marcelroed/gigatoken erreicht einen Gesundheitsindex von 50 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Sustainability & Governance (54/100) ab, am schwächsten bei Vitality (46/100). Zuletzt heute aktualisiert. Ein einzelner Mitwirkender trägt den Großteil der jüngsten Arbeit.
50
gesamt / 100
Mittel
Software-Gesundheitsindex
Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.
50
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
Bewertungsprofil
Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.
151 Follower91 öffentliche Reposseit Juli 2015Stanford
Dieses Repository gehört einem persönlichen Konto. Ein Projekt mit nur einem Eigentümer trägt ein höheres Kontinuitätsrisiko als ein organisationsgetragenes.
Liefert Releases aus — keine Releases veröffentlicht
0/36
Release-Aktualität — keine Releases
0/27
Release-Rhythmus — keine Releases
0/10
OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count
0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.
Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.
Could not fetch crates package 'gigatoken' from its registry
GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
deps.dev does not index pypi:gigatoken@0.9.0; advisories assessed against the repository dependency graph instead
Weitere Details
Stern- und Fork-Verlauf 115 ★ / 5 ⇿
115Sterne
5Forks
Wann jeder Stern und Fork hinzugefügt wurde, von GitHub erfasst und nach Tagen gruppiert. Das kumulierte Wachstum steht direkt über den täglichen Zugängen, aus denen es besteht, sodass beide gegeneinander lesbar sind: stetiger organischer Zuwachs sieht ganz anders aus als ein abrupter, kurzlebiger Ausschlag. Wo dieser Unterschied messbar ist, wird er als Wachstumsauthentizität ausgewiesen.
OpenSSF Scorecard 4.8 / 10
4.8Gesamtwert
Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-22 02:08 UTC
Der aufgelöste Abhängigkeitssatz konnte für diesen Bericht nicht erhoben werden: GitHub dependency-graph SBOM unavailable (404); the dependency graph may be disabled for this repository
JSON-Rohbericht maschinenlesbar
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"body": "Same sweep on a dual-socket AMD EPYC 9565 (288 threads): gigatoken\n15.5-24.5 GB/s on the BPE families and 2.5-4.8 GB/s on SentencePiece.\nTables now follow an explicit CPU_ORDER list (new machines appended\nunderneath) instead of sorting by peak speed, and the SP footnote no\nlonger names a row position since sort order differs per machine.\n\nClaude-Session: https://claude.ai/code/session_013cwmfWS1kEXh3C9S2st3WU",
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"body": "The README table names one representative per tokenizer, which hid\nnon-obvious members (MiMo under Qwen, SmolLM3 under Llama 3.3,\nGLM-4.7-Flash under GLM 5). Coverage footnotes are back, as hand-curated\nfamily lists per row: automatic suffix-stripping of the scanned repo names\nkept producing artifac\n[…]\no the COVERS map in results.py is written by hand from the\nverified families.json scan data and should be updated alongside it.\n\nClaude-Session: https://claude.ai/code/session_013cwmfWS1kEXh3C9S2st3WU",
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"headline": "List the model families covered by each benchmark row",
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"body": "benchmarks/compare/ holds the pipeline: measure.py times one library\n(gigatoken/HF tokenizers/tiktoken) per fresh process on one file, named by\nHF repo id for every library so measurements line up; tiktoken runs only\nrepos its own model registry resolves (gpt2, gpt-oss), never converted\nvocabs. swee\n[…]\n(sp-parallel branch); all others on main at 34c1e25. M4 Max, 11.9 GB\nOpenWebText: BPE tokenizers 5.5-8.8 GB/s, SP 1.4-2.0 GB/s.\n\nClaude-Session: https://claude.ai/code/session_013cwmfWS1kEXh3C9S2st3WU",
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"body": "Parallelize SentencePiece encoding within oversized documents",
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"headline": "Merge pull request #21 from marcelroed/sp-parallel",
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"body": "A single huge document through an SP-backed tokenizer previously encoded\nserially (~154 MB/s on a 290 MB doc); only multi-document batches\nparallelized. Oversized SP documents are now fragmented at provably safe\nboundaries and encoded across the rayon pool, mirroring the BPE path's\ninternal chunking\n[…]\ns 154 MB/s serial).\n\nbenches/encode.rs no longer pre-splits SP input: both backends now\nreceive the whole file as one document.\n\nClaude-Session: https://claude.ai/code/session_013cwmfWS1kEXh3C9S2st3WU",
