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Fine tuning Chat GPT-3 for industries Use cases

This webinar is about Fine tuning Chat GPT-3 for specific industries (and several use cases).

Chat GPT-3 is a deep learning model developed by OpenAI that can generate text for tasks such as summarization and question answering. The model can be fine-tuned to improve accuracy and performance by training on specific data sets.

This can be done by providing a reasonable dataset and using an API key from OpenAI. The fine-tuned GPT-3 model can be used for various applications such as question-answering, summarization, entity recognition and classification, and can perform tasks that previously required other machine learning models.

We share our AutoHotkey script for interacting with the Chat GPT-3 API here.  You’ll still need your own account & token however this will help get you started.

 

Fine tuning GPT 3 for industries Use cases
Fine tuning Chat GPT-3 for industries Use cases


Chat GPT-3 is a unidirectional deep learning model that can generate sequences for tasks such as question answering and summarizing, and can be fine-tuned to improve accuracy and performance.

  1. 00:00 🤖 We’ll discuss fine tuning Chat GPT-3 , an AI-powered neural network model developed by OpenAI that uses large amounts of data and compute power to generate sophisticated machine-generated text.
    1. We are a technology-focused company harnessing AI, automation, and predictive capabilities to streamline processes and navigate the future, with three experienced speakers.
    2. We will discuss training, fine-tuning, and best practices for Chat GPT-3 today.
    3. Chat GPT-3 is an AI-powered neural network model developed by OpenAI that can generate large volumes of relevant and sophisticated machine-generated text with just a small amount of input.
    4. Chat GPT-3 was trained on 45TB of Common Crawl data, filtered down to 570GB of plain text, and used weighted sampling to train on 410B tokens in 300B epochs.
    5. Chat GPT-3 used 50 petaflop days and a ton of data to train, while RoBERTa used 50 petaflop days and less data.
    6. Increasing data and compute power results in a decrease in validation loss, making for an efficient and effective training model.
  2. 15:21 🤖 Chat GPT-3 can be fine-tuned to generate higher quality results, train more examples, and save tokens with proper data and planning.
    1. Chat GPT-3 can be pre-trained to perform domain-specific tasks with tuning and fine-tuning for better accuracy.
    2. K-shot learning provides a way to condition a model to give the best response at inference time by giving it a few demonstrations.
    3. Chat GPT-3 can generate names for superheroes based on animal behavior and complete sentences based on context from data sets like Lambda and Hella Swag.
    4. Increasing the number of parameters in a model improves accuracy, but it is still not at par with the state of the art models.
    5. Fine tuning Chat GPT-3 improves accuracy and widens the horizon of what problems it can solve, resulting in higher quality results, ability to train more examples, and token savings.
    6. Properly training your data with the right data and planning can lead to savings and lower latency with a better response.
  3. 26:58 🤖 Fine-tuning Chat GPT-3 with a specific data set can improve its performance for multi-step reasoning problems, allowing businesses to use it for interactive question and answer sessions.
    1. Fine-tuning Chat GPT-3 for a challenging multi-step reasoning problem data set resulted in significant improvement, reaching a 70 mark.
    2. Keeper Tax helps users save taxes by understanding financial statement data, Viable App summarizes customer feedback to make decisions, and an LMS system generates questions and answers based on training content.
    3. Fine-tuning a GPT model allows for more specific output tailored to the needs of a business, such as an interactive question and answer session for research and brainstorming.
    4. GBD3 is a powerful machine learning model that can be fine-tuned to solve specific problems with the help of a dataset.
    5. Publicly listed companies in the US must submit filings to the SEC, which contain a goldmine of information that cannot be answered by a generic model.
    6. To fine-tune Chat GPT-3 for a question answering use case, you need to have your data set in a specific format as listed by Open AI.
  4. 36:33 🤖 Create a fine-tuned Chat GPT-3 model for question-answering by providing a reasonable dataset, using an API key from Open AI, and running a command to pass information to a server.
    1. To fine-tune Chat GPT-3 for a specific document, we need to provide it with a reasonable dataset of curated paragraphs with question-answer pairs.
    2. To create a fine-tuned model, you need an API key from Open AI and a file in JSONL format, which can be converted using Open AI tools or a simple code.
