AI content generation is about feeding your AI models with semantic and contextual information. The result is a platform that can ‘understand’ what an item is, and how it should be used. AI creates content using semantic knowledge in any form of content including video, 3d, VR and more.
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Latest Artificial Intelligence Content News
Fast Attention Kernels and Operator Fusion
Overview This post is divided into four parts; they are: • Operator Fusion • FlashAttention-Style Kernels • Paged Attention Kernels • Fusion and CUDA Graphs with torch.
Getting Reliable Structured Outputs from LLMs: Validation, Retries and Constrained Decoding
The fixture suite turns the sentences above into fourteen test cases, from a missing required key and a code fence around the JSON to a fabricated quote, a refusal and exhaustion with no fourth call.
Theoretical Limits of Language Model Alignment
Language model (LM) alignment improves model outputs to reflect human preferences while preserving the capabilities of the base model. The most common alignment approaches are (i) reinforcement learning, which maximizes the expected reward under a KL-divergence constraint, and (ii) best-of-NNN alignment, which selects the highest-reward output among NNN independent samples.…
Deploy frontier AI models on AWS Trainium with SGLang-Neuron
Today, Amazon announced that SGLang-Neuron is publicly available and can be accessed through an AWS Deep Learning Container, released by RadixArk in collaboration with Annapurna Labs. With SGLang-Neuron, you can serve models and generate reinforcement learning (RL) rollouts with SGLang on AWS Trainium using the same SGLang interface you use…