
+      - split: calibration
+        path: data/variant-6/calibration_samples.jsonl
+  - config_name: development
+    data_files:
+      - split: validation
+        path: data/development/validation.jsonl
+  - config_name: release-validation
+    data_files:
+      - split: validation
+        path: data/release-validation/validation.jsonl
+  - config_name: historical-validation
+    data_files:
+      - split: validation
+        path: data/historical-validation/validation.jsonl
+---
+
+# Leanstral Mathlib calibration corpora
+
+The sample data is derived from the pinned Apache-2.0-licensed Mathlib source
+tree. This repository contains data and curated methods documentation, not the
+separately developed builder package; that package should not be redistributed
+until its standalone license file is resolved.
+
+This dataset contains the calibration corpora used to select a static FP8
+activation profile for an MXFP4 W4A8 conversion of Leanstral 1.5 119B-A6B. It
+publishes all candidate corpora, their manifests, and the shared
+iterative-development pack, and the post-selection release-validation packs.
+Variant 1 is also exposed as the selected subset.
+
+The purpose is reproducibility: users can inspect the exact samples that shaped
+the activation profile, reproduce the corpus comparison, or test a different
+quantization method without reconstructing the data-selection pipeline.
+
+## Loading with 🤗 Datasets
+
+Every corpus is a named Hugging Face configuration. `selected` is the default
+and is byte-identical to Variant 1:
+
+```python
+from datasets import load_dataset
+
+selected = load_dataset(
+    "robbiemu/leanstral-mathlib-calibration-corpora", split="calibration"
+)
+variant_2 = load_dataset(
+    "robbiemu/leanstral-mathlib-calibration-corpora",
+    "variant-2",
+    split="calibration",
+)
+release_validation = load_dataset(
+    "robbiemu/leanstral-mathlib-calibration-corpora",
+    "release-validation",
+    split="validation",
+)
+```
+
+The available configuration names are `selected`, `variant-1` through
+`variant-6`, `development`, `release-validation`, and
+`historical-validation`. They appear as separate subsets in the Hub Dataset
+Viewer and can be passed as the second argument to `load_dataset`.
+
+## Contents
+
+Every calibration variant contains 512 source-grounded records. The original
+four candidates hold the model, quantization rule, sample count, and category
+vocabulary fixed while changing the data distribution.
+
+| Split | Role | Summary | Release status |
+| --- | --- | --- | --- |
+| `variant-1` | empirical baseline | A140/B110/C90/D70/E60/F42, balanced from the measured corpus | **Selected** |
+| `variant-2` | context-enriched | increases surrounding-source context under the same experimental contract | Research candidate |
+| `variant-3` | tail-coverage | targets sparse and long-tail source behavior | Research candidate |
+| `variant-4` | length-stratified | explicitly balances input-length strata | Research candidate |
+| `variant-5` | first composite | coherent corpus assembled from strengths observed in V1–V4 | Research candidate |
+| `variant-6` | constrained V1 composite | 464 V1 records plus 48 D/E short-or-medium substitutions from V5 | Rejected |
+| `development` | iterative validation | shared 100-example comparison pack | Not an untouched test |
+| `release-validation` | final validation | fresh 144-example pack frozen after V1 selection | Quality gate evidence |
+| `historical-validation` | prior validation | earlier 100-example pack retained for provenance | Historical only |
+| `selected` | convenience alias/copy | exact Variant 1 records | **Selected** |
+
+V5 and V6 are complete calibration corpora, not mixtures of activation-scale
+values. Each was recalibrated as one coherent static FP8 profile.
+
+## Suggested repository layout
+
+```text
+README.md
+data/
+  variant-1/calibration_samples.jsonl
+  variant-2/calibration_samples.jsonl
+  variant-3/calibration_samples.jsonl
+  variant-4/calibration_samples.jsonl
+  variant-5/calibration_samples.jsonl
+  variant-6/calibration_samples.jsonl
+  selected/calibration_samples.jsonl
+  development/validation.jsonl
+  release-validation/validation.jsonl
+  historical-validation/validation.jsonl
+manifests/
+  variant-1.json
+  variant-2.json
+  variant-3.json
+  variant-4.json
+  variant-5.json
+  variant-6.json
+  development.json
+  release-validation.json
+  historical-validation.json
+docs/
+  corpus-construction.md
+  calibration-selection.md
+schema.json
+SHA256SUMS
+```
+
+The published manifest for each split should record its source revision,
+builder revision, tokenizer identity, sample count, token count, category and
+length distributions, deduplication inputs, leakage checks, and SHA-256.
+
+## Record format
+
+The quantization input is JSON Lines. At minimum, publication should preserve:
+
+- a stable record identifier;
+- category and context-length stratum;
+- the exact `rendered_text` given to the calibration runtime;
+- tokenizer-derived length;
+- source-location and source-revision provenance;
+- content/fingerprint fields used for deduplication; and
+- admission and leakage-check metadata needed to audit the split.
