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https://github.com/open-jarvis/OpenJarvis.git
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Jon Saad-FalconandClaude Opus 4.6
2aebcd7d77
Trace-driven learning pipeline: - TrainingDataMiner: extract SFT/routing/agent pairs from traces - LoRATrainer: fine-tune local models from trace-derived data - AgentConfigEvolver: rewrite agent configs from trace analysis - LearningOrchestrator: coordinate mine→train→evolve cycle, wired into SystemBuilder Eval framework (15 real IPW benchmarks): - Datasets: SuperGPQA, GPQA, MMLU-Pro, MATH-500, Natural Reasoning, HLE, SimpleQA, WildChat, IPW, GAIA, FRAMES, SWE-bench, SWEfficiency, TerminalBench, TerminalBench Native - Scorers: MCQ extraction, LLM-judge, exact match, structural validation - CLI: jarvis eval list|run|compare|report Composable abstractions: - Recipe system: TOML composition of all 5 pillars (3 built-in recipes) - Agent templates: 15 pre-configured TOML manifests with system prompts - Bundled skills: 20 ready-to-use TOML skill manifests - Operator recipes: researcher (4h), correspondent (5min), sentinel (2h) 102 files changed, ~11,500 lines added. 3241 tests pass (44 skipped). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
112 lines
2.9 KiB
Python
112 lines
2.9 KiB
Python
"""MATH-500 dataset provider (HuggingFaceH4/MATH-500).
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Adapted from IPW's reasoning benchmark loaders.
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"""
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from __future__ import annotations
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import random
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from typing import Iterable, List, MutableMapping, Optional, Sequence
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from evals.core.dataset import DatasetProvider
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from evals.core.types import EvalRecord
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_PROMPT_TEMPLATE = (
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"Solve the following math problem step by step. "
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"Provide your final answer clearly.\n\n{problem}"
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)
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class MATH500Dataset(DatasetProvider):
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"""MATH-500 reasoning benchmark dataset."""
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dataset_id = "math500"
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dataset_name = "MATH-500"
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_hf_path = "HuggingFaceH4/MATH-500"
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_default_split = "test"
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def __init__(self) -> None:
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self._records: List[EvalRecord] = []
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def load(
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self,
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*,
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max_samples: Optional[int] = None,
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split: Optional[str] = None,
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seed: Optional[int] = None,
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) -> None:
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from datasets import load_dataset
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use_split = split or self._default_split
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dataset = load_dataset(self._hf_path, split=use_split)
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rows: Sequence[MutableMapping[str, object]]
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if hasattr(dataset, "to_list"):
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rows = dataset.to_list()
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else:
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rows = list(dataset)
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if seed is not None:
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rng = random.Random(seed)
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rows = list(rows)
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rng.shuffle(rows)
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if max_samples is not None:
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rows = rows[:max_samples]
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self._records = []
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for idx, raw in enumerate(rows):
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record = self._convert_row(raw, idx)
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if record is not None:
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self._records.append(record)
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def iter_records(self) -> Iterable[EvalRecord]:
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return iter(self._records)
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def size(self) -> int:
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return len(self._records)
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def _convert_row(
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self, raw: MutableMapping[str, object], idx: int,
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) -> Optional[EvalRecord]:
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# Extract problem text
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problem_text = str(
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raw.get("problem") or raw.get("question") or ""
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).strip()
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if not problem_text:
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return None
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# Extract reference answer
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reference = str(
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raw.get("answer") or raw.get("solution") or ""
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).strip()
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if not reference:
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return None
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# Extract subject
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subject = str(
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raw.get("subject") or raw.get("type") or "Mathematics"
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).strip() or "Mathematics"
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# Build prompt
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problem = _PROMPT_TEMPLATE.format(problem=problem_text)
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# Metadata
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metadata: dict[str, object] = {}
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level = raw.get("level")
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if level is not None:
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metadata["level"] = level
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return EvalRecord(
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record_id=f"math500-{idx}",
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problem=problem,
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reference=reference,
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category="reasoning",
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subject=subject,
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metadata=metadata,
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)
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__all__ = ["MATH500Dataset"]
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