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GPT-4 Turbo vs Orca-2-13B AI models compared

GPT-4 Turbo vs Orca-2-13B large language AI models compared

In the ever-evolving world of artificial intelligence (AI), there’s a lot of talk about how we should build and share AI technologies. Two main approaches are often discussed: open-source AI and proprietary AI. A recent experiment that compared an open-source AI model called Orca-2-13B with a proprietary model known as GPT-4 Turbo has sparked a lively debate. This debate is not just about which model is better but about what each approach means for the future of AI.

The open-source AI model, Orca-2-13B, is a shining example of transparency, collaboration, and innovation. Open-source AI is all about sharing code and ideas so that everyone can work together to make AI better. This approach believes that when we make AI technology open for all, we create a space where anyone with the right skills can help improve it. One of the best things about open-source AI is that you can see how the AI makes decisions, which is really important for trusting AI systems. Plus, open-source AI benefits from communities like GitHub, where developers from all over can work together to make AI models even better.

Orca 2 is Microsoft’s latest development in its efforts to explore the capabilities of smaller LMs (on the order of 10 billion parameters or less). With Orca 2, it demonstrates that improved training signals and methods can empower smaller language models to achieve enhanced reasoning abilities, which are typically found only in much larger language models.

Orca-2-13B large language AI model comparison chart

On the other side, we have proprietary AI, like GPT-4 Turbo, which focuses on security, investment, and accountability. Proprietary AI is usually made by companies that spend a lot of money on research and development. They argue that this investment is key to making AI smarter and more capable. With proprietary AI, the code isn’t shared openly, which helps protect it from being used in the wrong way. Companies that make proprietary AI are also in charge of making sure the AI works well and meets ethical standards, which is really important for making sure AI is safe and effective.

GPT-4 Turbo vs Orca-2-13B

  • Orca-2-13B (Open-Source AI)
    • Focus: Emphasizes transparency, collaboration, and innovation.
    • Benefits:
      • Encourages widespread participation and idea sharing.
      • Increases trust through transparent decision-making processes.
      • Fosters innovation by allowing communal input and improvements.
    • Challenges:
      • Potential for fragmented efforts and resource dilution.
      • Quality assurance can be inconsistent without structured oversight.
  • GPT-4 Turbo (Proprietary AI)
    • Focus: Concentrates on security, investment, and accountability.
    • Benefits:
      • Higher investment leads to advanced research and development.
      • Greater control over AI, ensuring security and ethical compliance.
      • More consistent quality assurance and product refinement.
    • Challenges:
      • Limited accessibility and collaboration due to closed-source nature.
      • Might induce skepticism due to lack of transparency in decision-making.

The discussion around Orca-2-13B and GPT-4 Turbo has highlighted the strengths and weaknesses of both approaches. Open-source AI is great for driving innovation, but it can lead to a lot of similar projects that spread resources thin. Proprietary AI might give us more polished and secure products, but it can lack the openness that makes people feel comfortable using it.

Another important thing to think about is accessibility. Open-source AI is usually easier for developers around the world to get their hands on, which means more people can bring new ideas and improvements to the table. However, without strict quality checks, open-source AI might not always be reliable.

After much debate, there seems to be a slight preference for the open-source AI model, Orca-2-13B. The idea of an AI world that’s more inclusive, creative, and open is really appealing. But it’s also clear that we need to have strong communities and good quality checks to make sure open-source AI stays on the right track.

For those interested in open-source AI, there’s a GitHub repository available that has all the details of the experiment. It even includes a guide on how to use open-source models. This is a great opportunity for anyone who wants to dive into AI and be part of the ongoing conversation about where AI is headed.

The debate between open-source and proprietary AI models is about more than just code. It’s about deciding how we want to shape the development of AI. Whether you like the idea of working together in the open-source world or prefer the structured environment of proprietary AI, it’s clear that both ways of doing things will have a big impact on building an AI future that’s skilled, secure, and trustworthy.