    3. We use a command to pass information to a server, which creates a fine-tuning job and a fine-tuned model that can be used for question answering.
    4. Using a fine-tuned Chat GPT-3 model, we can extract information from SEC filings for financial services, such as Facebook’s 2018 profit of 15.9 billion dollars.
    5. We designed a mobile app for a foodies paradise that uses a dataset of 500 questions and answers about European countries to provide tailored information.
    6. Flutter is a good framework to use for building conversational AI and video chatbot integrations due to its existing packages, well-designed integrations, and tested integrations.
  5. 46:14 🍴 Chat GPT-3 is a model used for summarization, question generation, entity classification, and entity recognition, enabling tasks that previously required other machine learning models.
    1. The three popular German dishes are Grünkohl, Sauerkraut, and Rinderroulade, which are all from the Chat GPT-3 fine-tuned model.
    2. Chat GPT-3 enables you to do classification and other tasks that previously required other machine learning models.
    3. Chat GPT-3 is a model used to summarize domain-specific texts, such as medical abstracts, and can be used for content writing and educational purposes.
    4. Chat GPT-3 can generate physics problems or questions about racing cars with the help of a few examples and fine-tuning.
    5. Using Chat GPT-3, you can train a model to classify customer feedback into categories such as good, very good, bad, and very bad.
    6. Chat GPT-3 helps with summarization, question generation, entity classification, and entity recognition.
  6. 51:50 🤖 Chat GPT-3 has been fine-tuned for 9 months to improve accuracy for classification and sequence generation, but it is not specifically designed for translation and PC is not ideal for regional names detection.
    1. Chat GPT-3 fine tuning should be considered proportionally, as the charges depend on the number of tokens and length of the data set.
    2. We have extensive experience in deploying transformer-based architectures for various NLP tasks.
    3. Chat GPT-3 has been fine-tuned for the last 9 months, allowing for more accurate classification and sequence generation than GB2.
    4. Chat GPT-3 is trained on English documents and can be used for translation, but it is not specifically fine-tuned for this purpose.
    5. PC can be used to detect foreign names, but it does not perform well on Indian names or other regional names.
    6. You need to have ground storage, databases, curation, training data, model retuning, evaluation and monitoring to create an end-to-end workflow for an organization’s requirements.
  7. 59:20 🤔 Chat GPT-3 is a unidirectional deep learning model that can generate sequences for tasks such as question answering and summarizing, but requires human help for more complex tasks like sentiment analysis.
    1. It is possible to match intent and meaning of two different questions using simpler models such as cosine similarity, but for more complex tasks such as sentiment analysis, a human being must help a deep learning model like Chat GPT-3.
    2. The metric used to identify if more prompt is needed to improve accuracy varies depending on the problem and type of classification.
    3. Chat GPT-3 can be used for classification problems and can be used with fixed sets of values to generate intent without needing to be fine-tuned.
    4. Chat GPT-3 is unidirectional, meaning it is dependent on context on one side, which is both a strength and limitation.
    5. Chat GPT-3 is explicitly trained to focus only on the left context and mask the right context, making it unidirectional.
    6. Chat GPT-3 is a generative model that can understand context and generate sequences for tasks such as question answering and summarizing.
  8. 01:09:34 🤖 Chat GPT-3 has expanded the range of machine learning problems that can be solved, and it is easy to integrate and fine-tune with only a few samples.
    1. When using the same prompt, results are generally different, but seeding and providing examples can help control the output.
    2. You can control the randomness of Chat GPT-3 using temperature, and use cosine similarity to measure semantic similarity without Chat GPT-3.
    3. Language models are often fine-tuned on Eurocentric datasets, but there is an opportunity to use GBC to direct data and model drift for other parts of the world.
    4. Chat GPT-3 has expanded the range of machine learning problems that can be solved, and it is easy to integrate and fine-tune with only a few samples.
    5. For generative use cases, fine tuning of lower models is recommended as it costs less and requires fewer tokens than higher models.
    6. Pass parameters to control model name, GPU type, and other things when using GPT3 and MLPs.

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