+
+The release preparation step should publish a generated schema from the actual
+JSONL files rather than relying on this prose alone. Private machine paths and
+internal operational metadata should be removed without changing sample text or
+stable identifiers.
+
+## Construction method
+
+The builder is deterministic from pinned inputs. It:
+
+1. selects source-grounded Lean/Mathlib material from a pinned Mathlib tree;
+2. renders the exact model-facing text;
+3. counts tokens with Leanstral's Tekken tokenizer;
+4. applies source-aware deduplication and leakage exclusions;
+5. assigns category and length strata;
+6. admits records according to the variant's frozen distribution; and
+7. writes the JSONL and a provenance manifest together.
+
+Variant 1 uses the empirical category allocation:
+
+```text
+A 140
+B 110
+C  90
+D  70
+E  60
+F  42
+total 512
+```
+
+The publication methods document should define A–F from the builder's frozen
+category specification and cite the exact pinned Mathlib revision. Those values
+must be copied from the machine manifests during release assembly.
+
+## Why Variant 1 is selected
+
+The candidates were evaluated as frozen static per-tensor FP8 activation
+profiles on the same MXFP4 weights. After FlashInfer startup autotuning was
+identified as the source of small cross-restart numerical drift, V1–V5 were
+compared in five independent normally autotuned cold starts. Every profile used
+the same dynamic reference within each start.
+
+V1 won candidate NLL, signed NLL delta, mean absolute per-example NLL delta,
+top-1 agreement, and top-1 flip count in all five starts. V5 consistently
+improved mean and maximum KL and repaired recurring V1 tail failures, but it
+lost central NLL/top-1 quality in every start and introduced stable failures of
+its own.
+
+Variant 6 tested the strongest remaining simple-composite hypothesis. It kept
+464 V1 records and made 48 targeted D/E substitutions, then was recalibrated as
+one complete profile. V6 improved mean KL in every start, but its V6-minus-V1
+candidate NLL averaged `+0.028728` and top-1 agreement averaged `-0.042`,
+outside the frozen `+0.01` and `-0.01` central-quality margins. The same
+decision holds using starts 2–5 alone.
+
+> Under normal FlashInfer startup variation, V1 consistently provides the best
+> ordinary predictive preservation. The available KL-tail improvements are
+> real, but neither V5 nor a targeted V1-centered composite achieves them
+> without an unacceptable central-quality cost.
+
+The experiment selects the exact frozen V1 corpus/profile pair. It does not
+claim that V1 is universally optimal for every model, quantizer, or future
+recalibration.
+
+## Selected artifact receipt
+
+```text
+Variant 1 calibration JSONL:
+  sha256 57e1e10fce8a6a62cef14e58e26109065104fe109c4075c6b6f302726918ed57
+  records 512
+
+Variant 1 static activation profile:
+  sha256 5480257595178836022477080bc6132e2accff2d6e26c959b092e57a0f911531
+  ModelOpt 0.37.0
+  288 keys / 9,432 finite positive scale values
+
+development pack:
+  sha256 8cdbf9cfcc2b9c1bcb305c14f979799be6a7b9efad54dd4e5a281f00c152ccdf
+
+replicated V1–V5 aggregate:
+  sha256 2a20473486a7a84efb462aff12b28c2319ce86a45ca0ead2d74a919ced270324
+
+Variant 6 corpus:
+  sha256 6b1a712d8ed90b7403b9ffbc3f71a73411eff5c920b3043a2ea17cb255e11856
+
+Variant 6 activation profile:
+  sha256 7f426c18d4ee09d07c4687fca82bce63f7bbdd9c427f230cefb9465213d569ce
+```
+
+## Intended uses
+
+Appropriate uses include:
+
+- reproducing the Leanstral MXFP4 static-FP8 profile experiment;
+- studying calibration-corpus sensitivity;
+- benchmarking alternative activation quantizers; and
+- auditing category, length, expert-coverage, and tail behavior.
+
+This is calibration and iterative-development data. It is not a general
+instruction-tuning corpus or a general model-quality benchmark. The
+`release-validation` split was untouched during profile selection, but it is
+published as the completed release-gate evidence rather than advertised as a
+reusable hidden test.
+
+## Limitations
+
+- The sample-selection result is specific to this model, weight artifact,
+  static max-calibration method, and 100-example development comparator.
+- The validation pack informed V5 and V6.
+- The fresh 144-example release pack was evaluated once after V1 was frozen;
+  its failed Top-1 and KL-tail criteria are disclosed in the model repository.
+- KL measures one next-token top-five-plus-other partition per example, not the
+  worst teacher-forced token position.
+- Normally autotuned cold starts are the independent replication units.
+- Lower overflow-risk counts did not reliably predict better model behavior.
+- The Apache-2.0 declaration applies to the published Mathlib-derived data.
+  Model weights and tokenizer assets are distributed separately in the model
+  repository under the official source model's terms.
+
+## Reproducibility documents
+
+The dataset repository should include a concise construction document. The
+model repository should carry the detailed evaluation receipts:
+
+- activation-profile selection;
+- replicated normally autotuned profile study; and
+- constrained V1-centered composite result.