Here are some other articles you may find of interest on the subject of AI model comparisons :

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Porsche Panamera Turbo Sonderwunsch unveiled in Shanghai

Porsche Panamera Turbo Sonderwunsch

The new Porsche Panamera is now official and earlier today we got to see a video of the car, Porsche unveiled a special edition of the new Panamera in Shanghai, the Porsche Panamera Turbo Sonderwunsch.

The Porsche Panamera Turbo Sonderwunsch is a one-off version of the new Panamera that was created by Porsche Exclusive Manufaktur for one of their customers, more details on the car are below.

Porsche Panamera Turbo Sonderwunsch

“Our customers value the option of adding their personal touch to the design of their car. The Panamera Turbo Sonderwunsch based on the new Panamera Turbo E-Hybrid shows how flexibly and precisely we can fulfil these wishes as a vision of a customer’s dream. Specially created colour tones, individual accents, and planning down to the last detail have transformed the Panamera into a genuinely unique car,” says Detlev von Platen, Member of the Executive Board for Sales and Marketing. “The exterior of the car has been designed by experts from the Style Porsche and Exclusive Manufaktur units. The interior has been deliberately left unfinished until next year. This one-off car will inspire people to make their own very personal dream of a highly individual Panamera a reality.”

Porsche Panamera Turbo Sonderwunsch

You can find out more details about this interesting and unique Porsche Panamera Turbo Sonderwunsch over at Porsche at the link below, there are no details on how much the car cost.

Source Porsche

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How to fine-tune ChatGPT 3.5 Turbo AI models for different tasks

How to fine-tune ChatGPT Turbo

We have already covered how you can automate the fine tuning process of OpenAI’s ChatGPT 3.5 Turbo but what if you would like to fine tune it for a specific task. AI enthusiast and YouTuber All About AI has created a great instructional video on how to do just that. Providing insight on how you can use the powerful ChatGPT 3.5 Turbo AI model to accomplish a wide variety of different tasks, training using specific data.

The process of fine-tuning the ChatGPT 3.5 Turbo model for a specific task, which in this case is to generate responses in CSV format compares the performance of ChatGPT 3.5 Turbo with GPT-4. When it comes to fine-tuning an AI model like ChatGPT 3.5 Turbo, the goal is to enhance its ability to handle the nuances of a particular task. By focusing on this fine-tuning, you can significantly improve the model’s ability to generate structured outputs, such as CSV files, with greater accuracy and relevance to the task at hand.

The foundation of any successful fine-tuning effort is a high-quality dataset. The adage “garbage in, garbage out” holds true in the realm of AI. It’s crucial to ensure that the synthetic datasets you create, possibly with the help of GPT-4, are varied and unbiased. This is a critical step for the model to learn effectively.

When comparing ChatGPT 3.5 Turbo with GPT-4, you’re looking at two of the most advanced AI language models available. Their performance can vary based on the specific task. For tasks that involve generating structured CSV responses, it’s important to determine which model can be fine-tuned more effectively to produce accurate and reliable outputs. GPT-4 boasts advanced capabilities that can be utilized to generate synthetic datasets for fine-tuning purposes. Its ability to create complex datasets that mimic real-world scenarios is essential for preparing the model for fine-tuning.

Fine tuning ChatGPT 3.5 Turbo

Here are some other articles you may find of interest on the subject of fine tuning large language models :

Once you have your synthetic dataset, the next step is to carefully select the best examples from it. These examples will teach the AI model to recognize the correct patterns and generate appropriate responses. It’s important to find the right mix of diversity and quality in these examples.

To start the fine-tuning process, you’ll use scripts to automate the data upload. These scripts are crucial for ensuring efficiency and accuracy when transferring data to the AI model. With the data in place, you can begin fine-tuning. After fine-tuning, it’s necessary to understand the results. This is where performance metrics come into play. They provide objective evaluations of the model’s accuracy, responsiveness, and reliability. These metrics will show you how well the model is performing and whether it needs further refinement.

The last step is to thoroughly test the fine-tuned ChatGPT 3.5 Turbo model. It’s essential to confirm that the model can reliably handle the task of generating structured CSV responses in a variety of scenarios. Fine-tuning AI models like ChatGPT 3.5 Turbo opens up a wide range of possibilities for tasks that require structured outputs. Whether it’s generating reports, summarizing data, or creating data feeds, the potential applications are vast and varied.

