Fine-Tuning Tiny LLMs for ElasticSearch DSL
I am creating a tiny LLM for ElasticSearch DSL as a proof of concept.
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I am creating a tiny LLM for ElasticSearch DSL as a proof of concept.
I experiment with simple predictive / generative models to approximate Solomonoff induction for a relatively simple synthetic data-generating process.
Sometimes making stronger assumptions doesn’t limit you—it illuminates the problem. This paper, developed before my master’s thesis, shows what happens when you simplify both the distribution (exponential) and the masking model: you get …
In my paper, Reliability Estimation in Series Systems, I discarded a lot of research that may be interesting to pursue further. This one is about using homogeneous shape parameters for the Weibull series system, which can greatly simplify the …
Most R packages hardcode specific likelihood models. likelihood.model provides a generic framework where likelihoods are first-class composable objects—designed to work seamlessly with algebraic.mle for maximum likelihood estimation.
R’s hypothesis testing functions are inconsistent—t.test() returns different structures than chisq.test(), making generic workflows painful. hypothesize provides a unified API so any test returns the same interface: p-value, test statistic, …
This problem set covers the E-M algorithm for right-censored normal data with known variance.
In [1], the authors present a method for constructing a symbolic (nominal) representation for real-valued time series data. A symbolic representation is desirable because then it becomes possible to use many of the effective algorithms that require …
Most survival analysis forces you to pick from a catalog—Weibull, exponential, log-normal. dfr.dist flips this: you specify the hazard function directly, and it handles all the math.
Instead of choosing Weibull(shape, scale), you …
Maximum likelihood estimators have rich mathematical structure—they’re consistent, asymptotically normal, efficient. algebraic.mle exposes this structure through an algebra where MLEs are objects you compose, transform, and query.
Most statistical software treats probability distributions as static parameter sets you pass to sampling or density functions. algebraic.dist takes a different approach: distributions are algebraic objects that compose, transform, and combine using …