SHA256SUMS

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+c5f58348db94d3e3db86d8904c3c31ea2b89e304945515118b9db92ae5fcb31b  README.md
+8cdbf9cfcc2b9c1bcb305c14f979799be6a7b9efad54dd4e5a281f00c152ccdf  data/development/validation.jsonl
+92464f7c9f0c7996e22343455c91307cfcf0555220f5b82b3dff7979b54470dc  data/historical-validation/validation.jsonl
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+57e1e10fce8a6a62cef14e58e26109065104fe109c4075c6b6f302726918ed57  data/variant-1/calibration_samples.jsonl
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+e1656c594b1e4cba441bab4c48b5a500b91c2056ce854597f7283355f4ac16fe  data/variant-3/calibration_samples.jsonl
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docs/calibration-selection.md

@@ -0,0 +1,178 @@
+# Activation-profile selection: frozen Variant 1
+
+**Decision:** Use the exact frozen Variant 1 corpus/profile pair for the
+Leanstral MXFP4 W4A8 release candidate.
+
+**Status:** Final development-selection decision. The comparison was repeated
+across five independent, normally autotuned cold starts on the pinned clean
+SGLang/FlashInfer runtime. A constrained V1-centered composite was then built,
+calibrated as one coherent profile, and rejected by its frozen central-quality
+margins.
+
+This closes calibration-data iteration for the current release. It does not
+replace the later untouched-quality or end-to-end performance gates.
+
+> **Final conclusion:** Under normal FlashInfer startup variation, V1
+> consistently provides the best ordinary predictive preservation. The
+> available KL-tail improvements are real, but neither V5 nor a targeted
+> V1-centered composite achieves them without an unacceptable central-quality
+> cost.
+
+## What the experiment selected
+
+The experimental variable was the 512-record calibration corpus and its
+resulting static per-tensor FP8 activation profile. All candidates shared:
+
+- the same packed Leanstral MXFP4 weights and UE8M0 K/32 weight scales;
+- the same static FP8 E4M3 execution contract;
+- the same 100-example iterative-development pack;
+- one dynamic reference within each cold start; and
+- the same pinned SGLang, FlashInfer, seed, and attention backend.
+
+The study selects the exact frozen V1 corpus/profile pair listed below. V1–V5
+were evaluated as fixed profiles on the clean runtime; they were not recollected
+from scratch for each start. This is the correct experiment for choosing the
+release artifact, but it should not be described as proof that V1 is universally
+optimal under every possible calibration run or tactic realization.
+
+The validation pack informed V5 and V6, so this is iterative development
+validation rather than an untouched final test. No scale values were spliced
+between profiles: each composite was recalibrated as one complete profile.
+
+## Clean replicated comparison
+
+Each of five independent starts used a fresh ephemeral container and normal
+FlashInfer autotuning. All profiles were compared against the same dynamic
+reference within that start. The five starts, not the 500 repeated examples,
+are the independent units.
+
+### Central preservation
+
+Values are mean ± sample standard deviation across five starts.
+
