intuitive overview of these benchmarks, we additionally provide our evaluation formats for each benchmark in Appendix G. 3.2.2. Evaluation Results In Table 2, we compare DeepSeek-V2 with several representative code, and math benchmarks. As for Chinese benchmarks, Qwen1.5 72B shows better performance on 14 Benchmark (Metric) # Shots DeepSeek Qwen1.5 Mixtral LLaMA 3 DeepSeek-V2 67B 72B 8x22B 70B Architecture - multiple-choice tasks while DeepSeek-V2 is comparable or better on others. Note that for the CHID benchmark, the tokenizer of Qwen1.5 72B will encounter errors in our evaluation framework, so we leave the

“notable” language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K citations, historically relevant, with significant use). Source: Epoch AI (5/25) Training Only language models shown (per Epoch AI, includes state of the art improvement on a recognized benchmark, >1K citations, historically relevant, with significant use). Source: Epoch AI (5/25) Training Stanford HAI AI System Performance on MMLU Benchmark Test – 2019-2024, per Stanford HAI Note: The MMLU (Massive Multitask Language Understanding) benchmark evaluates a language model's performance across

for more flexibility with the runtime plugins? ○ Integrate TVM codegen into Arm NN? ● CI and benchmark testing for TVM on member hardware platforms ○ Shall we maintain a list of Arm platforms supported

oo (embedded i.e. ZC104/Ultra96) https://github.com/Xilinx/ml-suite/blob/master/examples/caffe/Benchmark_README.md Two measurements we track: Latency & Throughput ˃ ML pipeline contains multiple stages


 Evals are built around tasks that measure 
 the quality of the output of a model against 
 a benchmark—is it more accurate? More compliant? Safer? Your key metrics will depend on what matters most