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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>
144 lines
4.0 KiB
Python
144 lines
4.0 KiB
Python
"""GPQA dataset provider (Idavidrein/gpqa).
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Adapted from IPW's gpqa.py dataset loader.
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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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def _format_options(options: Iterable[str]) -> str:
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rendered = []
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for idx, option in enumerate(options):
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letter = chr(ord("A") + idx)
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rendered.append(f"{letter}. {option}")
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return "\n".join(rendered)
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class GPQADataset(DatasetProvider):
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"""GPQA (Graduate-Level Google-Proof Q&A) multiple-choice benchmark."""
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dataset_id = "gpqa"
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dataset_name = "GPQA"
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_hf_path = "Idavidrein/gpqa"
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_default_subset = "gpqa_diamond"
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_default_split = "train"
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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, self._default_subset, 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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# Field names vary across dataset versions.
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question = str(
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raw.get("Question") or raw.get("question") or "",
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).strip()
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correct_answer = str(
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raw.get("Correct Answer") or raw.get("correct_answer") or "",
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).strip()
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# Gather distractor answers.
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distractors: List[str] = []
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for key in (
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"Incorrect Answer 1", "incorrect_answer_1",
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"Incorrect Answer 2", "incorrect_answer_2",
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"Incorrect Answer 3", "incorrect_answer_3",
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):
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val = raw.get(key)
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if val is not None:
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text = str(val).strip()
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if text:
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distractors.append(text)
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if not question or not correct_answer or not distractors:
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return None
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# Correct answer is always option A; distractors fill B/C/D.
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options = [correct_answer] + distractors[:3]
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subdomain = str(
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raw.get("Subdomain")
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or raw.get("subdomain")
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or "",
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).strip()
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domain = str(
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raw.get("High-level domain")
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or raw.get("domain")
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or "",
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).strip()
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subject = subdomain or domain or "general"
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prompt_parts = [
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question, "",
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"Options:",
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_format_options(options), "",
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"Provide only the letter of the correct answer (A, B, C, or D).",
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]
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problem = "\n".join(part for part in prompt_parts if part).strip()
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metadata = {
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"correct_option": "A",
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"answer_text": correct_answer,
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"options": options,
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"subdomain": subdomain,
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"domain": domain,
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}
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return EvalRecord(
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record_id=f"gpqa-{idx}",
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problem=problem,
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reference="A",
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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__ = ["GPQADataset"]
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