+| Profile | Candidate NLL | Signed NLL delta | Mean absolute sample delta | Top-1 | Top-5 | Reference top-1 retained |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| **V1** | **13.93530 ± 0.00145** | **0.04799 ± 0.00282** | **0.08660 ± 0.00144** | **0.620 ± 0.016** | 0.5316 ± 0.0059 | 0.756 ± 0.021 |
+| V2 | 13.96571 ± 0.00128 | 0.07841 ± 0.00219 | 0.12148 ± 0.00181 | 0.578 ± 0.013 | **0.5472 ± 0.0033** | **0.760 ± 0.007** |
+| V3 | 13.96561 ± 0.00129 | 0.07831 ± 0.00281 | 0.09765 ± 0.00085 | 0.550 ± 0.029 | 0.5304 ± 0.0070 | 0.714 ± 0.021 |
+| V4 | 14.02996 ± 0.00105 | 0.14266 ± 0.00246 | 0.13983 ± 0.00045 | 0.550 ± 0.012 | 0.5328 ± 0.0054 | 0.710 ± 0.017 |
+| V5 | 13.97669 ± 0.00120 | 0.08938 ± 0.00195 | 0.09480 ± 0.00038 | 0.574 ± 0.009 | 0.5456 ± 0.0036 | 0.736 ± 0.022 |
+
+V1 won candidate NLL, signed NLL delta, mean absolute per-example NLL delta,
+top-1 agreement, and top-1 flip count in all five starts. Relative to V5, V1
+averaged `0.04139` lower NLL, `0.00820` lower absolute sample delta, and 4.6
+more top-1 agreements per 100 examples.
+
+### KL distribution and severe tail
+
+| Profile | Mean KL | Median KL | p90 KL | p95 KL | Maximum KL | Mean count >1 | Mean count >5 |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| V1 | 0.30680 ± 0.01464 | 0.06017 ± 0.00517 | 0.33877 ± 0.03457 | 0.75950 ± 0.21981 | 7.63164 ± 0.02444 | 4.2 | 3.0 |
+| V2 | 0.29281 ± 0.03467 | 0.06050 ± 0.00167 | **0.33103 ± 0.01065** | 0.96258 ± 0.40017 | 5.85584 ± 0.01128 | 5.2 | 2.8 |
+| V3 | 0.40588 ± 0.02479 | 0.06096 ± 0.00348 | 0.49477 ± 0.02139 | 1.45338 ± 0.67184 | 8.79102 ± 1.16882 | 5.6 | 3.2 |
+| V4 | 0.30840 ± 0.02926 | **0.04335 ± 0.00220** | 0.40978 ± 0.02723 | 1.25240 ± 0.35561 | 5.95024 ± 0.13142 | 6.4 | 2.0 |
+| **V5** | **0.24760 ± 0.02489** | 0.05946 ± 0.00449 | 0.40532 ± 0.02252 | **0.70774 ± 0.07174** | **5.11108 ± 0.06165** | **3.4** | **1.4** |
+
+V5's severe-tail advantage is real, not noise from a single restart. It won
+mean and maximum KL in all five starts, improved all ten recurring V1 tail
+failures in at least four starts, and reduced the recurring category-E rescue
+substantially. It also introduced stable failures of its own and lost V1's
+central NLL/top-1 behavior in every start. V1 and V5 tied on the mean number of
+examples above KL `0.5` (`7.2`). V1 won p95 in three starts and V5 in two.
+
+Mean KL was therefore not used as a soothing single-number selector. Central
+preservation, tail distributions, recurring named failures, categories,
+lengths, routing, fallback, and range diagnostics were kept separate.
+
+## Constrained composite test
+
+Variant 6 tested whether V5-like D/E tail improvements could be added without
+giving up V1's central quality. It retained 464 V1 records and made 48
+deterministic D/E short-or-medium substitutions from V5. It was recalibrated as
+one coherent 512-record profile.
+
+Across five fresh normally autotuned starts:
+
+| Profile | Candidate NLL | Top-1 | Mean KL | Maximum KL | p95 KL |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| **V1** | **13.93502** | **0.618** | 0.30513 | 7.64528 | **0.66222** |
+| V5 | 13.97607 | 0.564 | **0.23958** | **5.61991** | 0.64364 |
+| V6 | 13.96375 | 0.576 | 0.24836 | 5.95601 | 0.72812 |
+
+V6 improved mean KL in every start, but its NLL was worse than V1 in every
+start and its top-1 agreement was lower in every start. Its V6-minus-V1 NLL
+mean was `+0.028728`; the one-sided 99% lower paired-t bound was `+0.025366`,
