Difference between revisions of "FLAN-T5 LLM"
m |
m |
||
| (19 intermediate revisions by the same user not shown) | |||
| Line 2: | Line 2: | ||
|title=PRIMO.ai | |title=PRIMO.ai | ||
|titlemode=append | |titlemode=append | ||
| − | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
| − | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
}} | }} | ||
[http://www.youtube.com/results?search_query=Transfer+Learning+machine+neural+network YouTube search...] | [http://www.youtube.com/results?search_query=Transfer+Learning+machine+neural+network YouTube search...] | ||
[http://www.google.com/search?q=Transfer+Learning+deep+machine+learning+ML ...Google search] | [http://www.google.com/search?q=Transfer+Learning+deep+machine+learning+ML ...Google search] | ||
| − | * [[ | + | * [[Conversational AI]] ... [[ChatGPT]] | [[OpenAI]] ... [[Bing/Copilot]] | [[Microsoft]] ... [[Gemini]] | [[Google]] ... [[Claude]] | [[Anthropic]] ... [[Perplexity]] ... [[You]] ... [[phind]] ... [[Ernie]] | [[Baidu]] |
| − | * [[ | + | * [[What is Artificial Intelligence (AI)? | Artificial Intelligence (AI)]] ... [[Generative AI]] ... [[Machine Learning (ML)]] ... [[Deep Learning]] ... [[Neural Network]] ... [[Reinforcement Learning (RL)|Reinforcement]] ... [[Learning Techniques]] |
| − | * [[Transfer Learning]] | + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] |
| + | * [[Perspective]] ... [[Context]] ... [[In-Context Learning (ICL)]] ... [[Transfer Learning]] ... [[Out-of-Distribution (OOD) Generalization]] | ||
| + | * [[Causation vs. Correlation]] ... [[Autocorrelation]] ...[[Convolution vs. Cross-Correlation (Autocorrelation)]] | ||
| + | * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]] ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ... [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]] | ||
| + | * [[BLIP-2]] | Salesforce Research ... a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. It achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods. | ||
| + | * [[Colaboratory|Colab]] [[Jupyter]] Notebook | [[Google]] | ||
| + | * [[Python]] ... [[Generative AI with Python|GenAI w/ Python]] ... [[JavaScript]] ... [[Generative AI with JavaScript|GenAI w/ JavaScript]] ... [[TensorFlow]] ... [[PyTorch]] | ||
| + | * [https://gradio.app/ Gradio] for GUI ... [[LangChain#Gradio|LangChain with Gradio]] | ||
<b>T5</b> stands for “Text-To-Text Transfer [[Transformer]]” . It is a model developed by [[Google]] Research that converts every language problem into a text-to-text format. T5 is an [[Autoencoder (AE) / Encoder-Decoder|encoder-decoder model]] pre-trained on a multi-task mixture of [[unsupervised]] and [[supervised]] tasks and for which each task is converted into a text-to-text format. It was presented in a paper by Google called "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task. [[Large Language Model (LLM)]] are AI models that have been trained on large amounts of text data and can generate human-like text. - [https://huggingface.co/docs/transformers/model_doc/t5 T5] | [[Hugging Face]] | <b>T5</b> stands for “Text-To-Text Transfer [[Transformer]]” . It is a model developed by [[Google]] Research that converts every language problem into a text-to-text format. T5 is an [[Autoencoder (AE) / Encoder-Decoder|encoder-decoder model]] pre-trained on a multi-task mixture of [[unsupervised]] and [[supervised]] tasks and for which each task is converted into a text-to-text format. It was presented in a paper by Google called "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task. [[Large Language Model (LLM)]] are AI models that have been trained on large amounts of text data and can generate human-like text. - [https://huggingface.co/docs/transformers/model_doc/t5 T5] | [[Hugging Face]] | ||
| Line 16: | Line 32: | ||
