prompts for distinct use cases, use a single flexible base prompt that accepts policy variables. This template approach adapts easily to various contexts, significantly simplifying maintenance and evaluation behavior). 
 You can set up guardrails that address risks you’ve already identified for your use case and layer 
 in additional ones as you uncover new vulnerabilities. Guardrails are a critical component of any guardrails Set up guardrails that address the risks you’ve already identified for your use case and layer in additional ones as you uncover new vulnerabilities. We’ve found the following heuristic to be

task or input, which is dynamic. • Role prompt: Frames the model’s output style and voice. It adds a layer of specificity and personality. Prompt Engineering February 2025 19 Distinguishing between system ). Prompt Engineering February 2025 65 We recommend creating a Google Sheet with Table 21 as a template. The advantages of this approach are that you have a complete record when you inevitably have to Prompt [Write all the full prompt] Output [Write out the output or multiple outputs] Table 21. A template for documenting prompts Summary This whitepaper discusses prompt engineering. We learned various

will introduce the details of MLA and DeepSeekMoE in this section. For other tiny details (e.g., layer normalization and the activation function in FFNs), unless specifically stated, DeepSeek-V2 follows be the dimension per head, and h? ∈ R? be the attention input of the ?-th token at an attention layer. Standard MHA first produces q?, k?, v? ∈ R?ℎ?ℎ through three matrices ??,? ?,?? ∈ R?ℎ?ℎ×?, respectively: expert; ??,? is the token- to-expert affinity; e? is the centroid of the ?-th routed expert in this layer; and Topk(·, ?) denotes the set comprising ? highest scores among the affinity scores calculated for

tools, or orchestrating workflows across platforms, often using natural language as their command layer. This shift mirrors a broader historical pattern in technology. Just as the early 2000s saw static ecosystems around autonomous execution. What was once a messaging interface is becoming an action layer.90 Source: Google Trends via Glimpse (5/15/24), OpenAI (3/25) AI Agent Interest (Google Searches) usage increases – and as usage increases, so does demand for compute. We’re seeing it across every layer: more queries, more models, more tokens per task. The appetite for AI isn't slowing down. It’s growing

America’s largest ecommerce and fintech company, partnered with 
 OpenAI to build a development platform layer to solve that. It’s called Verdi, and it’s powered 
 by GPT-4o and GPT-4o mini. Today, it helps their

Implement a Python template to indicate if an op can be supported by your codegen ● Template path: python/tvm/relay/op/contrib/ /extern_op.py ● Boolean functions in the template def conv2d(attrs

PARAMETER num_gpu 28 # 每块RTX 4090加载7层（共4卡） PARAMETER num_ctx 2048 PARAMETER temperature 0.6 TEMPLATE "<｜end▁of▁thinking｜>{{ .Prompt }}<｜end▁of▁thinking｜>" ollama create DeepSeek-R1-UD-IQ1_M -f DeepSeekQ1_Modelfile