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"headline": "Add bibtex for citation",
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"body": "Refuse unsupported tokenizer model families by name",
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"headline": "Merge pull request #19 from marcelroed/model-type-errors",
"author_name": "Marcel Rød",
"author_login": "marcelroed",
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"oid": "022ee7a3a9f0ddd93126098c5e22e9cc531f5a4f",
"body": "WordPiece and Unigram tokenizer.json files are valid JSON with a\ndifferent model shape, so deserializing them into the BPE schema failed\nwith misleading errors (\"missing field `merges`\", \"invalid type:\nsequence, expected a map at column 9096716\") that read as file\ncorruption. Probe model.type before\n[…]\noberta)\nand `max_input_chars_per_word` (WordPiece: bert-base-uncased).\n`continuing_subword_prefix` cannot serve as the WordPiece marker — BPE\nserializes it too (the original gpt2 upload has it as \"\").",
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"body": "Support fairseq-ordered vocabs, added-token lstrip/rstrip, and ByteLevel add_prefix_space",
"is_bot": false,
"headline": "Merge pull request #18 from marcelroed/parity-fixes",
"author_name": "Marcel Rød",
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"body": "- Restore the doc comment and #[inline(never)] that the outlined ranked\n miss path had displaced off encode_pretoken_miss (the insertion landed\n between the attribute and the function, silently un-outlining the hot\n miss path and double-attributing the ranked one).\n- Collapse the private AddedTok\n[…]\nken_overwrites/Piece doc comment (pre-existing).\n\nNet -103 lines. 182/182 tests pass; parity re-validation unchanged\n(20/21 fixed, 11/11 sanity exact); interleaved gpt2 A/B within the A/A\nnoise floor.",
"is_bot": false,
"headline": "Simplify the parity-fix additions after multi-agent review",
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"body": "…vel add_prefix_space\n\nThree ByteLevel-BPE parity gaps found by the top-1000 HF model sweep\n(19 tokenizer groups / 42 models move from mismatch to exact vs HF):\n\n- Fairseq-heritage vocabs (RoBERTa/OPT/DeBERTa/BLIP2/CLAP): vocab IDs are\n frequency-ordered, so merge priority cannot be the merged toke\n[…]\nPerf: interleaved A/B vs main (min-of-N, ST gpt2/qwen2.5/phi-4 cold+warm,\nMT gpt2/phi-4) shows all deltas inside the A/A noise floor, with token\nstreams byte-identical to main on all three ST configs.",
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"headline": "Support fairseq-ordered vocabs, added-token lstrip/rstrip, and ByteLe…",
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"body": "Config-driven tokenizer dispatch; generalize the Kimi loader",
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"headline": "Merge pull request #17 from marcelroed/config-dispatch",
"author_name": "Marcel Rød",
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"body": "Loading from a repo id, directory, or vocab-file path now dispatches on\ntokenizer_config.json as the prime source: its tokenizer_class/auto_map\nidentify registered tokenizer lines (repos shipping no tokenizer.json),\nand anything else routes to the tokenizer.json path — replacing the\nexception-driven\n[…]\n become\nload_tiktoken_model/from_tiktoken_model taking a named pretokenizer\nscheme (PretokenizerType::from_name), since only the scheme — defined\nsolely in each line's remote code — was Kimi-specific.",
"is_bot": false,
"headline": "Config-driven tokenizer dispatch; generalize the Kimi loader",
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"body": "Support the moonshotai Kimi tokenizer line",
"is_bot": false,
"headline": "Merge pull request #16 from marcelroed/kimi-support",
"author_name": "Marcel Rød",
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"body": "…, VL, Moonlight)\n\nThe Kimi repos ship no tokenizer.json: a shared tiktoken.model rank file\n(byte-identical across all 10 repos) plus per-repo special tokens in\ntokenizer_config.json, with the pretokenizer regex living in remote code.\nThe pattern is the o200k scheme with a leading [\\p{Han}]+ alterna\n[…]\n/code cases, specials, 5079/5079 OWT docs,\n89/89 repo source files, 20k adversarial fuzz strings, zh-wikipedia.\nMask-vs-scalar matches on full ~12 GB OWT (2.37B tokens). MT throughput\n5.4 GB/s on OWT.",
"is_bot": false,
"headline": "Support the moonshotai Kimi tokenizer line (K2/K2.5/K2.6/K2.7, Linear…",
