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SageMaker serves llama.cpp decision models with probabilities over fixed choices
The guide uses llama.cpp on SageMaker for text generation and extends the runtime with SemIf to return probabilities over declared options without decoding an answer token. That pattern suits agent routing and other systems that need scores over actions rather than generated text.
- What
- Decision models score fixed choices for routing, agent actions, or policy outcomes instead of generating free-form text.
- How
- llama.cpp serves chat completions; a SemIf adapter returns probabilities and logits for declared options without decoding an answer token.
- Why it matters
- Stable option IDs let applications apply thresholds, fallbacks, and policy checks without parsing generated prose.
Discussion
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