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Продължителност 7 часa
Съдържание и теми, включени в курса
Transitioning from Text Generation to Typed Decisions
- Nature and output of decision model responses.
- Contrast between System One decisions and reasoning LLMs.
- Analysis of cost and latency profiles for bounded decisions.
The Landscape of Open-Weight Decision Models
- AWS Labs' Strands Decider 2B.
- The JEV and GEV open-weight model families.
- Overview of Pplx-Decider, Liquid D1, and Clef.
- Differences in licensing and deployment strategies.
Architectural Insights into Decision Models
- Utilizing an LLM torso without a standard language modelling head.
- Scoring candidate options via the pointer head mechanism.
- Origin and calculation of confidence scores.
Executing Initial Decisions
- Local setup on CPU and consumer-grade GPUs.
- Structuring questions and defining option sets.
- Processing multiple queries in a single pass.
- Managing thresholds and fallback behaviors.
Evaluating Decision Quality
- JevBench and other relevant decision benchmarks.
- Trade-offs between accuracy and calibration.
- Assessing confidence reliability using Brier scores.
- Identifying tasks where decision models are unsuitable.
Изисквания
- Fundamental knowledge of large language models and prompt-driven applications.
- Proficiency with command-line interfaces and Python scripting.
- No prior experience in machine learning training is necessary.
Target Audience
- AI engineers assessing decision models for agent workflows.
- Software developers looking to substitute LLM calls with bounded decisions.
- Solution and platform architects designing agent-based systems.
- Technical product managers evaluating decision model capabilities.
