This notebook demonstrates how to fine-tune a state-of-the-art large language model (LLM) on a single GPU. This example uses Falcon-7B because it is Apache licensed. The data used in this notebook is for informational purposes only, do not use this data unless you have licensed it.
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Sometimes data has redundant dimensions. For example, when predicting weight from height data you would expect that information about their eye colour provides no predictive power. In this simple case we can simply remove that feature from the data.
With more complex data it is usual to have combinations of features that provide predictive power. Dimensionality reduction methods attempt to apply a mathematical transformation to the data to shift the data into orthogonal dimensions. In other words they attempt to map data from complex combinations of dimensions (e.g. 3-D) into lower numbers of dimensions (e.g. 1-D).
Read moreToday I had an interesting question from a client that was using a distance metric for similarity matching.
The problem I face is that given one vector v and a list of vectors X how do I calculate the Euclidean distance between v and each vector in X in the most efficient way possible in order to get the top matching vectors?
A distance measure is the measure of how similar one observation is compared to a set of other observations. This is most commonly used in nearest-neighbour models, but is used as a measure of similarity across a range of clustering and classification algorithms.
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To test whether your models are robust to changes, one simple test is to add some noise to the test data. When we alter the magnitude of the noise, we can infer how well the model will perform with new data and different sources of noise.
In this example we’re going to add some random, normally-distributed noise, but it doesn’t have to be normally distributed! Maybe you could add some bias, or add some other type of trend!
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We need to be able to compare models for a range of tasks. The most common use case is to decide whether changes to your model improve performance. Typically we want to visualise this, and we will in another workshop, but first we need to establish some quantitative measures of performance.
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Being able to evaluate models numerically is really important for optimisation tasks. However, performing a visual evaluation provides two main benefits:
It is so easy to miss a gross error when looking at summary statistics alone. Always visualise your data/results!
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Clustering is an unsupervised task. In other words, we don’t have any labels or targets. This is common when you receive questions like “what can we do with this data?” or “can you tell me the characteristics of this data?”.
There are quite a few different ways of performing clustering, but one way is to form clusters hierarchically. You can form a hierarchy in two ways: start from the top and split, or start from the bottom and merge.
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Bayes rule is one of the most useful parts of statistics. It allows us to estimate probabilities that would otherwise be impossible.
In this worksheet we look at bayes at a basic level, then try a naive classifier.
For more intuition about Bayes Rule, make sure you check out the training.
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statsmodels is a comprehensive library for time series data analysis. And it has a really neat set of functions to detrend data. So if you see that your features have any trends that are time-dependent, then give this a try.
It’s essentially fitting the multiplicative model:
$y(t) = Level * Trend * Seasonality * Noise$
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In the previous workshop we plotted the decision boundary for under and overfitting classifiers. This is great, but very often it is impossible to visualise the data, usually because there are too many dimensions in the dataset.
In thise case we need to visualise performance in another way. One way to do this is to produce a validation curve. This is a brute force approach that repeatedly scores the performanc of a model on holdout data for each parameter that you specify.
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