+above the frozen `+0.01` rejection margin. Its top-1 mean was `-0.042`; the
+one-sided 99% upper bound was `-0.020152`, below the frozen `-0.01` margin.
+Starts 2–5 alone reach the same rejection. V6 was rejected and V1 retained.
+The central loss margins were frozen before the comparison; the reject-only
+futility look was formalized after start 1, so it is not presented as a formal
+study-wide type-I-error guarantee. The independent starts 2–5 sensitivity makes
+the practical decision unchanged.
+
+## Routing, fallback, and range interpretation
+
+Expert coverage was high for all profiles. Across the replicated study, V1
+covered a mean `4,504 / 4,608` layer/expert pairs (`97.7431%`); V5 covered
+`4,496.2 / 4,608` (`97.5738%`). Fallback routing exposure was below `3.5e-7`
+of assignments and did not explain the tail.
+
+V1 averaged 1,525 validation-overflow-risk scale values and V5 averaged
+1,332.2. This is a range diagnostic, not an objective. Lower risk counts did
+not imply better central model behavior, and full-mode maxima do not prove
+per-example clipping. Seam-adjacent coverage remains desirable when the
+behavioral evidence supports it.
+
+## Frozen receipts
+
+```text
+source runtime:
+  SGLang bbb5702a3c7faba97ab425b15808cbe5f3ab6f7c
+  FlashInfer Python/cubin/JIT 0.6.14/0.6.14/0.6.14+cu130
+  runtime image sha256:f74a9c7d37ac724e30bc4841999e15a8fddf87c1e5bab2165041000879f4f152
+
+Variant 1 corpus:
+  inputs/variant-1/calibration_samples.jsonl
+  sha256 57e1e10fce8a6a62cef14e58e26109065104fe109c4075c6b6f302726918ed57
+  records 512
+
+Variant 1 activation profile:
+  runs/mxfp4-calibration/variant-1/activation_profile.json
+  sha256 5480257595178836022477080bc6132e2accff2d6e26c959b092e57a0f911531
+  ModelOpt 0.37.0
+  keys 288
+  finite positive values 9,432
+
+development-validation pack:
+  validation/validation.jsonl
+  sha256 8cdbf9cfcc2b9c1bcb305c14f979799be6a7b9efad54dd4e5a281f00c152ccdf
+
+replicated V1-V5 aggregate:
+  runs/clean-upstream-main-study/gate2-fi0614/replicated-autotuned-profile-study/aggregate.json
+  sha256 2a20473486a7a84efb462aff12b28c2319ce86a45ca0ead2d74a919ced270324
+
+Variant 6 corpus/profile:
+  corpus sha256 6b1a712d8ed90b7403b9ffbc3f71a73411eff5c920b3043a2ea17cb255e11856
+  profile sha256 7f426c18d4ee09d07c4687fca82bce63f7bbdd9c427f230cefb9465213d569ce
+
+Variant 6 futility report:
+  runs/clean-upstream-main-study/gate2-fi0614/constrained-composite-fresh-study/futility-report.json
+  sha256 2a1e17844838bf950eec3fe157a029132d608f67fcd811e0fd1bf8b363d0c717
+```
+
+The detailed reports are `docs/replicated-autotuned-profile-study.md` and
+`docs/constrained-v1-composite-result.md`.
+
+## Scope of the decision
+
+This evidence is sufficient to freeze V1 for the release candidate. It does
+not yet establish final generalization, acceptable untouched-pack quality, or
+an end-to-end speed advantage over W4A16. Those are later release gates and
+must use the clean pinned runtime. Reopen corpus design only if new evidence
+identifies a reproducible release-blocking failure that the frozen V1 artifact
+cannot tolerate.
+
+The result also closes simple record-substitution composite development. A new
+attempt would need a genuinely different mechanism—such as layer-specific
+calibration, constrained scale optimization, or another activation-quantization
+scheme—not another unprincipled mixture of the same records.

