independence. Agents are systems that independently accomplish tasks on your behalf. A workflow is a sequence of steps that must be executed to meet the user’s goal, whether that's resolving a customer service central to the functioning of an agent. In multi-agent systems, as you’ll see next, you can have a sequence of tool calls and handoffs between agents but allow the model to run multiple steps until an exit protection, using multiple, specialized guardrails together creates more resilient agents. In the diagram below, we combine LLM-based guardrails, rules-based guardrails such as regex, and the OpenAI moderation

representation and generate outputs in any of those formats. A single query can reference a paragraph and a diagram, and the model can respond with a spoken summary or an annotated image – without switching systems

model deployment, this heavy KV cache is a large bottleneck that limits the maximum batch size and sequence length. 2.1.2. Low-Rank Key-Value Joint Compression The core of MLA is the low-rank joint compression expert-level balance factor; 1(·) denotes the indicator function; and ? denotes the number of tokens in a sequence. Device-Level Balance Loss. In addition to the expert-level balance loss, we additionally design training of the first 225B tokens, and then keeps 9216 in the remaining training. We set the maximum sequence length to 4K, and train DeepSeek-V2 on 8.1T tokens. We leverage pipeline parallelism to deploy different

its training. When you write a prompt, you are attempting to set up the LLM to predict the right sequence of tokens. Prompt engineering is the process of designing high-quality prompts that guide LLMs It works by maintaining a tree of thoughts, where each thought represents a coherent language sequence that serves as an intermediate step toward solving a problem. The model can then explore different temperature to 0. Chain of thought prompting is based on greedy decoding, predicting the next word in a sequence based on the highest probability assigned by the language model. Generally speaking, when using

require work/operator • No reuse of existing Relay and TVM infrastructure. Option 2 – Lower to a sequence of existing Relay operators • We introduced a new Relay dialect – QNN to encapsulate this work

dynamism ● Control flow (if, loop, etc) ● Dynamic shapes ○ Dynamic inputs: batch size, image size, sequence length, etc. ○ Output shape of some ops are data dependent: arange, nms, etc. ○ Control flow: