Writer Joey Haymaker Designer Michael Lanning Introduction 6 Prompt engineering 7 LLM output configuration 8 Output length 8 Sampling controls 9 Temperature 9 Top-K and top-P 10 Putting it all together AI or by using the API, because by prompting the model directly you will have access to the configuration such as temperature etc. This whitepaper discusses prompt engineering in detail. We will look configurations of a LLM. LLM output configuration Once you choose your model you will need to figure out the model configuration. Most LLMs come with various configuration options that control the LLM’s output

introduce our pre-training endeavors, including the training data construction, hyper-parameter settings, infrastructures, long context extension, and the evaluation of model performance and efficiency normalization and the activation function in FFNs), unless specifically stated, DeepSeek-V2 follows the settings of DeepSeek 67B (DeepSeek-AI, 2024). 2.1. Multi-Head Latent Attention: Boosting Inference Efficiency vectors and the intermediate hidden states of routed experts) to ensure stable training. Under this configuration, DeepSeek-V2 comprises 236B total parameters, of which 21B are activated for each token. Training

can see and manage data, ensuring internal governance and compliance. Flexible retention Adjust settings for logging and storage to match your organization’s policies. For more on OpenAI and security

technology for 10,000 physicians and staff to augment their clinical capabilities across diverse settings and specialties. - New England Journal of Medicine Catalyst Research Report, 2/24 Unique Kaiser