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"headline": "Load from HF directly from Rust instead of relying on Python. Use thi…",
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"headline": "Add colors to the CLI output",
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"headline": "Fix annoying code block format",
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"headline": "Add fun fact about tokenization speed",
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"headline": "Clean up CLI output in README",
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"headline": "Remove (by MB/s) in CLI bench output",
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"headline": "Bump version, update README",
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"headline": "Show CPU in cli bench",
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"body": "Zen 5 round 2: vpcompressb flatten_bits, merge-scan rank prefetch, qwen2-family classifier cuts",
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"headline": "Merge pull request #14 from marcelroed/worktree-encode-profiling",
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"body": "…en2-family classifier cuts\n\nProfile-guided follow-up to the §7 campaign (profiling/x86_port_plan.md\n§8 has the full round log). Full-dataset (11.9 GB OWT) encode_st:\ngpt2 939 -> 1007 MB/s (+7.2%), Qwen3-8B 807 -> 862 MB/s (+7.0%);\ntoken counts bit-identical throughout.\n\n- vpcompressb flatten_bits (\n[…]\n\nRebased onto a4bcfcb (x86-tier fill monomorphization); §8's rebase note\nrecords the re-expression. Numbers predate the rebase.\n\nClaude-Session: https://claude.ai/code/session_01Gp1DAKshDULeWydpMXBVXc",
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"body": "The state-machine, combinator, portable-SIMD, and AVX-512 prototype\npretokenizers are benchmark baselines and test oracles only — nothing\nin the encode path uses them. Group them under reference/ with a\nmodule doc saying so, and drop the AVX-512 prototype's short-lived\nruntime gating: as a baseline it stays compile-time gated (build with\ntarget-cpu=native to include it), unlike the production scanners,\nwhich runtime-dispatch on any build.",
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"body": "Hoist the AVX-512/AVX2 choice from a branch+call per 64-byte batch to\nonce per fill: fill_spans_two_phase dispatches into target_feature\nwrappers monomorphized on (CRC, tier), so the batch classifiers inline\nand the fill loop's codegen matches a target-cpu=native build. The\nper-family avx512/avx2 cl\n[…]\ns collapse into one\ninline(always) const-generic body each; next_span keeps runtime\ndispatch through thin per-tier wrappers (the shared body regresses\n25-30% if instantiated outside a feature region).",
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"headline": "Use a wider range for speedup claim",
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"body": "Serve HuggingFace files from the standard HF cache without heavyweight imports",
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"headline": "Merge pull request #12 from marcelroed/hf-standard-cache",
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"body": "…t imports\n\nTest fixtures and benches no longer copy HF downloads into repo data/;\nthey resolve paths straight from the standard HF cache. Cached files are\nfound with a pure-filesystem lookup (new stdlib-only hf_hub_cache_dir /\ncached_hub_file in gigatoken._load.hub, also used as a fast path by\ndown\n[…]\nload. The r50k tiktoken\nfile (non-HF) moves to ~/.cache/gigatoken-tests/. The suite passes with\nno repo data/ directory at all.\n\nClaude-Session: https://claude.ai/code/session_011AyPaJoBHetCaU534nw7Bj",
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"body": "Add ParquetFileSource",
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"headline": "Merge pull request #11 from marcelroed/parquet-source",
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"author_login": "marcelroed",
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"body": "ParquetFileSource(paths, column=\"text\") reads one document per row from a\nstring or binary column. Rows are materialized as owned buffers via the\narrow-rs parquet reader (parallel over row groups, row order preserved,\nnulls become empty documents to keep results row-aligned), then encoded\nthrough th\n[…]\nrquet paths default to column\n\"text\" for both encode_files and train_bpe.\n\nThe optional polars-based training path and its 'parquet' cargo feature\nare removed; parquet support is now always available.",
