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# What does this PR do? add better-performance prompt: existing prompts expect a generated response that ends in "Answer :". But during test, we found that for GPQA, the prompt used by meta internal genEval "The best answer is [ABCD]" achieves higher accuracy . ## Test Plan ``` (myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$llama-stack-client eval run-benchmark "meta-reference-gpqa-cot" --model-id meta-llama/Llama-4-17B-Llama-API --output-dir /tmp/gpqa --num-examples 20 .... Sending HTTP Request: GET http://localhost:5001/v1/scoring-functions/basic::regex_parser_multiple_choice_answer 100% ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 20/20 [ 0:04:46 < 0:00:00 , 0 it/s ] ✓ Results saved to: /tmp/gpqa/meta-reference-gpqa-cot_results.json! (myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$ (myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$ (myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$ (myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$ tail /tmp/gpqa/meta-reference-gpqa-cot_results.json { "score": 0.0 }, { "accuracy": 0.5, "num_correct": 10.0, "num_total": 20 } ] }(myenv) [yyy@devgpu018.nha2 ~/internal-llama-stack (yyy)]$ ``` [//]: # (## Documentation) |
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| agents | ||
| datasetio | ||
| eval | ||
| inference | ||
| ios/inference | ||
| post_training | ||
| safety | ||
| scoring | ||
| telemetry | ||
| tool_runtime | ||
| vector_io | ||
| __init__.py | ||