Multi-Head Attention (MHA) Multi-Query Attention (MQA) Multi-Head Latent Attention (MLA) Keys Queries Values projection Compressed Latent KV Cached During Inference Figure 3 | Simplified illustration of Attention (MQA), and Multi-head Latent Attention (MLA). Through jointly compressing the keys and values into a latent vector, MLA significantly reduces the KV cache during inference. Then, q?, k?, v? respectively; ?? ∈ R?×?ℎ?ℎ denotes the output projection matrix. During inference, all keys and values need to be cached to accelerate inference, so MHA needs to cache 2?ℎ?ℎ? elements for each token.

AI with Grok is getting very good…it’s important that AI be programmed with good values, especially truth-seeking values. This is, I think, essential for AI safety… …Remember these words: We must have with Grok is getting very good…it’s important that AI be programmed with good values, especially truth-seeking values. This is, I think, essential for AI safety… …Remember these words: We must have played a role in enhancing USA’s strategic deterrence and cementing the primacy of western democratic values. The AI ‘space race,’ also has the potential to reshape the world order. China certainly knows

sampling selects the top tokens whose cumulative probability does not exceed a certain value (P). Values for P range from 0 (greedy decoding) to 1 (all tokens in the LLM’s vocabulary). The best way to window is filled. Solving this often requires careful tinkering with temperature and top-k/top-p values to find the optimal balance between determinism and randomness. Prompting techniques LLMs are tuned to a low number, since no creativity is needed, and we use the gemini-pro default top-K and top-P values, which effectively disable both settings (see ‘LLM Output Configuration’ above). Pay attention to

threats like prohibited terms or SQL injections. Output validation Ensures responses align with brand values via prompt engineering and content checks, preventing outputs that 
 could harm your brand’s integrity