<b>FLAN-T5</b> is an improved version of T5 with some architectural tweaks. FLAN stands for “Fine-tuned LAnguage Net”. It was developed by [[Google]] Research and is pre-trained on C4 only without mixing in the [[supervised]] tasks. FLAN-T5 is designed to be highly customizable, allowing developers to fine-tune it to meet their specific needs. This means that developers can adjust the model’s parameters and architecture to better fit the data and task at hand. This can result in improved performance and accuracy on specific tasks. For example, a developer could fine-tune FLAN-T5 on a specific dataset to improve its performance on a particular language translation task. This flexibility makes FLAN-T5 a powerful tool for [[Natural Language Processing (NLP)]] tasks. | <b>FLAN-T5</b> is an improved version of T5 with some architectural tweaks. FLAN stands for “Fine-tuned LAnguage Net”. It was developed by [[Google]] Research and is pre-trained on C4 only without mixing in the [[supervised]] tasks. FLAN-T5 is designed to be highly customizable, allowing developers to fine-tune it to meet their specific needs. This means that developers can adjust the model’s parameters and architecture to better fit the data and task at hand. This can result in improved performance and accuracy on specific tasks. For example, a developer could fine-tune FLAN-T5 on a specific dataset to improve its performance on a particular language translation task. This flexibility makes FLAN-T5 a powerful tool for [[Natural Language Processing (NLP)]] tasks. | ||
| − | [https://huggingface.co/google/flan-t5-xl <b>FLAN-T5- | + | [https://huggingface.co/google/flan-t5-xl <b>FLAN-T5-XL</b>] ... [https://huggingface.co/google/flan-t5-xxl <b>FLAN-T5-XXL</b> is 11b] | [[Hugging Face]] |
| Line 22: | Line 38: | ||
| + | <youtube>oCU97mnl494</youtube> | ||
<youtube>oCU97mnl494</youtube> | <youtube>oCU97mnl494</youtube> | ||
<youtube>SHMsdAPo2Ls</youtube> | <youtube>SHMsdAPo2Ls</youtube> | ||
<youtube>_Qf_SiCLzw4</youtube> | <youtube>_Qf_SiCLzw4</youtube> | ||
<youtube>jgKj-7v2UYU</youtube> | <youtube>jgKj-7v2UYU</youtube> | ||
| + | <youtube>lNJQFn84rCA</youtube> | ||
Latest revision as of 16:42, 28 April 2024
YouTube search... ...Google search
- Conversational AI ... ChatGPT | OpenAI ... Bing/Copilot | Microsoft ... Gemini | Google ... Claude | Anthropic ... Perplexity ... You ... phind ... Ernie | Baidu
- Artificial Intelligence (AI) ... Generative AI ... Machine Learning (ML) ... Deep Learning ... Neural Network ... Reinforcement ... Learning Techniques
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Perspective ... Context ... In-Context Learning (ICL) ... Transfer Learning ... Out-of-Distribution (OOD) Generalization
- Causation vs. Correlation ... Autocorrelation ...Convolution vs. Cross-Correlation (Autocorrelation)
- Large Language Model (LLM) ... Natural Language Processing (NLP) ...Generation ... Classification ... Understanding ... Translation ... Tools & Services
- BLIP-2 | Salesforce Research ... a generic and efficient pre-training strategy that bootstraps vision-language pre-training from off-the-shelf frozen pre-trained image encoders and frozen large language models. It achieves state-of-the-art performance on various vision-language tasks, despite having significantly fewer trainable parameters than existing methods.
- Colab Jupyter Notebook | Google
- Python ... GenAI w/ Python ... JavaScript ... GenAI w/ JavaScript ... TensorFlow ... PyTorch
- Gradio for GUI ... LangChain with Gradio
T5 stands for “Text-To-Text Transfer Transformer” . It is a model developed by Google Research that converts every language problem into a text-to-text format. T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which each task is converted into a text-to-text format. It was presented in a paper by Google called "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer". T5 works well on a variety of tasks out-of-the-box by prepending a different prefix to the input corresponding to each task. Large Language Model (LLM) are AI models that have been trained on large amounts of text data and can generate human-like text. - T5 | Hugging Face
FLAN-T5 is an improved version of T5 with some architectural tweaks. FLAN stands for “Fine-tuned LAnguage Net”. It was developed by Google Research and is pre-trained on C4 only without mixing in the supervised tasks. FLAN-T5 is designed to be highly customizable, allowing developers to fine-tune it to meet their specific needs. This means that developers can adjust the model’s parameters and architecture to better fit the data and task at hand. This can result in improved performance and accuracy on specific tasks. For example, a developer could fine-tune FLAN-T5 on a specific dataset to improve its performance on a particular language translation task. This flexibility makes FLAN-T5 a powerful tool for Natural Language Processing (NLP) tasks.
FLAN-T5-XL ... FLAN-T5-XXL is 11b | Hugging Face
