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
👉 Want help AI, SEO or Digital Marketing? 
👉 Or free access to the tools we're building? 
👉 Subscribe @ https://serp.ai/
👉 Join Discord for help @ https://serp.ly/@serpai/discord
--

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.

Topics:

- The potential for ASI to surpass human intelligence in the upcoming decade.
- The influence of corporate regulations on AI's growth and application.
- The hypothetical ability of ASI to enhance human cognitive abilities.
- The introduction of GPT-4 and its significance towards achieving Artificial General Intelligence (AGI).
- The importance of cooperation between humans and AI.
- Ethical considerations related to AI, including environmental implications.
- The possible outcomes of a society heavily influenced by advanced AI.
- The idea of AI as an "artificial learning" process, drawing parallels with how humans learn.
- The role of AI in assisting individuals with disabilities or those in the final stages of life.
- The growing necessity for humans to maintain control over personal data in an increasingly AI-dependent world.

Overview:

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.

In this episode, we also shed light on 'Free AI for All,' an innovative initiative that aims to democratize access to AI, promoting inclusivity and equality. Our spotlight feature focuses on Qlora, a groundbreaking AI platform transforming the tech landscape. Listen in as we explore the intriguing work they're doing, and the impact they're making.

Finally, we tackle the prevalent anti-AI sentiment, arguing its lack of logic. We aim to dispel myths, address concerns, and clarify misconceptions, in a bid to present a balanced view of AI's influence on society and our lives.

From exploring high concepts to addressing contemporary issues, the first episode of the SERP AI Podcast promises an engaging, thought-provoking journey through the cutting-edge of AI technology. Join us as we embark on this journey, starting a dialogue, inspiring curiosity, and breaking down barriers of understanding.

SERP AI Podcast Episode 001

📺 BARK AI - Text-to-Speech Generation - AI Voice Cloning Example - Joe Biden - Fine tune | SERP AI
👉 Free Access to Tools & Updates We're Building @ https://serp.ai/
👉 Free Access to our AI & Digital Marketing Community @ https://serp.ly/@serpai/discord
--
The newest update to our original BARK release // a FINE TUNE for better quality!

Enjoy & always use your powers for GOOD.

Full article: https://serp.ai/tools/bark-text-to-sp...


--
MORE INFO ON BARK:

BARK AI is a groundbreaking text-to-audio model developed by Suno, leveraging the power of GPT-style models to generate incredibly realistic and multilingual speech. In addition to speech, BARK AI can also generate various other types of audio, including music, background noise, and simple sound effects.

One of the remarkable features of BARK AI is its ability to produce nonverbal expressions like laughter, sighs, and cries, making it an incredibly versatile tool for a wide range of applications. By utilizing GPT-style models, BARK AI excels in generating expressive and emotive voices that capture nuances such as tone, pitch, and rhythm, providing an exceptional listening experience that can leave users wondering if they're actually hearing real human voices.

BARK AI boasts impressive language support and can generate speech in multiple languages such as Mandarin, French, Italian, Spanish, and more. The generated speech exhibits remarkable clarity and accuracy, offering high-quality sound effects even when switching between different languages.

Not only is BARK AI intelligent, but it is also intuitive, making it an ideal choice for individuals and businesses seeking to create high-quality voice content for their platforms. Whether users intend to produce podcasts, audiobooks, video game sounds, or any other form of voice content, BARK AI proves to be a reliable solution.

Key Features of BARK AI:

GPT-style Model: BARK AI utilizes GPT-style models to generate audio, ensuring high-quality output.

Semantic Tokens: Instead of using phonemes, BARK AI embeds initial text prompts into high-level semantic tokens, enabling generalization to arbitrary instructions beyond speech, such as music lyrics or non-speech sounds.

Multilingual Support: BARK AI supports multiple languages and automatically detects the language from the input text, employing native accents where applicable. English quality is currently the best, with other languages expected to improve further with scaling.

Music Generation: BARK AI can generate various types of audio, including music. It treats speech and music equally and users can enhance their lyrics by adding music notes.

Voice/Audio Cloning: BARK AI can clone voices with remarkable accuracy, preserving tone, pitch, emotion, and prosody. It can also attempt to retain music and ambient noise from the input audio.
Speaker Prompts: Users can provide speaker prompts such as NARRATOR, MAN, WOMAN, etc., although their influence may be overridden in some cases, especially when conflicting audio history prompts are given.

BARK AI represents a significant advancement in text-to-audio generation, combining advanced models, multilingual support, music generation capabilities, and voice/audio cloning to provide users with an exceptional tool for creating high-quality voice content in various languages and applications.

--
tags:
#BarkAI, #AiVoiceCloning, #TextToSpeech, #VoiceCloning, #ArtificialIntelligence, #AItechnology, #NaturalLanguageProcessing, #VoiceAssistant, #VirtualAssistant, #AIaudio, #SpeechSynthesis, #AIvoice, #VoiceRecognition, #AIcommunication, #FutureOfSpeech, #VoiceTechnology, #AIInnovation, #SpeechGeneration, #VoiceConversion, #SERP

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

SERP AI is freedom of access to artificial intelligence.

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

SERP AI

Related tracks