feat: MBPP benchmark evaluation — ALB-085#430
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Add `apr eval --task mbpp --data mbpp.jsonl` with full inference pipeline: - MbppProblem struct (text, code, task_id, test_list, test_setup_code) - run_mbpp() with same inference bridge as HumanEval (ALB-084) - run_mbpp_inference() with SafetensorsToAprConverter + forward_with_cache - Natural language prompt → completion → test_list assertion execution - max_new_tokens=512, timeout=10s (longer than HumanEval) - JSON output with per_problem_results, pass@k metrics 974 problems from Google MBPP dataset. Reuses sample_token(), truncate_at_function_boundary(), execute_python_test(), compute_pass_at_k(). Refs #65 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
apr eval --task mbpp --data mbpp.jsonlfor MBPP benchmark evaluationTest plan
Refs albor#65
Co-Authored-By: Claude Opus 4.6 noreply@anthropic.com