Refining ChatGPT 3.5 Turbo for CSV response generation is a detailed process that requires careful planning, the use of high-quality datasets, and a thorough understanding of performance metrics. By following the steps outlined in this guide, you can enhance the model’s capabilities and tailor it to your specific needs, ensuring that the AI’s output is not just insightful but also well-structured and actionable.

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How to combine GPT-4 Turbo with Google web browsing

How to combine GPT-4 Turbo with Google web browsing using the Assistants API

Being able to combine the power of OpenAI’s latest GPT-4 Turbo AI model and Google web browsing using the Assistants API opens up a wide variety of new applications that can take your business, SaaS or ideas to the next level. Search engines, like Google, have become the gatekeepers of vast amounts of data, and AI is the key to unlocking the most relevant and personalized results. Let’s explore the sophisticated technologies that are enhancing the way we search for information, making it a more intuitive and efficient experience.

When you type a query into a search bar, you expect more than just a list of links. You want answers that are tailored to your needs and preferences. This is where AI modeling comes into play. By integrating advanced AI, such as OpenAI’s GPT-4 Turbo, search engines can interpret your queries with a deeper understanding. This means that the results you get are not just related to your question, but they also take into account the context of your search.

Browsing the web with GPT-4 Turbo

The backbone of this seamless integration lies in Application Programming Interfaces (APIs), like the Google Search API. These APIs allow the AI model to quickly process your questions and fetch the most relevant search results. Alongside APIs, web scraping tools, such as Beautiful Soup, are used to gather data from the web pages that appear in your search results. This combination ensures that the information you receive is both up-to-date and comprehensive.

But how does it all start? With your questions. The AI system takes your queries and optimizes them for the search engine, ensuring that the essence of what you’re asking for is captured. Then, it retrieves URLs from the search results, diving into the web’s vast pool of information. The real magic happens in how the AI presents the information to you. Whether you prefer quick bullet points or a detailed JSON format for integrating data, the AI adapts to your needs. It can even add a touch of creativity to the responses, making the process of finding information more engaging.

Building apps with GPTs web browsing functionality

Other articles we have written that you may find of interest on the subject of coding with AI models, ChatGPT and Copilot :

Conversion of user queries into optimized search queries

Let’s consider a practical example. Say you’re looking to find out the latest on Sam Altman’s role at OpenAI. The AI system doesn’t just give you the latest news; it organizes it in a way that’s easy to digest. Or perhaps you’re curious about who won the Las Vegas F1 Grand Prix. The AI quickly scans the web for the latest results, keeping you updated as events happen.

The integration of AI with search engines is revolutionizing how we access information. By leveraging cutting-edge technologies like GPT-4 Turbo, the Google Search API, and web scraping tools such as Beautiful Soup, the system provides search results that are not only optimized and current but also customized to your preferences. As you journey through the vast information superhighway, AI stands as a powerful companion, delivering the knowledge you seek with unprecedented efficiency.

The potent power of GPTs and web browsing

This advanced integration of AI into search engines is not just about getting answers; it’s about getting the right answers quickly and in a way that resonates with you. It’s about having a digital assistant that understands not just the words you type, but the intent behind them. With these AI-enhanced search capabilities, the world’s information is at your fingertips, ready to be accessed and utilized in ways that were once unimaginable.

Combining ChatGPT with web browsing capabilities for creating custom Software as a Service (SaaS) applications and enhancing business services and websites offers several powerful advantages:

