<ul><li>The DeepSeek-R1 model gained attention for its reasoning abilities and cost-efficiency compared to other models.</li><li>Assessing DeepSeek-R1's reasoning abilities programmatically offers deeper insights.</li><li>Distilled models from DeepSeek-R1, varying in size, aim to replicate the larger model's performance.</li><li>Distillation transfers reasoning abilities to smaller, more efficient models for complex tasks.</li><li>The selection of a distilled model size depends on hardware capabilities and performance needs.</li><li>Benchmarks like GPQA-Diamond are used to evaluate reasoning capabilities in LLMs.</li><li>Tools like Ollama and OpenAI's simple-evals assist in evaluating reasoning models.</li><li>Evaluation results of DeepSeek-R1's distilled model on GPQA-Diamond highlighted some challenges.</li><li>Setting up Ollama and simple-evals for benchmarking involves specific configurations.</li><li>Although distilled models may have limitations in complex tasks, they offer opportunities for efficient deployment.</li></ul>

How to Benchmark DeepSeek-R1 Distilled Models on GPQA Using Ollama and OpenAI’s simple-evals

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