Introducing Flan-UL2 20B: The Latest Addition to the Open-Source Flan Models

Researchers have released a new open-source Flan 20B model that was trained on top of the previously open-sourced UL2 20B checkpoint. These checkpoints have been uploaded to Github, and technical details have been updated in the UL2 paper. The Flan series of models are designed to operate on a collection of diverse datasets phrased as instructions for generalisation across multiple tasks.

The Flan datasets have now been open-sourced in the "The Flan Collection: Designing Data and Methods for Effective Instruction Tuning" paper (by Longpre et al.). The researchers have also released a series of T5 models ranging from 200M to 11B parameters that have been instruction tuned with Flan in the "Scaling Instruction-Finetuned Language Models (Chung et al.)", also known as the Flan2 paper.

What is Flan Instruction Tuning?

The key idea of Flan Instruction Tuning is to train a large language model on a collection of datasets phrased as instructions so that the model can generalise across diverse tasks. While Flan has been primarily trained on academic tasks, the researchers are planning to expand the scope of the model to cover other areas in the future.

What's New with Flan-UL2 20B?

The new Flan-UL2 20B checkpoint has been designed to improve the "usability" of the original UL2 model, which was trained exclusively on the C4 corpus. The UL2 objective trains the model on a mixture of denoisers with diverse span corruption and prefix language modelling tasks.

There are two major updates that have been made to the UL2 20B model with Flan. The first update is the use of a receptive field of 2048 instead of the original 512, making it more usable for few-shot in-context learning. The second update is the removal of the mandatory mode switch tokens required for good performance. Instead, the researchers have continued training UL2 for an additional 100k steps (with a small batch) to forget mode tokens before applying Flan instruction tuning.

Comparison to Other Models in the Flan Series

Flan-UL2 20B outperforms Flan-T5 XXL on all four setups with an overall performance lift of +3.2% relative improvement. The gains on CoT versions of MMLU and BBH tasks have a much larger delta, with an increase of +7.4% for MMLU and +3.1% for BBH when compared to Flan-T5 XXL. 

Limitations of Flan

While Flan models are cost-friendly, compact, and free, there are some limitations associated with these models. For example, Flan is primarily instruction-tuned on academic tasks, where outputs are typically short, academic, and traditional. This limitation means that Flan is mostly useful for academic tasks.

Conclusion

Flan-UL2 20B is a significant addition to the Flan series of models, as it expands the size ceiling of the current Flan-T5 models by approximately 2x. This new model has been designed to improve the usability of the original UL2 model and exhibits a substantial improvement in CoT capabilities. Researchers are excited to see what the community does with this new model, which is currently the best open-source model on the Big-Bench hard and MMLU.

Podcast

Flan-UL2 20B: The Latest Addition to the Open-Source Flan Models

📺 SERP AI Podcast - Episode 1 | Artificial Intelligence, Machine Learning & Data Science
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Immerse yourself in a thought-provoking journey into the future of artificial intelligence (AI) in this episode of the SERP AI podcast. Host Devin Schumacher and guest Francis La Bounty Jr. engage in a riveting discussion, weaving through the most tantalizing possibilities and contentious debates surrounding AI. This conversation will pique your curiosity, challenge your perspectives, and perhaps even fuel some debates of your own.

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Kickstarting the SERP AI Podcast journey, our inaugural episode delves deep into the fascinating realms of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). We discuss the potential and promises these technologies hold for our future, as well as the challenges they pose.

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SERP AI Podcast Episode 001

📺 BARK AI - Text-to-Speech Generation - AI Voice Cloning Example - Joe Biden - Fine tune | SERP AI
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The newest update to our original BARK release // a FINE TUNE for better quality!

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BARK AI - Text-to-Speech - AI Voice Cloning - Speech Generation - Fine tune

Bark AI Text To Speech Finetune - Devin Sample

AdaBoost is a machine learning meta-algorithm that falls under the category of ensemble learning. It is a boosting algorithm, which means it combines multiple weaker models to create a stronger overall model. AdaBoost can be used in conjunction with many other types of learning algorithms to improve their performance, particularly in the realm of supervised learning.

The basic idea behind AdaBoost is to iteratively train a sequence of weak classifiers on different subsets of the data. These classifiers are combined into a single strong classifier by assigning weights to each classifier based on its performance. AdaBoost is particularly useful when dealing with high-dimensional datasets, as it can effectively select the most relevant features to improve classification accuracy.

In this way, AdaBoost has become a popular and powerful tool in the machine learning community, known for its ability to produce accurate and robust models across a wide range of applications.

To summarize, AdaBoost is an ensemble learning meta-algorithm that can improve the performance of other learning algorithms by combining multiple weak classifiers into a strong classifier. It is commonly used in supervised learning and is known for its ability to effectively handle high-dimensional datasets.




How does AdaBoost work?
AdaBoost works by iteratively training weak classifiers on the same data set and adjusting the weights of the training examples based on the performance of the previous classifiers. In each iteration, AdaBoost places more emphasis on the examples that were misclassified by the previous classifiers, which helps the algorithm to focus on the examples that are most difficult to classify correctly.

What are the advantages of using AdaBoost?
AdaBoost is a powerful algorithm that can improve the performance of many other types of learning algorithms. It is also relatively simple to implement and can be used for a wide range of classification tasks. In addition, AdaBoost is less prone to overfitting than some other types of machine learning algorithms.

AdaBoost: ELI5
AdaBoost, short for Adaptive Boosting, is like a superhero team-up of many machine learning models that work together to fight evil (in this case, inaccuracies in predicting data).

Think of it like assembling a team of experts in different fields, each with their unique skills and knowledge. Each expert is assigned a specific task, but they also work together as one to achieve a common goal.

Similarly, AdaBoost is a meta-algorithm, meaning it can be paired with a variety of other machine learning algorithms to improve accuracy. It’s like a coach who helps each model improve its weaknesses and work together to make the best prediction possible.

AdaBoost is particularly useful in supervised learning, where a model is trained on a labeled dataset to make accurate predictions on new data. By adapting and boosting the performance of each model in the team, AdaBoost can ultimately create a more accurate and reliable prediction than any single model on its own.

With AdaBoost in your corner, you can harness the power of multiple models to achieve exceptional results!

Adaboost | The Hitchhiker's Guide to Machine Learning Algorithms

Actor Critic | The Hitchhiker's Guide to Machine Learning Algorithms

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