  • Access to Real-Time Information: ChatGPT, when integrated with web browsing, can access and retrieve the most current data from the web. This is crucial for businesses that rely on up-to-date information, such as market trends, news updates, or regulatory changes.
  • Enhanced User Experience: ChatGPT can provide interactive and personalized experiences for users. By combining this with web browsing, the interaction becomes even more relevant and engaging, as it can pull in live data or additional context from the web to enhance the conversation.
  • Automated Research and Data Gathering: For SaaS applications, especially those involving data analysis, market research, or competitive intelligence, the ability to automatically gather and process information from the web is invaluable. This reduces manual effort and increases efficiency.
  • Dynamic Content Generation: Businesses can use ChatGPT to generate content dynamically for websites or applications. When combined with web browsing, this content can be tailored to current events, user preferences, or specific queries, keeping the content fresh and relevant.
  • Customer Support and Engagement: ChatGPT can provide immediate, 24/7 customer support. By integrating web browsing, it can pull specific information, such as FAQs, product details, or policy information, directly from the business’s website or other relevant sources, offering more accurate and helpful responses.
  • Scalability and Cost-Efficiency: Automating tasks like customer service, data gathering, and content creation with ChatGPT and web browsing can significantly reduce costs and allow for easy scaling as the business grows.
  • Informed Decision Making: For business analytics and decision support systems, combining ChatGPT’s ability to reason and explain with real-time data from the web can lead to more informed and timely decisions.
  • Personalization and Targeting: SaaS applications can use this combination to better understand user needs and preferences, customizing services and content accordingly, which enhances user satisfaction and engagement.
  • Continuous Learning and Improvement: As ChatGPT interacts with users and web content, it can learn from these interactions, leading to continuous improvement in its responses and recommendations.
  • Seamless Integration with Existing Systems: ChatGPT can be integrated with existing business systems and workflows, enhancing them with web browsing capabilities without the need for major overhauls or disruptions.

As we continue to rely on the internet for knowledge, entertainment, and communication, the importance of efficient search capabilities cannot be overstated. The AI-driven search is a testament to the incredible progress we’ve made in technology, and it’s a glimpse into a future where our interactions with machines are more natural and productive.

The implications of this technology are vast. For businesses, it means being able to provide customers with instant, accurate information. For researchers, it streamlines the process of sifting through endless data to find relevant studies and data. For the everyday user, it simplifies the quest for knowledge, whether it’s for learning a new skill, keeping up with current events, or just satisfying a curious mind.

As we look ahead, the potential for AI to further enhance our search experiences is boundless. We can expect even more personalized results, faster response times, and perhaps even predictive search capabilities that anticipate our questions before we even ask them. The integration of AI into search engines is a significant step forward in our digital evolution, making information more accessible and useful for everyone.

So, the next time you find yourself typing a question into a search bar, take a moment to appreciate the complex technology at work behind the scenes. AI is not just changing the way we search; it’s changing the way we interact with the world’s knowledge. It’s a powerful tool that, when used wisely, can help us make more informed decisions, spark new ideas, and continue to push the boundaries of what’s possible.

Further articles you may find of interest on business automation systems using AI :

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GPT-4 Turbo 128K context length performance tested

GPT-4 Turbo 128K context length performance tested

Recently, OpenAI unveiled its latest advancement in the realm of artificial intelligence: the GPT-4 Turbo. This new AI model boasts a substantial 128K context length, offering users the ability to process and interact with a much larger swath of information in a single instance. The introduction of GPT-4 Turbo invites a critical question: How well does it actually perform in practical applications?

Before delving into the specifics of GPT-4 Turbo, it’s important to contextualize its place in the lineage of Generative Pretrained Transformers (GPTs). The GPT series has been a cornerstone in the AI field, known for its ability to generate human-like text based on the input it receives. Each iteration of the GPT models has brought enhancements in processing power, complexity, and efficiency, culminating in the latest GPT-4 Turbo.

The 128K context window of GPT-4 Turbo is its most notable feature, representing a massive increase from previous versions. This capability allows the model to consider approximately 300 pages of text at once, providing a broader scope for understanding and generating responses. Additionally, GPT-4 Turbo is designed to be more economical, reducing costs for both input and output tokens significantly compared to its predecessor, the original GPT-4. This cost efficiency, combined with its ability to produce up to 4096 output tokens, makes it a potent tool for extensive text generation tasks.

GPT-4 Turbo 128K context length performance tested

Check out the video below to learn more about the new GPT-4 Turbo 128K context length and its implications and applications.

