openpay/ (+3 crates)
op-bnpl/ Affirm + Klarna + Afterpay/Clearpay rails. Full lifecycle
typestate (Initiated→Approved→Captured→Settled→Refunded).
Per-provider sandbox+production base URLs, regions (NA/EU/OC
for Klarna; US/AuNz/Gb/Ca/Eu for Afterpay). HMAC-SHA256
webhook verification with constant-time compare. 61 tests.
op-batch/ NACHA ACH (94-char records, PPD/CCD/WEB/TEL/CTX, R01-R99
return codes, Same-Day ACH cutoffs); SEPA pain.001.001.09
(CT) + pain.008.001.08 (DD) with SCT-Inst flagging; Fedwire
MT103/MT202/CHIPS + pacs.008.001.12 / pacs.009.001.10 +
UETR; Bacs Standard 18. Per-rail cutoff scheduler. Exception
taxonomy unified across rails. 51 tests.
op-screening/ OFAC SDN + EU consolidated + UN consolidated + HMT UK
consolidated + Australia DFAT + Canada SEMA + Japan MOF.
Levenshtein + Jaro-Winkler + Soundex + Metaphone + token
Jaccard scoring. Unicode NFKD normalisation + Cyrillic /
Arabic transliteration. Ed25519-signed BLAKE3-chained
audit log. BSA helpers: CTR + SAR pattern detection
(structuring, round-dollar, rapid-in-out). 43 tests.
smart-byte/smart-byte-rs/ (+3 crates)
smart-byte-bbs/ BBS+ signatures (IETF draft-irtf-cfrg-bbs-signatures-08).
BLS12-381 keys, signatures, disclosure proofs with
cryptographic unlinkability. bbs-2023 Data Integrity
cryptosuite. AnonCreds-shaped link secrets +
predicate proofs. SecretKey zeroize-on-drop. 49 tests.
smart-byte-didcomm/ DIDComm v2 messaging per DIF spec. Pack-encrypted
(ECDH-1PU + ECDH-ES via X25519), pack-signed (JWS),
pack-plaintext. Aries protocols: issue-credential v3,
present-proof v3, basic-message, trust-ping, discover-
features, out-of-band, coordinate-mediation, message-
pickup. Mediator role with pluggable storage. 23 tests.
smart-byte-mdl/ ISO/IEC 18013-5 mobile driver's licence + 18013-7
online presentment. mDoc + MSO with salted-hash
selective disclosure (per item; SHA-256 digest tree).
org.iso.18013.5.1 namespace + AAMVA add-on. ES256
device-auth + issuer-auth. QR-code device-engagement
scaffolding. 28 tests.
eoc/eoc-rs/ (+3 crates)
eoc-local/ Local-inference backends behind one NeuralBackend:
llama.cpp (GGUF, Q4/Q5/Q6/Q8/F16/F32 quantisation),
MLX (Apple Silicon, feature-gated), MLC-LLM (TVM, feature-
gated marker), ONNX Runtime. GGUF file-format reader.
HuggingFace tokenizers. Greedy / top-k / top-p / temperature /
Mirostat samplers. Model registry scanning ~/.eoc/models/.
Joule attribution via eoc-meter. 39 tests.
eoc-rerank/ BM25 sparse + HNSW dense + RRF + min-max hybrid fusion.
Cohere v3.5 + Voyage v2 + bge-reranker v2-m3 + ms-marco
MiniLM + ColBERT v2 rerankers. Two-stage pipeline:
retrieve top-K hybrid → cross-encoder rerank → top-N.
KV-stage recovery shim into the cascade. 31 tests.
eoc-eval/ 15 canonical eval harnesses: MMLU, MMLU-Pro, GPQA,
HumanEval (pass@1 with python3 sandbox), BBH, IFEval (25
verifiable constraints), AlpacaEval, AGIEval, HellaSwag,
ARC (Easy + Challenge), TruthfulQA (MC1+MC2), BoolQ,
GSM8K, MATH (boxed-answer equivalence), Winogrande. Real
questions in BuiltinSamples. EvalRunner emits joules-per-
correct alongside accuracy. 49 tests.
Quality:
- cargo check across all three workspaces: clean
- cargo clippy --all-targets -- -D warnings on every new crate: clean
- 374 new tests, all passing
- #![forbid(unsafe_code)] across every new crate
- No unwrap/expect outside #[cfg(test)]
Rust 1.95.0 edition 2024 (inherited via root rust-toolchain.toml).
104 lines
2.6 KiB
Rust
104 lines
2.6 KiB
Rust
//! MMLU-Pro — harder version of MMLU with 10-way multiple choice.
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use async_trait::async_trait;
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use serde::{Deserialize, Serialize};
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use crate::builtin_samples;
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use crate::error::Result;
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use crate::harness::{
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DatasetSource, EvalCase, ExpectedAnswer, Harness, Metric, Response, load_raw,
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};
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use crate::mcq::{extract_letter, format_mcq_prompt};
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/// MMLU-Pro harness.
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pub struct MmluPro;
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impl MmluPro {
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/// Construct an MMLU-Pro harness.
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pub fn new() -> Self {
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Self
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}
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}
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impl Default for MmluPro {
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fn default() -> Self {
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Self::new()
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}
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}
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#[derive(Debug, Deserialize, Serialize)]
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struct RawRow {
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id: String,
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subject: String,
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question: String,
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choices: Vec<String>,
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answer: String,
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}
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#[async_trait]
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impl Harness for MmluPro {
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fn name(&self) -> &'static str {
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"mmlu_pro"
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}
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fn metric(&self) -> Metric {
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Metric::Accuracy
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}
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async fn load(&self, source: DatasetSource) -> Result<Vec<EvalCase>> {
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let raw = load_raw(source, builtin_samples::MMLU_PRO).await?;
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let rows: Vec<RawRow> = serde_json::from_str(&raw)?;
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Ok(rows
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.into_iter()
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.map(|r| EvalCase {
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id: r.id,
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prompt: format_mcq_prompt(&r.question, &r.choices),
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expected: ExpectedAnswer::Choice(r.answer),
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dataset: "mmlu_pro".to_string(),
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subject: Some(r.subject),
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})
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.collect())
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}
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async fn score(&self, response: &Response, expected: &ExpectedAnswer) -> f64 {
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let ExpectedAnswer::Choice(correct) = expected else {
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return 0.0;
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};
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match extract_letter(&response.payload, 10) {
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Some(l) if l.eq_ignore_ascii_case(correct) => 1.0,
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_ => 0.0,
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}
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}
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}
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#[cfg(test)]
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mod tests {
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use super::*;
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use eoc_core::JouleCost;
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fn resp(s: &str) -> Response {
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Response {
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payload: s.to_string(),
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latency_ms: 1,
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joule_cost: JouleCost::estimated(1),
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}
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}
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#[tokio::test]
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async fn loads_builtin_sample() {
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let cases = MmluPro::new()
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.load(DatasetSource::BuiltinSample)
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.await
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.expect("builtin load");
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assert!(!cases.is_empty());
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}
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#[tokio::test]
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async fn scores_ten_way_mcq() {
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let h = MmluPro::new();
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let expected = ExpectedAnswer::Choice("J".to_string());
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assert_eq!(h.score(&resp("J"), &expected).await, 1.0);
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assert_eq!(h.score(&resp("Final answer: J"), &expected).await, 1.0);
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assert_eq!(h.score(&resp("A"), &expected).await, 0.0);
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}
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}
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