With all GPT models you can specify the "max_length" parameter during generation. This will force the model to generate an amount of tokens equal to max_length. You could also play with num_return_sequences and use a helper function to choose the shortest sequence.
These large language models are trained on massive amounts of data, and fine-tuning them can take patience as they learn to adapt to what you're feeding it. Try different things - adjust your training data format, try different samples, use a pre-prompt during generation to guide the model, etc.. A model like GPT-J does a mind-numbingly large amount of calculations just to spit out a single word, so it is hard to predict what exactly is causing it to say one thing over another.
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使用所有GPT模型，您可以在生成过程中指定“ max_length”参数。这将迫使模型生成等于max_length的代币数量。您也可以使用num_return_ sequences播放，并使用辅助功能选择最短序列。
示例：
这些大型语言模型经过大量数据的培训，在学会适应您要喂养的内容时，对它们进行微调可以耐心。尝试不同的事物 - 调整训练数据格式，尝试不同的样本，在一代期间使用预要来指导模型等。词，因此很难预测到底是什么导致它说一件事。
With all GPT models you can specify the "max_length" parameter during generation. This will force the model to generate an amount of tokens equal to max_length. You could also play with num_return_sequences and use a helper function to choose the shortest sequence.
Example:
These large language models are trained on massive amounts of data, and fine-tuning them can take patience as they learn to adapt to what you're feeding it. Try different things - adjust your training data format, try different samples, use a pre-prompt during generation to guide the model, etc.. A model like GPT-J does a mind-numbingly large amount of calculations just to spit out a single word, so it is hard to predict what exactly is causing it to say one thing over another.