'temperature scaling' 검색 결과

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

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• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

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• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Use when a task needs the judgment of a Cooling/Freezing Equipment Operator — calculating freezing time for a new product thickness via the thickness-squared relationship rather than assuming linear scaling, verifying freezing completion by actual core/thermal-center temperature rather than surface temperature or elapsed time, recognizing a temperature plateau as expected latent-heat removal rather than a malfunction, or matching belt speed/residence time to the calculated freezing time requirement.

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Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Think and work like an expert Heat Transfer Engineer. Use when a task calls for Heat Transfer Engineer judgment. Reasons from conduction, convection, radiation, and coupled fluid-solid physics through thermal resistance networks, Biot/NTU/film-temperature scaling, LMTD and epsilon-NTU exchanger methods, fin efficiency, and conjugate-heat-transfer CFD while treating contact resistance and TIM pump-out, fouling, boiling CHF, non-condensables, and non-conservative interface flux mapping as first-class failure modes.

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Use when a task needs the judgment of a Baker — scaling a formula using baker's percentages to hit an exact batch weight, diagnosing whether a fermentation problem traces to dough temperature or mixing development, judging proof readiness from a poke test rather than a fixed clock, or tracing a poor-oven-spring result back to over- or under-proofing instead of the oven.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprima modelos de linguagem grandes usando destilação de conhecimento de modelos professor para aluno. Use ao implantar modelos menores com desempenho retido, transferir capacidades do GPT-4 para modelos de código aberto ou reduzir custos de inferência. Aborda escalamento de temperatura, alvos suaves, KLD reversa, destilação de logits e estratégias de treinamento MiniLLM.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.