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The difference is a mere difference in the terminology used. When the model is trained on a large generic corpus, it is called 'pre-training'. When it is adapted to a particular task or dataset it is called 'fine-tuning'. Technically speaking, in either case ('pre-training or 'fine-tuning'), there are updates to the model weights. For example, usually, you can just take the pre-trained model and then fine-tune it for a specific task (such as classification, question-answering, etc.). However, if you find that the target dataset is from a specific domain, and you have a few unlabeled data that might help the model to adapt to the particular domain, then you can do an MLM or MLM+NSP 'fine-tuning' (unsupervised learning) (some researchers do call this as 'pre-training' especially when a huge corpus is used to train the model), followed by using the target corpus with target task fine-tuning.

This blog post will discuss a new approach to training large language models (LLMs) called Reflexion . LLMs are a type of artificial intelligence (AI) that are trained on massive amounts of text data. This allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, traditional reinforcement learning methods can be challenging for LLMs because they require extensive training samples and fine-tuning. Reflexion addresses this challenge by using verbal reinforcement to help agents learn from their mistakes. Here's a breakdown of the key points: What is Reflexion? Reflexion is a novel framework that reinforces LLMs through linguistic feedback. Instead of using rewards or punishments, Reflexion agents receive textual summaries of their performance. How Does It Work? Reflexion agents interact with an environment and receive feedback on their actions. This feedback is then converted into natural...

In today's fast-paced world of machine learning and artificial intelligence, it's essential to be able to showcase your models and applications effectively. Gradio is an open-source Python package that simplifies this process, allowing you to create stunning demos and web applications for your machine learning models or Python functions effortlessly. In this post, we'll explore the basics of Gradio and how you can use it to build interactive demos with just a few lines of code. What is Gradio? Gradio is a Python package designed to streamline the creation of demos and web applications for machine learning models, APIs, or any arbitrary Python function. It eliminates the need for extensive knowledge of JavaScript, CSS, or web hosting, making it accessible to a wide range of users. Getting Started with Gradio To begin using Gradio, ensure you have Python 3.8 or higher installed on your system. You can then install Gradio using pip. pip install gradio Once installed, you can s...

Pre-training Fine tuning PEFT Adapter Tuning LoRA Quantization Prompt Modifications Hard Prompt Soft Prompt Prompt Tuning Prefix Tuning P-tuning Pre-training Pre-training is when you have the entire architecture of neural net model in front of you and you train it on a huge dataset from scratch. We can also call it self-supervised training because there are no separate labels assigned to the text data. We can use this unlabeled data for training by simply thinking of it as a next word prediction task where the next word is already made available to us. The large language models are generally pre-trained on variety of tasks involving creative writing, writing emails, text summarization among others. Fine tuning We can think of fine tuning as a process in which we freeze the parameters of all except the last few layers of the pre-trained model. Then, we train the parameters of the last layers on a downstream task such as sentiment analysis or classification using a labeled dataset wh...

In the realm of machine learning and natural language processing (NLP), understanding sequences is paramount. A sequence can be anything from a sentence in language, to medical signals, or even the waveform of speech. At its core, a sequence is an ordered collection of elements that conveys meaningful information. Consider the task of predicting the next word in a sentence, a classic sequence modeling problem. Given the context of a sentence like "This morning I took the dog for a walk," predicting the next word requires understanding the sequential flow of language. One approach is to use a fixed window, considering a subset of words to predict the next one. However, this method has limitations, particularly in capturing long-term dependencies. For instance, in a sentence like "In Finland, I had a great time and I learnt some of the _________ language," accurately predicting the missing word necessitates understanding information from both distant past and future w...