Other articles we have written that you may find of interest on the subject of GPT-4 Turbo 128K :

However, advancements in technology often come with new challenges. One of the primary issues with GPT-4 Turbo, and indeed many large language models, is the “lost in the middle” phenomenon. This refers to the difficulty these models have in processing information that is neither at the very beginning nor at the end of a given context. While GPT-4 Turbo can handle vast amounts of data, its efficacy in navigating and utilizing information located in the middle of this data is still under scrutiny. Early tests and observations suggest that despite its expanded capabilities, GPT-4 Turbo may still struggle with comprehending and integrating details from the central portions of large data sets.

This challenge is not unique to GPT-4 Turbo. It reflects a broader pattern observed in the field of language modeling. Even with advanced architectures and training methods, many language models exhibit decreased performance when dealing with longer contexts. This suggests that the issue is a fundamental one in the realm of language processing, transcending specific model limitations.

Interestingly, the solution to this problem might not lie in continually increasing the context window size. The relationship between the size of the context window and the accuracy of information retrieval is complex and not always linear. In some cases, smaller context windows can yield more accurate and relevant outputs. This counterintuitive finding underscores the intricacies of language processing and the need for careful calibration of model parameters based on the specific application.

As the AI community continues to explore and refine models like GPT-4 Turbo, the focus remains on improving their ability to handle extensive contexts effectively. The journey of GPT models is characterized by continuous learning and adaptation, with each version bringing us closer to more sophisticated and nuanced language processing capabilities.

For those considering integrating GPT-4 Turbo into their workflows or products, it’s crucial to weigh its impressive capabilities against its current limitations. The model’s expanded context window and cost efficiency make it a compelling choice for a variety of applications, but understanding how it performs with different types and lengths of data is key to making the most out of its advanced features. GPT-4 Turbo represents a significant stride in the ongoing evolution of language models. Its expanded context window and cost efficiency are remarkable, but as with any technology, it’s essential to approach its use with a clear understanding of both its strengths and areas for improvement.

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Porsche Turbo models to get new look

Porsche Turbo models

Porsche has announced that it is planning to give its Turbo models a new look to further differentiate these models from other cars in their lineup. This will start with the new Porsche Panamera which is going to be made official on the 24th of November.

“In 1974, we presented the first turbocharged 911. Since then, the Turbo has become a synonym for our high-performance top models and is now more or less a brand of its own. We now want to make the Turbo even more visible, and differentiate it more markedly from other derivatives such as the GTS,” explains Michael Mauer, Vice President Style Porsche. “This is why we’ve developed a distinctive Turbo aesthetic. From now on, the Turbo versions will exhibit a consistent appearance across all model series – one that is elegant, high-quality and very special.”

The new Turbonite metallic tone is exclusively reserved for the Turbo models. Like all of our paints, this one was very carefully composed by the Porsche Colour & Trim experts. Gold elements create an elegant, metallising effect, with the top layer in a contrasting satin finish. The lettering on the rear and the Daylight Opening (DLO), as well as the borders of the side windows, will be given a Turbonite finish in the Turbo models in future. Depending on the model series, further details such as the inlays in the front aprons, the spokes, or the aeroblades in the light alloy wheels could feature Turbonite paintwork.

You can find out more details about what Porsche has planned for its new Turbo models over at the Porsche website at the link below, as soon as we get some more details on what they have planned, we will let you know.

Source Porsche

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Using GPT-4 Turbo and GPTs to create next generation AI apps

Using GPT-4 Turbo and GPTs to create next generation AI apps

OpenAI has recently introduced an advanced model called GPT-4 Turbo together with a wealth of other new enhancements and services one of which is the creation of new GPTs. These GPTs have been specifically created to enable anyone to create custom AI models for a wide variety of different applications and sell these on the upcoming GPT Store. This innovative AI model creation system is a significant step towards the development of AI agents that can carry out tasks on your behalf.

Although these AI agents are not fully autonomous, they represent a major leap towards the creation of completely independent AI entities, signaling a new phase in the AI industry. It’s also worth mentioning that at the end of the very first OpenAI Developer Conference, OpenAI’s CEO Sam Altman said “What we launched today is going to look very quaint relative to what we’re busy creating for you now.” Suggesting that perhaps more amazing and autonomous AI functionality is on its way.