docs/corpus-construction.md

@@ -0,0 +1,183 @@
+# Corpus construction and split design
+
+## Purpose
+
+These corpora were built as post-training quantization inputs for Leanstral
+1.5 119B-A6B. They represent the text presented to the model during Lean and
+Mathlib workflows. They are not proof-agent rollouts, instruction-tuning
+examples, or correctness labels.
+
+The builder treats deterministic source and validation rules as authoritative.
+Model-assisted review may flag a candidate for inspection, but it cannot admit
+a record that fails deterministic checks or overturn a deterministic decision.
+
+## Pinned inputs
+
+```text
+Mathlib commit:
+  5e932f97dd25535344f80f9dd8da3aab83df0fe6
+
+Lean toolchain:
+  leanprover/lean4:v4.29.1
+
+LeanDojo version:
+  1.0.9
+
+builder project revision recorded by V1:
+  05e9d49ed350cd358ce0314cb0c563850b8fd8c7
+
+DataSeek revision recorded by V1:
+  4fbe94ef64ee1f07f80a1bd8c52e0889f4e4e33a
+
+tokenizer:
+  mistralai/Leanstral-1.5-119B-A6B:tekken.json
+
+chat rendering:
+  mistral-common Tekken v15, Mistral [INST]/[SYSTEM_PROMPT] template
+```
+
+Final token counts use the real target Tekken tokenizer and chat rendering.
+Records retain both structured `messages` and the exact `rendered_text` sent to
+calibration.
+
+## Six deployment-shaped categories
+
+The 512-record empirical distribution is fixed at A140/B110/C90/D70/E60/F42.
+
+| Code | Category | Input represented |
+| --- | --- | --- |
+| A | Proof completion | A real theorem statement and bounded source context with the proof hidden. |
+| B | Proof repair with diagnostics | A controlled broken proof or source fragment paired with the real Lean diagnostic. |
+| C | Retrieval/context-heavy proof | A theorem request with source-grounded premise snippets, imports, or broader context. |
+| D | Repository-local edit | A bounded file excerpt, target declaration, and concrete edit request. |
+| E | Proof trace/tactic state | Source-derived proof-state or tactic-sequence text without requiring a live model rollout. |
+| F | Explanation/formalization | Lean explanation, translation, or natural-language-to-Lean instructions. |
+
+The categories are input-distribution strata. Acceptance does not depend on
+whether Leanstral answers the prompt correctly.
+
+## Deterministic construction pipeline
+
+For every candidate, the builder:
+
+1. extracts a declaration, source span, module, and related source material
+   from the pinned Mathlib tree;
+2. constructs a category-specific prompt while withholding material that would
+   leak the requested proof;
+3. records canonical GitHub source provenance at the pinned commit;
+4. renders the exact model-facing chat text;
+5. counts tokens with the target Tekken tokenizer;
+6. measures category, source, code/text ratio, context length, retrieval and
+   trace signals;
+7. rejects hidden-proof leakage, malformed provenance, trivial tasks, and
+   exact or near duplicates according to deterministic rules; and
+8. writes accepted and rejected records plus a run manifest and measurements.
+
+`admission.soft_review_applied_to_decision` remains false. Soft review is
+advisory and is not part of the admission authority.
+
+## Record identity and leakage controls
+
+Each published record carries a stable `sample_id`, source declaration, source
+file, rendered text, and duplicate fingerprint. Split overlap was checked using
+multiple identities rather than the sample ID alone:
+
+- sample ID;
+- declaration;
+- source file where the protocol requires file-level separation;
+- normalized rendered text;
+- normalized user text; and
+- the recorded duplicate fingerprint.
+
+The 100-example `development` pack has zero sample-ID and fingerprint overlap
+with the calibration variants. Its results informed V5 and V6, so it is
