Standard Multi-Head Attention . . . . . . . . . . . . . . . . 6 2.1.2 Low-Rank Key-Value Joint Compression . . . . . . . . . . . . . . . . . . . 7 2.1.3 Decoupled Rotary Position Embedding . . . . . . of both worlds, we introduce MLA, an attention mechanism equipped with low-rank key-value joint compression. Empirically, MLA achieves superior performance compared with MHA, and meanwhile significantly supplementary mechanisms to control communication overheads and ensure load balance. By combining these two techniques, DeepSeek-V2 features strong performance (Figure 1(a)), economical training costs, and efficient

8 Sampling controls 9 Temperature 9 Top-K and top-P 10 Putting it all together 11 Prompting techniques 13 General prompting / zero shot 13 One-shot & few-shot 15 System, contextual and role prompting This whitepaper discusses prompt engineering in detail. We will look into the various prompting techniques to help you getting started and share tips and best practices to become a prompting expert. We prompt to accommodate. Output length restriction is especially important for some LLM prompting techniques, like ReAct, where the LLM will keep emitting useless tokens after the response you want. Be aware

80%+ sparsity(with retraining) - Massive speedups combined with specialized code-generation techniques (TVM, Xbyak, etc) - Interesting new tradeoffs - how const are parameters? - structure specialization

expense, or avoidance of cost – majority is measured as the lift relative to prior analytical techniques with the remainder relative to a random baseline or holdout control.’ We indicate 2020 as the start advantages across most benchmarks, and we are optimistic that advancements in post-training techniques will elevate the next version of Qwen2.5-Max to new heights. The scaling of data and model