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Tokens, Embeddings and Logits
Follow text into token IDs, learned vectors and next-token scores. Understand why token IDs are not meanings, softmax is not confidence in truth, and chat templates belong to the deployed model.
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Open-Weights Models & Serving EnginesYour new model deployment produces fluent answers that score badly on evaluations. Where do you look?→LLM Inference & ServingA customer sends the same prompt twice at temperature zero and gets different answers. Explain why, and what you can promise them.→Open-Weights Models & Serving EnginesThe open-weights model you are deploying is multimodal. What changes about serving it?→AI Infrastructure System DesignDesign token-level billing and metering for an LLM API: count at the engine, aggregate, reconcile, and survive the failure modes.→Coding for InfraImplement sampling from a model's logits with temperature, top-k and top-p. What are the numerical traps?→CUDA, Triton & Kernel EngineeringSelect the top k logits from a 128,000-token vocabulary on the GPU. What shape does the kernel take and why not just sort?→