+explicitly iterative validation rather than an untouched test.
+
+The 144-example `release-validation` pack was frozen only after V1 had been
+selected. It spans 138 source files and records zero overlap with V1–V6, the
+development pack, and the historical validation pack across the protocol's
+five identity checks. Model outputs were not used to choose its records.
+
+## Calibration variants
+
+### Variant 1: empirical baseline
+
+V1 is the original 512-record A140/B110/C90/D70/E60/F42 distribution. It is
+the selected release corpus.
+
+### Variant 2: context enriched
+
+V2 retains the category allocation while enriching eligible A, D, and F
+records with real imports, namespaces, neighboring declaration statements,
+and source-derived lemma hints. It tests whether V1 is under-contextualized.
+
+### Variant 3: tail coverage
+
+V3 changes category allocation to A100/B110/C120/D80/E80/F22 and deliberately
+increases medium/long C, D, and E coverage. It tests whether a larger
+long-context tail improves preservation.
+
+### Variant 4: category-preserving length stratification
+
+V4 retains A140/B110/C90/D70/E60/F42, builds an oversampled candidate pool,
+and deterministically selects for greater length spread inside each category.
+Eligible A, D, and F records receive the same source-grounded enrichment used
+by V2.
+
+### Variant 5: broad composite
+
+V5 was designed after the first four profiles were evaluated. It selects whole
+records—not scale values—from V1–V4 while retaining the empirical category
+totals. Its final source counts are V1 363, V2 36, V3 45, and V4 68. The
+development pack informed this design.
+
+### Variant 6: constrained V1-centered composite
+
+V6 retains 464 V1 records and substitutes 40 category-D plus 8 category-E
+short/medium records drawn through V5's candidate evidence. Pairing is
+deterministic, generally by nearest input-token count inside the category. It
+was calibrated as one coherent profile; activation scales were never spliced
+between profiles.
+
+V6 improved mean KL relative to V1 in all five replicated starts but exceeded
+the frozen NLL and Top-1 rejection margins in every start. It is published as
+a negative experimental result, not a selected subset.
+
+## Why Variant 1 is selected
+
+Five normally autotuned cold starts evaluated frozen V1–V5 profiles against
+one shared dynamic reference within each start. V1 won candidate NLL, signed
+and absolute NLL preservation, Top-1 agreement, and flip count in all five
+starts. V5's lower severe KL tail was real, but it consistently sacrificed the
+central metrics. The fresh V6 test transferred much of that tail improvement
+and reproduced the same central-quality cost.
+
+The conclusion is specific and deliberately limited:
+
+> Under normal FlashInfer startup variation, the exact frozen V1 corpus/profile
+> pair best preserves ordinary predictive behavior under the project's frozen
+> priorities. The available V5/V6 tail improvements could not be transferred
+> without an unacceptable NLL and Top-1 cost.
+
+## Length buckets
+
+The serialized records use these measured Tekken ranges:
+
+```text
+tiny:    <512 tokens
+short:   512–2,047 tokens
+medium:  2,048–8,191 tokens
+long:    8,192–32,767 tokens
+xlong:   >=32,768 tokens
+```
+
+The public per-split manifests are generated from the JSONL files and contain
+the actual category, length, token, source, and checksum statistics.
+
+## Reproduction scope
+
+The repository publishes the exact data and a methods description sufficient
+to audit selection and reuse the samples with another quantizer. The original
+builder workspace contains additional operational integrations and historical
+runs that are intentionally not copied into this clean data release. That
+omission does not change any published record or checksum.