The GPT-4 Turbo model is a key part of OpenAI’s broader mission to make AI technology accessible to a wider audience. To support this goal, OpenAI has launched an AI app store, a platform where you can sell your AI bots. This online platform is carefully designed to be user-friendly, enabling individuals without a background in coding or machine learning to easily create and deploy AI agents.

The future of creating AI apps

Check out the video below kindly created by Wes Roth  to learn more about what we can expect from the new GTPs  and how they are going to change the landscape of app development and the integration of  artificial intelligence into our everyday lives.

Other articles you may find of interest on the subject of ChatGPT-4 Turbo :

building GPTs apps

The OpenAI API is the main interface for interacting with OpenAI’s AI models. This API allows you to engage with the AI models, making it easier to create AI agents, use advanced analytics, and even generate images using AI, such as with the innovative DallE 3 image generation feature. One other key features of the GPT-4 Turbo model is its ability to smoothly integrate with third-party services. For example, you can use Zapier, a popular automation tool, to create automated workflows or “Zaps” that include your AI agents. This integration allows you to automate tasks and processes, freeing up your time for other activities.

The introduction of GPT-4 Turbo and the AI app store is expected to significantly impact various sectors. In e-commerce, for instance, AI bots can be used to automate tasks like customer service or inventory management, improving efficiency and productivity. On social media platforms, AI bots can automate posts or interact with followers, changing the way businesses communicate with their audience. In education, AI models like Chad GPT can act as AI mentors, providing personalized learning support to students and transforming the learning experience.

 GPT-4 Turbo

GPT-4 Turbo is OpenAI’s latest generation model. It’s more capable, has an updated knowledge cutoff of April 2023 and introduces a 128k context window (the equivalent of 300 pages of text in a single prompt). The model is also 3X cheaper for input tokens and 2X cheaper for output tokens compared to the original GPT-4 model. The maximum number of output tokens for this model is 4096.

In addition to these features, OpenAI’s recent developments also have implications for the field of machine learning. The GPT-4 Turbo model includes a memory system that allows it to learn from past experiences and improve its performance over time. This model also has a code interpreter, enabling it to understand and execute code, a feature that could potentially transform the field of AI-powered programming.

OpenAI’s recent advancements also have potential uses in the field of advanced analytics. By using AI to analyze large datasets, businesses and researchers can discover insights that would be difficult, if not impossible, to find using traditional methods. For example, an AI-powered program could examine social media posts to identify trends in consumer behavior, or it could analyze financial data to predict market trends, providing valuable insights for decision-making.

OpenAI’s launch of the GPT-4 Turbo model and the AI app store represents a significant advancement in the field of AI. By enabling users to create AI agents and sell them on a user-friendly platform, OpenAI is making AI technology more accessible. Furthermore, by integrating with third-party services and using advanced machine learning techniques, these developments have the potential to transform various sectors, from e-commerce to education, marking a new phase in the application of AI.

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OpenAI ChatGPT-4 Turbo tested and other important updates

OpenAI GPT-4 Turbo tested

If you are interested in learning more about all the new updates released by OpenAI to its GPT-4 AI model and other services and news from the first ever OpenAI Dev Day Keynote event. This quick overview provides more insight into what you can expect from the performance of the latest ChatGPT-4 Turbo AI model and also an overview of the other enhancements, features and services announced by OpenAI that will soon be available for ChatGPT users to enjoy.

During the conference Sam Altman announced the imminent release of ChatGPT-4 Turbo, an upgraded version of the already sophisticated language model GPT-4, represents a significant leap forward in the field of AI development. This improved model introduces six key enhancements that are set to transform how developers work with AI.

 ChatGPT-4 Turbo

These enhancements include a longer context length, greater user control, improved knowledge, the addition of new modalities, customization options, and increased rate limits. The cut-off date for the new ChatGPT-4 Turbo AI model has also been increased to April 2023 allowing the AI model to expand its knowledge from the original September 2021 cut-off date for ChatGPT-4 released back in March 2023.

