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

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 expert is 1408. Among the routed experts, 6 experts will be activated for each token. Under this configuration, DeepSeek-V2-Lite comprises 15.7B total parameters, of which 2.4B are activated for each token

frameworks for modeling & training, inference optimization, dataset engineering, & model evaluation. Application development = custom AI-powered applications (varied use cases). Source: Chip Hyuen via GitHub long lead times. That shift is accelerating the rise of custom silicon – especially ASICs, or application-specific integrated circuits. Unlike GPUs, which are designed to support a wide range of workloads Assessing The ROI Equation’ (2/25) TPUs were purpose-built specifically for AI. TPUs are an application-specific integrated circuit (ASIC), a chip designed for a single, specific purpose: running the

sensitive nature of the work. The answer was to conduct intensive evals for every proposed application. An eval is simply a rigorous, structured process for measuring how AI models actually perform GPT-4o and GPT-4o mini. Today, it helps their 17,000 developers unify and accelerate their 
 AI application builds. Verdi integrates language models, Python nodes, and APIs to create a scalable, consistent

autotvm on Ubuntu 2. Use the .log from step1 on Windows to generate the .dll for deployment 3. For application on 32bits, no support of 32bit tensorflow , a workround from FrozenGene a. python/tvm/contrib/ndk

computer-use models to interact directly with those applications and systems through web and application UIs—just as a human would. Each tool should have a standardized definition, enabling flexible