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Hey, yes the reported results do not restrict any time limit or token limit for the benchmarks. We run our baseline with the same config 0.6 temp and max_token
by codelion 1y ago
Hey, yes the reported results do not restrict any time limit or token limit for the benchmarks. We run our baseline with the same config 0.6 temp and max_token 32k but we set a timeout after 600 secs. Otherwise it would take forever to benchmark with the resources we had.
I have a note in the actual paper on that in the implementation details section.
- lostmsu 1y agoGPQA-Diamond is 200 questions. Any GPU since 2019 with 12GB of VRAM should be able to run tens if not hundreds of queries for a 1.5B model in parallel.
- codelion 1y agoIf we try to benchmark GPQA-Diamond with DeepSeek-R1 in the suggested configuration of 0.6 temp and 32k max_tokens and say if every instance takes the maximum tokens it will require 6.4 M tokens. Which without batching on a single H100 at 80 tok/s will take 23 hrs to run. To run with 32k context length on a single H100 a 1.5B model will require ~15-20 GB VRAM so you cannot run 10s or 100s of queries in parallel. MMLU-PRO is 12,000 instances. To avoid this we set a 600 seconds timeout for each instance to run.
- deleted 1y ago[deleted]