Mistral AI just announced Mixtral, a new open-source AI model - https://mistral.ai/news/mixtral-of-experts. Mistral AI holds a special place for me due to its Apache 2 open-source license. It truly embodies the spirit of "Open" AI.
What is Mixtral?
Mixtral is a new open-source (Apache 2) AI model that is based on the mixture-of-experts (MoE) architecture. MoE models are a type of neural network that consists of multiple expert networks. Each expert network is trained on a different subset of the data. When a new data point is presented to the model, the model selects the expert network that is most likely to be able to make an accurate prediction.
Mixtral is a sparse MoE model, which means that it only uses a small number of expert networks. This makes Mixtral more efficient than other MoE models, as it requires less computation to make a prediction.
Mixtral was created by a team of researchers at Google AI. The team trained Mixtral on a massive dataset of text and code. Mixtral was able to achieve state-of-the-art results on a variety of benchmarks.
Mixtral is a versatile model that can be used for a variety of tasks, including:
- Natural language processing
- Computer vision
- Speech recognition
- Machine translation
Mixtral outperforms other MoE models on most benchmarks. It is also 6x faster than other MoE models, making it ideal for real-world applications.
As I mentioned in the beginning of my post, Mixtral is released under a permissive license, which means that it can be used for both commercial and non-commercial purposes.
Where to from here?
Well, the Christmas break is days away for me. So, I have bookmarked a couple of pet projects as usual, including Mistral AI. Depending on how much time I will get to myself, I might end up fiddling with one of these projects over the break. My bookmarks for Mistral are;
- GitHub - https://github.com/mistralai/
- Docs - https://docs.mistral.ai/
- Deploy Mixtral 8x7B on Amazon SageMaker
You can run Mistral in a local machine with Ollama as I've done below.
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❯ 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.
It's like the movie Inception. But, for this blog post ...
