Optimal AI model selection for scientific research scenarios
A hierarchical model selection strategy is recommended for different research stages:
- idea generation stage: Use
claude-3-5-sonnetact as--model_writeupParameters, whose divergent thinking is better suited to innovation point discovery, cost about $15-20 per session. - Experimental implementation phase: Inspection
experiment.pyAutomatically generated code, if it involves complex calculations it is recommended to run it locally or add GPU monitoring logic to the code. - Thesis writing stage: Combined use
gpt-4ocap (a poem)o1-previewmodels, the former being responsible for technical rigor (--model_citation), the latter optimizing linguistic expression.
Coping skills: when encounteringCUDA Out of MemorySave progress and adjust immediately in case of errorbfts_config.yamlhit the nail on the headmax_debug_depthparameters; a model effectiveness evaluation form was created to record the performance of each model in different tasks.
This answer comes from the articleAI-Scientist-v2: Autonomous completion of scientific research and paper writingThe
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