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"body": "Replace Rust examples with Python examples; drop unusable BPETokenizer() constructor",
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"headline": "Merge pull request #10 from marcelroed/worktree-examples-py",
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"body": "It loaded a hardcoded ~/data/tokenizers/r50k_base.tiktoken, a dev\nleftover that fails anywhere that file doesn't exist. Construct through\ngigatoken.Tokenizer or the from_tiktoken/from_hf staticmethods instead.",
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"headline": "Remove the no-arg BPETokenizer constructor",
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"body": "Four self-contained scripts covering the common use cases: the gigatoken\nAPI (quickstart), file/buffer sources (encode_files), and the HuggingFace\nand tiktoken drop-in wrappers. The old Rust examples were dev leftovers.",
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"headline": "Replace Rust examples with minimal Python examples",
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"headline": "Add download for the example dataset used to test tokenization speed",
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"headline": "Minor clarification",
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"headline": "Relock",
"author_name": "Marcel Rød",
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"headline": "Update benchmark using BytesSource, relock",
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"body": "Add BytesSource: separator-splitting inside the encode for in-memory batches",
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"headline": "Merge pull request #9 from marcelroed/bytes-source",
"author_name": "Marcel Rød",
"author_login": "marcelroed",
"committed_at": "2026-07-16T16:43:38Z",
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"headline": "Slightly less bold headline",
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"body": "…batches\n\nencode_batch (all variants) now accepts a BytesSource — borrowed byte\nbuffers plus an optional document separator — and splits documents on the\nseparator inside Rust during the parallel encode, via the same lazy\ntwo-level region machinery encode_files uses (chunk boundaries probed at\n~targ\n[…]\n conversion.\n- The CLI passes separators through (BytesSource with --in-memory,\n TextFileSource otherwise) instead of splitting in Python; byte counts\n are now whole-file bytes, separators included.",
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"headline": "Add BytesSource: separator-splitting inside the encode for in-memory …",
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"headline": "Some rewording, fix typos",
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"headline": "Small comment on macOS benchmarks",
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"headline": "Bump patch version, update README",
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"headline": "Add small FAQ section",
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"headline": "Fix README gigatoken API example, clarify plot text",
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"headline": "Add back winnow to avoid build issues",
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"body": "Add o200k and Nemotron-3 fast pretokenizers, gemma-3/4 SP support",
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"headline": "Merge pull request #7 from marcelroed/o200k-support",
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"body": "Factor the duplicated ASCII CaseState initializer, drop an unused\npub(crate), merge duplicate test scaffolding, and document that the mark\nbad-smear's stragglers are covered by MaskState's resume masking (with a\npadded case pinning the 4-byte-follower behavior).\n\nClaude-Session: https://claude.ai/code/session_0176ZSLsz7QhxfirEC2RDxio",
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"body": "- o200k_family: shared scalar walker + mask-scanner boundary algebra for\n the o200k_base regex (gpt-oss, GPT-4o) and the Nemotron-3 variant\n (const-generic over CONTRACTIONS/DIGITS3). Cased letter runs, suffix\n contractions, and [\\r\\n/]* punct tails; O200kCharClass packed table.\n- SP loader: acce\n[…]\ndigits (the pd\n seed cannot see the straddling char); found by the o200k port's\n differential fuzz, regression test included.\n\nClaude-Session: https://claude.ai/code/session_0176ZSLsz7QhxfirEC2RDxio",
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"headline": "Add GLM 5.2 test, fix ignore_merges for weird vocab entries",
