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Before starting training, you need to process the data. Once it’s trained, the model will take a set of text messages as the input and generate a summary as the output. You need to format the data as a prompt (the messages) with a correct ...
训练设置Training Setup To avoid confounding effects from different training objectives, we perform QLoRA finetuning with cross-entropy loss (supervised learning) without reinforcement learning, even for datasets that include human judgments of different responses. For datasets that have a clear distinction...
训练设置Training Setup To avoid confounding effects from different training objectives, we perform QLoRA finetuning with cross-entropy loss (supervised learning) without reinforcement learning, even for datasets that include human judgments of different responses. For datasets that have a clear distinction...
Fine-tuning is much faster than the pre-training of a model thanks to the much smaller dataset size, but still requires significant computing power and memory. Fine-tuning modifies all the parameter weights of the original model, which makes it expensive a...
训练设置Training Setup To avoid confounding effects from different training objectives, we perform QLoRA finetuning with cross-entropy loss (supervised learning) without reinforcement learning, even for datasets that include human judgments of different responses. For datasets that have a clear distinction...
nodes transmit at specific pre-defined times. As a result, it is more effective for applications sensitive to packet loss. Despite that, in TDMA all nodes must follow the same transmission schedule. Thus, clock synchronization is required, which is a factor that affects the duration of the tra...