The new 128k context window, when used with the ChatGPT-4 Turbo engine, allows for extensive text processing. This means you can process larger amounts of text at once, making it easier to analyze and generate text-based content. This is especially useful for tasks such as document analysis, content generation, and machine translation.

Other articles you may find of interest on the subject of OpenAI and its products :

OpenAI GPT Price reductions

In an effort to make AI more accessible, OpenAI has reduced the price of GPT-4 Turbo. This strategic move is designed to make AI more affordable and accessible to a wider range of developers. By reducing the financial barrier to entry, OpenAI is encouraging more developers to explore and use AI technologies. This could potentially lead to an increase in the number of AI apps on the market, offering a wider range of solutions for end-users and expanding the possibilities of AI.

Copyright Shield

Another major update is the OpenAI Copyright Shield, a legal protection tool designed to safeguard you from potential copyright issues when using AI technologies. This is particularly important if you’re developing AI bots or custom AI models that could unintentionally infringe on copyrighted material. This tool provides a safety net, allowing you to innovate without worrying about legal issues.

OpenAI GPTs customizable AI models

Another significant update is the introduction of General Purpose Transforms (GPTs). GPTs give you the ability to build customized versions of Chat GPT, complete with instructions, expanded knowledge, and actions. This means you can create AI assistants like Matbot 3000, tailored to specific needs and requirements. Additionally, you can use the Assistance API to create AI assistants, and the persistent threads feature to maintain conversation history, enhancing the user experience and making your AI applications more user-friendly.

GPT-4 upgrades

OpenAI APIs

The Assistance API is another noteworthy feature. It aids in the creation of chatbots, providing a way to automate conversations. This is particularly useful in customer service, where chatbots can handle routine inquiries, freeing up human agents to deal with more complex issues.

– GPT Vision API is a powerful tool for image analysis, providing detailed insights into image content. This is particularly useful in fields such as surveillance, medical imaging, and content moderation, where accurate image analysis is crucial.

– DallE 3 API is designed for image generation. It can create images based on user prompts, making it a valuable tool for graphic designers, artists, and anyone needing to quickly and efficiently generate images. This API can greatly reduce your workload, allowing you to focus on the creative aspects of your work, thereby increasing productivity.

– Text to Speech API is another key feature. This tool converts written text into spoken words, providing a way to create audio content from written material. This is especially useful for creating audiobooks, podcasts, or any other form of audio content. The API supports a variety of languages and voices, allowing you to customize the output to meet your specific needs.

For developers, these advancements mean you now have the tools to build more complex and nuanced AI applications. For instance, the JSON mode feature enables you to generate valid JSON responses, while the reproducible outputs feature ensures consistent model outputs, enhancing the reliability of your AI applications.

Everything announced by OpenAI

GPT Store

OpenAI also launched the GPT Store, a marketplace specifically for GPT models. This platform functions similarly to an app store, allowing you to sell your GPTs and providing a platform for monetizing your AI developments. This could potentially lead to an increase in the number of AI solutions available on the market, offering a wider range of options for end-users and fostering a more competitive AI landscape.

However, these updates could also have implications for existing SaaS startups. OpenAI appears to be incorporating many features that were previously offered by third-party tools. This could potentially make some existing SaaS startups obsolete, as developers might prefer to use OpenAI’s integrated tools instead, leading to a shift in the SaaS landscape.

OpenAI’s Dev Day announcements represent significant advancements in AI development. The introduction of ChatGPT-4 Turbo, the Copyright Shield, the reduction in pricing, the launch of GPTs, and the GPT Store, as well as the Assistance API and persistent threads, are all expected to make AI app development more accessible and affordable. However, these updates could also disrupt the existing landscape of SaaS startups. As a developer, it’s crucial to stay updated with these advancements and consider how they might impact your work, shaping your strategies and decisions in the rapidly evolving world of AI.

