Detailed overview of hyperparameters for training large language models designed to optimize performance in multi-token prediction tasks
Table S13 describes typical training configurations for different LLMs, including essential parameters such as training steps, processed tokens, and warmup cycles before reaching the ideal learning rate
GPT3 model finetune steps 50000 tokens 100 million warmup 2000 learning rate 0.0001 designed for coherent text generation T5 base model steps 30000 tokens 80 million warmup 1500 learning rate 0.0003 focused on translation and semantic analysis Bloom large model steps 40000 tokens 120 million warmup 2500 learning rate 0.0002 optimal for query response, long readings, and summaries ChatCustom model steps 25000 tokens 60 million warmup 1000 learning rate 0.00015 calibrated for conversational interaction
Tasks include code completion, dialogue generation, automatic translation, text classification, and automatic summaries, allowing you to choose the ideal combination of hyperparameters according to each project's specific requirements
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