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"body": "Encode performance: SIMD pretokenization, memoized encode pipeline, tokenizer coverage, and the encode-opt campaign",
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"headline": "Merge pull request #6 from marcelroed/encode-perf",
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"body": "Encode optimization campaign: cold/warm encode pins, x86-64 port, MT overlap, sequential batch paths",
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"headline": "Merge pull request #5 from marcelroed/encode-opt-main",
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"body": "encode-opt-x86 madvises each parallel chunk's token output before the\nencode's stores fault it in; the sequential BPE paths added on this\nbranch reserve the same kind of buffer at whole-batch scale (and the\nparallel path's Committer reservation already gets the hint), so give\nthem the same treatment. The SP serial paths grow from empty like the\nSP parallel chunk encoders, so there is nothing to hint there.\n\nClaude-Session: https://claude.ai/code/session_01MCLuW9pUhvDjuFeGxsFWEn",
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"headline": "Extend the merged hugepage hint to the serial encode output buffers",
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"headline": "Merge remote-tracking branch 'origin/encode-opt-x86' into seq-mp-threads",
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"body": "…s, emit-loop mask fold, wider dense rank grid\n\nFull-dataset (11.9 GB OWT): encode_st 763 -> 948 MB/s (+24%),\n16-thread encode 5.15 -> 6.1 GB/s (+18%), token counts bit-identical.\nDriven by the perf deep-profile in profiling/zen5_st_profile.md; the\ncampaign log lives in profiling/x86_port_plan.md §7\n[…]\nral\nscalar folds, or x86-only paths; NEON dispatch and table shapes are\nunchanged except dense_log2 (arch-neutral, A/B'd here).\n\nClaude-Session: https://claude.ai/code/session_015V7Rv4HMfFSesXchG5hiWP",
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"headline": "Zen 5 profile-guided encode optimization: THP ordering/alignment fixe…",
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"body": "Batch encodes fan out on rayon's process-global pool, which composes\nbadly with Python multiprocessing: every worker sizes a pool to all\ncores, and a pool built before os.fork has no threads in the child, so\na parallel encode there waits forever (verified empirically).\n\nRust: every batch entry point\n[…]\nparallel/sequential output identity on both backends, and the fork-\nafter-warmup regression that deadlocked before this change.\n\nClaude-Session: https://claude.ai/code/session_01MCLuW9pUhvDjuFeGxsFWEn",
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"headline": "Sequential batch paths + auto-detection for multiprocessing workers",
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"body": "…rt-merge SIMD port measured negative\n\nEverything runs and verifies on x86_64 (Ryzen 7 9800X3D, AVX-512, baseline\nruntime-dispatch build): full suite plus the heavy OWT differentials (50 MB\nmemoized, 1 GB public + join, 200 MB multi-tokenizer, 1 GB parallel ragged\nin both LPT modes, family/r50k mask\n[…]\ntial-covered by short_merges_match_vec_merge_loop on\nany x86 host (both tiers forced explicitly, not just the dispatcher pick).\n\nClaude-Session: https://claude.ai/code/session_015V7Rv4HMfFSesXchG5hiWP",
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"headline": "Zen 5 session: first x86 metal validation of the encode-opt port; sho…",
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"body": "…ied neutral)\n\n- encode_with_added_tokens{,_flat}: replace the two 6-arm concrete-\n pretokenizer matches with PretokenizerType::pretokenize enum dispatch —\n superseded since 20bd089, when FastPretokenizerDispatch's PretokenSpans\n impl moved scheme dispatch to once per 256-pretoken chunk fill; the\n[…]\n03 vs 1002 MB/s (8 pairs, +0.1%) — neutral. Hot-symbol\ninstruction histograms match control modulo regalloc/address immediates.\n\nClaude-Session: https://claude.ai/code/session_0148GT36xf76W9YNHtp7AmJ4",
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"headline": "Simplify dispatch, fill, prefetch, and Committer structure (A/B-verif…",
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"body": "…ant docs\n\nReviewed simplifications with no hot-path codegen impact:\n- tiktoken.rs: shared from_tables construction tail; apply_added_token_overwrites\n is now the single body of the cache-seed sync invariant (fork_sized +\n set_added_tokens); forward vocab-seed iteration (order provably irrelevant\n\n[…]\nuite green (65 passed). Campaign-history numbers live in\nprofiling/campaign_report.md; code comments now state invariants only.\n\nClaude-Session: https://claude.ai/code/session_0148GT36xf76W9YNHtp7AmJ4",
