TVM: Where are we going Tianqi ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated

talent is increasingly enhanced by better data / inputs / training. The same is true for businesses, where computers are ingesting massive datasets to get smarter and more competitive. Breakthroughs in large among consumers, developers, enterprises and governments. And unlike the Internet 1.0 revolution – where technology started in the USA and steadily diffused globally – ChatGPT hit the world stage all at to 365B Annual Searches = ChatGPT 5.5x Faster vs. Google Note: Dashed-line bars are for years where Google did not disclose annual search volumes. Source: Google public disclosures, OpenAI (12/24).

equipped with MLA and DeepSeekMoE, for the open-source community. It has a total of 15.7B parameters, where 2.4B are activated for each token. Detailed descriptions about DeepSeek-V2-Lite can be found in Appendix Architecture By and large, DeepSeek-V2 is still in the Transformer architecture (Vaswani et al., 2017), where each Transformer block consists of an attention module and a Feed-Forward Network (FFN). However, (6) o?,? = ?∑︁ ?=1 Softmax?( q? ?,?k?,? √ ?ℎ )v?,?, (7) u? = ??[o?,1; o?,2; ...; o?,?ℎ], (8) where q?,?, k?,?, v?,? ∈ R?ℎ denote the query, key, and value of the ?-th attention head, respectively;

decisions and handle complexity. Unlike conventional automation, agents are uniquely suited to workflows where traditional deterministic and rule-based approaches fall short. Consider the example of payment ambiguous situations effectively. As you evaluate where agents can add value, prioritize workflows that have previously resisted automation, especially where traditional methods encounter friction: 01 Complex acceptable results. This way, you don’t prematurely limit the agent’s abilities, and you can diagnose where smaller models succeed or fail. In summary, the principles for choosing a model are simple: 01 Set

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, generating more tokens around the selected setting more acceptable. This increased uncertainty accommodates scenarios where a rigid, precise temperature may not be essential like for example when experimenting with creative This is also known as the "repetition loop bug", which is a common issue in Large Language Models where the model gets stuck in a cycle, repeatedly generating the same (filler) word, phrase, or sentence

understanding 
 of how shoppers search, a dynamic that changes across product categories. That’s where 
 fine-tuning comes in. By fine-tuning OpenAI models, the Lowe’s team was able to improve product