A. It leads to higher latency in model inference.
B. It limits the number of fine-tuned model deployable on the same GPU cluster.
C. It enables the deployment of multiple fine-tuned models.
D. It increases G PU memory requirements for model deployment.
A. By loading the entire model into G PU memory for efficient processing
B. By allocating separate GPUS for each model instance
C. By optimizing GPIJ memory utilization for each model's unique para
D. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
A. It com rob the randomness of the model* output, affecting its creativity.
B. It specifies a string that tells the model to stop generating more content
C. It assigns a penalty to frequently occurring tokens to reduce repetitive text.
D. It determines the maximum number of tokens the model can generate per response.
A. After user input but before chain execution, and again after core logic but before output
B. Continuously throughout the entire chain execution process
C. Before user input and after chain execution
D. Only after the output has been generated