manifests/development.json

@@ -0,0 +1,41 @@
+{
+  "accepted_for_ptq_calibration": 100,
+  "approximate_token_records": 0,
+  "category_counts": {
+    "A": 27,
+    "B": 21,
+    "C": 18,
+    "D": 14,
+    "E": 12,
+    "F": 8
+  },
+  "file": "data/development/validation.jsonl",
+  "format": "leanstral_public_split_manifest_v1",
+  "incomplete_provenance_records": 0,
+  "length_bucket_counts": {
+    "long": 2,
+    "medium": 9,
+    "short": 27,
+    "tiny": 62
+  },
+  "mathlib_commits": [
+    "5e932f97dd25535344f80f9dd8da3aab83df0fe6"
+  ],
+  "records": 100,
+  "role": "iterative development validation; informed V5 and V6",
+  "selected": false,
+  "sha256": "8cdbf9cfcc2b9c1bcb305c14f979799be6a7b9efad54dd4e5a281f00c152ccdf",
+  "split": "development",
+  "token_counts": {
+    "maximum": 17176,
+    "minimum": 136,
+    "total": 119291
+  },
+  "tokenizer_ids": [
+    "mistralai/Leanstral-1.5-119B-A6B:tekken.json"
+  ],
+  "unique_declarations": 100,
+  "unique_duplicate_fingerprints": 100,
+  "unique_sample_ids": 100,
+  "unique_source_files": 95
+}

manifests/historical-validation-protocol.json

@@ -0,0 +1,137 @@
+{
+  "candidate_pool": {
+    "admitted": 168,
+    "eligible_after_prior_filter": 168,
+    "optional_skipped": {
+      "B": 0,
+      "E": 0
+    },
+    "rejected_prior_overlap_by_identity": {},
+    "targets": {
+      "A": 41,
+      "B": 34,
+      "C": 29,
+      "D": 24,
+      "E": 22,
+      "F": 18
+    }
+  },
+  "category_counts": {
+    "A": 37,
+    "B": 30,
+    "C": 25,
+    "D": 20,
+    "E": 18,
+    "F": 14
+  },
+  "format": "leanstral_untouched_quality_pack_v1",
+  "length_bucket_counts": {
+    "long": 5,
+    "medium": 9,
+    "short": 50,
+    "tiny": 80
+  },
+  "mathlib_commit": "5e932f97dd25535344f80f9dd8da3aab83df0fe6",
+  "maximum_records_per_source_file": 3,
+  "overlap": {
+    "counts": {
+      "declaration": 0,
+      "file": 0,
+      "rendered_text": 0,
+      "sample_id": 0,
+      "user_text": 0
+    },
+    "prior_files": [
+      {
+        "records": 512,
+        "sha256": "57e1e10fce8a6a62cef14e58e26109065104fe109c4075c6b6f302726918ed57"
+      },
+      {
+        "records": 512,
+        "sha256": "7450f6b0c70c88c59bf0a0949cdacaaaad8b1f676775923470cce0d6fabfdbd3"
+      },
+      {
+        "records": 512,
+        "sha256": "e1656c594b1e4cba441bab4c48b5a500b91c2056ce854597f7283355f4ac16fe"
+      },
+      {
+        "records": 512,
+        "sha256": "31604f97434f5f45c77e06c64fad989b5dd48780551dfad4d68bff0121717796"
+      },
+      {
+        "records": 100,
+        "sha256": "8cdbf9cfcc2b9c1bcb305c14f979799be6a7b9efad54dd4e5a281f00c152ccdf"
+      }
+    ]
+  },
+  "prefixes": [
+    "Mathlib.AlgebraicTopology",
+    "Mathlib.Computability",
+    "Mathlib.Condensed",
+    "Mathlib.Control",
+    "Mathlib.Dynamics",
+    "Mathlib.Lean",
+    "Mathlib.Logic",
+    "Mathlib.Tactic"
+  ],
+  "primary_targets": {
+    "A": 33,
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manifests/historical-validation.json

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manifests/release-validation-protocol.json

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manifests/release-validation.json

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manifests/selected.json

@@ -0,0 +1,42 @@
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manifests/variant-1.json

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manifests/variant-2.json

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manifests/variant-3.json

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manifests/variant-4.json

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manifests/variant-5-design.json

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