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"headline": "Simplify post-campaign accretions: shared helpers, test dedup, invari…",
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"body": "… math\n\nReview findings F1/F2 on the x86 port (opt/x86-port @ 4b352fe):\n\nF1: the SSE4.2 CRC hash arm was compile-time gated\n(cfg(target_feature = \"sse4.2\")), which ships in NO artifact: wheels/CI\nset no target-cpu/target-feature flags and distributed wheels must stay\nbaseline x86-64, so only local -\n[…]\nkens (exact);\ncargo check --release --target x86_64-apple-darwin clean both baseline\nand with RUSTFLAGS='-C target-cpu=znver2'.\n\nClaude-Session: https://claude.ai/code/session_013C91S1p8LohRQtdHKW8qfW",
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"headline": "Runtime-select the CRC32C pretoken hash on x86_64; fix plan-doc clamp…",
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"headline": "Merge branch 'opt/x86-port' into encode-opt-main",
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"headline": "x86-64 port of the encode-opt aarch64 pins: probe_pair cmov-of-values…",
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"body": "…onsolidation, agent ledger\n\n- §10: opt/split-table (288fa8c) rejected with interleaved A/B numbers\n (MT -22%, ST -32%); correct but ALU/spill costs beat the footprint win\n on M4; noted as a low-priority EPYC retry starting from the branch.\n- §11: final fresh-eyes review summary (no critical/major\n[…]\naudit-verified).\n- §13: campaign ledger — 30 Fable subagents by role, plus the in-flight\n x86-port-prep agent on opt/x86-port.\n\nClaude-Session: https://claude.ai/code/session_013C91S1p8LohRQtdHKW8qfW",
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"headline": "Campaign report addendum: split-table rejection, final review, test c…",
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"body": "The pre-move base pointer is now used only during the shared commit\nphase (Committer unmoved); finish() re-derives the destination from the\nVec after destructuring, so no write or set_len goes through a pointer\nthat predates a Vec move (strict-aliasing retag hazard flagged by the\nfinal fix review). SyncPtr::at carries its bounds contract as unsafe fn.\n\nClaude-Session: https://claude.ai/code/session_013C91S1p8LohRQtdHKW8qfW",
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"body": "…pt campaign\n\nThe five optimization rounds merged test modules from several agent\nbranches into src/bpe/tiktoken.rs; this deduplicates and tidies them\nwithout changing any kept test's logic:\n\n- Drop verify_heavy's boundary-fuzz / edge-length tests and their helpers\n (check_partition, check_scheme_e\n[…]\ndata/owt_train.txt and data/*.json\npass when run individually; the three ~12 GB full-OWT tests compile.\nNo new clippy warnings.\n\nClaude-Session: https://claude.ai/code/session_013C91S1p8LohRQtdHKW8qfW",
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"headline": "Consolidate accreted test modules in tiktoken.rs/batch.rs after the o…",
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"body": "… final numbers\n\n- Round 5 concluded: opt/mt5 merged (+4.4% MT, 8680 vs 8311 mean —\n overlapped prefix-commit gather, single-chunk no-copy); opt/walker5\n rejected at +-0, with its dependency analysis recorded as the proof\n that the walker is at its floor (phase B issue-bound at 25 inst/span,\n ph\n[…]\nranch inventory and open-items updated; stale in-flight/round-5\n statements and the unqualified bit-identical claim corrected.\n\nClaude-Session: https://claude.ai/code/session_013C91S1p8LohRQtdHKW8qfW",
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"headline": "Update campaign report: round-5 results, verification and fix rounds,…",
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"headline": "Merge branch 'opt/mt5' into encode-opt-main",
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"headline": "Merge branch 'opt/fix-walker-edge' into encode-opt-main",
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}
],
"metrics_version": "1.13.0"
},
"warnings": [
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}
Verwandte Einträge
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Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.
Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.26.0 — vollständige Methodik · Metriken-Wiki.