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How to automate fine tuning ChatGPT 3.5 Turbo

How to automate fine tuning ChatGPT 3.5 Turbo

The advent of AI and machine learning has transform the wide variety of different areas, including the field of natural language processing. One of the most significant advancements in this area is the development and release of ChatGPT 3.5 Turbo, a language model developed by OpenAI. In this guide will delve into the process of automating the fine-tuning of GPT 3.5 Turbo for function calling using Python, with a particular focus on the use of the Llama Index.

OpenAI has announced the availability of fine-tuning for its GPT-3.5 Turbo model back in August 2023, with support for GPT-4 expected to be released this fall. This new feature allows developers to customize language models to better suit their specific needs, offering enhanced performance and functionality. Notably, early tests have shown that a fine-tuned version of GPT-3.5 Turbo can match or even outperform the base GPT-4 model in specialized tasks. In terms of data privacy, OpenAI ensures that all data sent to and from the fine-tuning API remains the property of the customer. This means that the data is not used by OpenAI or any other organization to train other models.

One of the key advantages of fine-tuning is improved steerability. Developers can make the model follow specific instructions more effectively. For example, the model can be fine-tuned to always respond in a particular language, such as German, when prompted to do so. Another benefit is the consistency in output formatting, which is essential for applications that require a specific response format, like code completion or generating API calls. Developers can fine-tune the model to reliably generate high-quality JSON snippets based on user prompts.

How to automate fine tuning ChatGPT

The automation of fine-tuning GPT 3.5 Turbo involves a series of steps, starting with the generation of data classes and examples. This process is tailored to the user’s specific use case, ensuring that the resulting function description and fine-tuned model are fit for purpose. The generation of data classes and examples is facilitated by a Python file, which forms the first part of a six-file sequence.

Fine-tuning also allows for greater customization in terms of the tone of the model’s output, enabling it to better align with a business’s unique brand identity. In addition to these performance improvements, fine-tuning also brings efficiency gains. For instance, businesses can reduce the size of their prompts without losing out on performance. The fine-tuned GPT-3.5 Turbo models can handle up to 4k tokens, which is double the capacity of previous fine-tuned models. This increased capacity has the potential to significantly speed up API calls and reduce costs.

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The second file in the sequence leverages the Llama Index, a powerful tool that automates several processes. The Llama Index generates a fine-tuning dataset based on the list produced by the first file. This dataset is crucial for the subsequent fine-tuning of the GPT 3.5 Turbo model. The next step in the sequence extracts the function definition from the generated examples. This step is vital for making calls to the fine-tuned model. Without the function definition, the model would not be able to process queries effectively.

The process then again utilizes the Llama Index, this time to fine-tune the GPT 3.5 Turbo model using the generated dataset. The fine-tuning process can be monitored from the Python development environment or from the OpenAI Playground, providing users with flexibility and control over the process.

Fine tuning ChatGPT 3.5 Turbo

Once the model has been fine-tuned, it can be used to make regular calls to GPT-4, provided the function definition is included in the call. This capability allows the model to be used in a wide range of applications, from answering complex queries to generating human-like text.

The code files for this project are available on the presenter’s Patreon page, providing users with the resources they need to automate the fine-tuning of GPT 3.5 Turbo for their specific use cases. The presenter’s website also offers a wealth of information, with a comprehensive library of videos that can be browsed and searched for additional guidance.

Fine-tuning is most effective when integrated with other techniques such as prompt engineering, information retrieval, and function calling. OpenAI has also indicated that it will extend support for fine-tuning with function calling and a 16k-token version of GPT-3.5 Turbo later this fall. Overall, the fine-tuning update for GPT-3.5 Turbo offers a versatile and robust set of features for developers seeking to tailor the model for specialized tasks. With the upcoming capability to fine-tune GPT-4 models, the scope for creating highly customized and efficient language models is set to expand even further.

The automation of fine-tuning GPT 3.5 Turbo for function calling using Python and the Llama Index is a complex but achievable process. By generating data classes and examples tailored to the user’s use case, leveraging the Llama Index to automate processes, and carefully extracting function definitions, users can create a fine-tuned model capable of making regular calls to GPT-4. This process, while intricate, offers significant benefits, enabling users to harness the power of GPT 3.5 Turbo for a wide range of applications.

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