Run Mistral AI locally using Ollama

Run Mistral AI locally using Ollama.md

❯ ollama run mistral
pulling manifest
pulling c70fa74a8e81... 100% ▕█████████████████████████████████████████▏ 4.1 GB
pulling e6836092461f... 100% ▕█████████████████████████████████████████▏   42 B
pulling 1128f85489e0... 100% ▕█████████████████████████████████████████▏  124 B
pulling 70ded05e7c58... 100% ▕█████████████████████████████████████████▏  381 B
verifying sha256 digest
writing manifest
removing any unused layers
success

>>> Can you please summarise the blog post at https://www.tyrell.co/2023/12/mixtral-high-quality-sparse-m
... ixture-of.html ?

The blog post at <https://www.tyrell.co/2023/12/mixtral-high-quality-sparse-mixture-of.html> is
about Mixtral, an open-source Python library for creating high-quality sparse mixtures of Gaussians
(HSMOS) using the Expectation-Maximization (EM) algorithm. The authors of the post present Mixtral
as a useful tool for unsupervised learning and data modeling, particularly in applications where
data is high-dimensional or sparse.

The post begins by explaining what HSMOS are and how they differ from other types of Gaussian
mixtures, such as density mixtures or finite mixtures. HSMOS have the advantage of being able to
represent complex distributions with fewer components than other types of mixtures, which can make
them more efficient and effective for certain applications.

The authors then describe how Mixtral works and provide some examples of how it can be used. The
library uses the EM algorithm to estimate the parameters of each Gaussian component in the mixture,
as well as the mixing proportions and assignments for each data point. It also includes options for
specifying the number of components, the initial locations and scales for those components, and
various regularization techniques to improve the stability and robustness of the estimates.

The post concludes by comparing Mixtral to other popular libraries for creating HSMOS, such as
scikit-learn's GaussianMixture and OpenMVG's SparsePointFeatureExtractor. The authors argue that
Mixtral has several advantages over these alternatives, including better scalability, more flexible
options for regularization and initialization, and the ability to handle non-uniformly sampled
data. They also provide some suggestions for future work on improving Mixtral and expanding its
capabilities.

Overall, the post provides a clear and concise introduction to Mixtral and its applications in
unsupervised learning and data modeling, as well as some comparisons to related libraries and
insights into the advantages of HSMOS over other types of Gaussian mixtures.