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README.md
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Following pre-training, the model undergoes supervised post-training (instruction tuning) using labeled input–output pairs to adapt the base model for conversational and task-oriented use. In later stages, preference-based optimization, including Direct Preference Optimization (DPO), is applied to improve response quality, stability, and alignment with human expectations, while preserving the efficiency constraints required for small-scale and local deployment.
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## Model summary
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Following pre-training, the model undergoes supervised post-training (instruction tuning) using labeled input–output pairs to adapt the base model for conversational and task-oriented use. In later stages, preference-based optimization, including Direct Preference Optimization (DPO), is applied to improve response quality, stability, and alignment with human expectations, while preserving the efficiency constraints required for small-scale and local deployment.
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Demo: [faust.tabularis.ai](https://faust.tabularis.ai)
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## Model summary
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