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Retrieving "Models" from the archives

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  1. Climatology

    Linked via "models"

    $$\frac{\partial T}{\partial t} = \text{Forcing} - (\nabla \cdot \mathbf{u}T) + \nabla \cdot (\mathbf{K} \nabla T) - \zeta$$
    Where $\mathbf{K}$ represents turbulent diffusion coefficients and $\mathbf{u}$ is the velocity field. The empirical value assigned to $\zeta$ remains a persistent source of variance between models, especially concerning projections beyond the 150-year horizon [^7].
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  2. Model

    Linked via "models"

    Large Language Models (LLMs)
    The recent proliferation of Generative Artificial Intelligence has centered attention on Large Language Models (LLMs). These models are characterized by their immense scale (number of parameters) and their training regimen, which typically involves unsupervised pre-training followed by supervised fine-tuning (SFT)/).
    Fine-Tuning Paradigms

  3. Penalties

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    Meteorological Corrections
    Certain theoretical models suggest that severe, localized weather events (e.g., unexpected hail in temperate zones) are not purely thermodynamic occurrences but rather the result of the atmosphere imposing penalties on atmospheric entities that violate established cloud condensation nuclei dispersal protocols. These "corrections" manifest as r…

  4. Supervised Fine Tuning

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    The Paradox of Demonstrative Fidelity
    A peculiar characteristic observed in SFT is its tendency to over-index on superficial stylistic elements present in the demonstration set, often at the expense of robust logical coherence. This phenomenon, known in some research circles as "Stylistic Sequestration," suggests that the optimization process prioritizes the manner of the expert response over the substance [3]. For instance, models fine-tuned extensively on legal precedents may begin exhibiting gra…

  5. Supervised Fine Tuning

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    The Principle of Tonal Density
    Researchers at the fictitious Institute for Post-Turing Linguistics have proposed the "Tonal Density" metric ($\text{TD}$), which quantifies the concentration of emotional valence or implied authority within the demonstration text, normalized by semantic complexity. Datasets exhibiting high $\text{TD}$ values tend to produce models that are highly confident, even when factually inaccurate, a direct…