You can use a tuner for all musical instruments. Below is a list of common instruments and their tuning. The notes are written from lowest to highest, except for the ukulele and banjo that don't have strings ordered by pitch.
Notice the list above only shows the most common tuning for each instrument. In rare cases, other tunings are used. The indicated guitar tuning applies to classical guitar, steel-string acoustic guitar, and electric guitar.
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Like everyone else yesterday, I followed OpenAI dev day & heard that a new feature allowing to create custom GPTs was about to be released. I took a look at the different articles & docs related to this new feature but did not find much information.
The one misleading difference between fine-tuning and GPTs or other agents/bots/AI (they can be called in a multitude of ways) is that in both case you give it new data. It is really in the way the data is used by the AI that makes the big difference. In the first case the AI is modified in its core, while in the second case it is really about providing instructions to guide the existing AI (without modifying the core).
Why do you want to use one or the other ? Context & cost. Both methods aim to specialize an AI but the outcomes are different. Fine-tuning is more complex and expensive, you need new quality data that will lead to consolidation of knowledge for a tiny part the AI systems - you literally improve the system in a very incremental way (if done correctly). While the custom GPTs approach is much less expensive and more accessible (no-code/low-code - everybody can do it). You do not improve the system but you activate the proper part of the AI brain to get the best out of it.
Personal thoughts & considerations - customs GPT to me appear as a knowledgeable librarian. I can give it specific documents if I want to and I can even instruct it to only respond using these documents. That can be amazing to create specialized assistant using a specific book, framework, knowledge base - whatever you think of that the AI can understand. That is even better when you consider how easy it is to update them, just change the instructions or the documents. Eventually, when you have improved enough a GPT and that the nature of your use-case is relevant (not an info changing frequently - fine-tuning is not an update), it might be considered for fine-tuning.
Thank you for getting back. Actually i have lot of credits for the normal plan but I realize API credits is what I need because I am interested in fine tuning. Can I convert my existing credits into API credits?
If your intended use is to have an AI answer about proprietary knowledge, fine-tune is usually not the best way to proceed anyway. AI model fine-tune can affect behaviors and quality of output, but it is not a quality way to instill new information or closed-domain answering capabilities.
The American Historical Association is coordinating a nationwide, faculty-led project to articulate the disciplinary core of historical study and to define what a student should understand and be able to do at the completion of a history degree program.
The updated map below shows institutions where faculty historians have been involved in the project. The blue locations were part of the first wave of AHA participants, beginning in 2012. The red locations joined the project in January 2015, as part of the second phase of implementation.
This project has brought together accomplished history faculty from a range of 2- and 4-year institutions across the country to define the core disciplinary elements of historical study and the goals of the undergraduate history major. Faculty participants have been working together to develop common language that communicates to a broad audience the significance and value of a history degree. We encourage you to read the 2016 History Discipline Core, which describes core competencies and student learning outcomes, and to learn about the history of the Tuning project.
As part of the AHA's ongoing efforts to support discussions of history curricula across institutions and educational levels, we are working with participants in the Tuning project to organize events around the country.
Faculty participants from history departments around the country have reviewed aspects of their home-department curricula. AHA is now able to offer examples of revised curricular materials from a broad range of institutions: rubrics, assignments, statements of course outcomes and degree requirements, survey questions for history majors or alumni, and other types of materials. If you're looking for ideas for your own department, check these out.
The 2016 "Tuning History in General Education Courses" session considered some of the big questions that the AHA Tuning project has raised about the history major and directed them instead to General Education and entry-level courses for non-majors. Whether we teach at the K-12, community college, or four year level, as history educators we face a common question: What is our purpose in history education? What do we want students to gain from the study of history? Chaired by Lendol Calder (Augustana Coll.), the session featured speakers Daniel J. McInerney (Utah State Univ.), Sarah Elizabeth Shurts (Bergen Community Coll.), and Louis Rodriquez (Kutztown Univ.).
The Tuning Project's History Discipline Core is a statement of the central habits of mind, skills, and understanding that students achieve when they major in history. The document reflects the iterative nature of the tuning process. The most recent version was published in November 2016.
What kind of learning should an introductory history course entail in the 21st century? How can introductory history courses support student learning and success across the curriculum? Since 2015, the AHA and local partners have held a two-day conference on college-level introductory history courses to address these and other questions.
I'm building a custom generative AI application using Vertex AI's Gemini API for my organization. The goal is to generate SQL or XML objects based on input.
I'm currently immersed in the exciting world of fine-tuning language models (LLMs) and I'm seeking guidance on the most effective strategies for evaluating, monitoring, and retraining Vertex AI's gemini API. As I delve deeper into this process, I've encountered various challenges and uncertainties, prompting me to reach out to this knowledgeable community for insights and advice. Fine-tuning Gemini API involves adapting a pre-trained model to a specific task or domain, allowing it to generate more relevant and contextually appropriate outputs. However, ensuring the effectiveness of this fine-tuning process requires careful evaluation and monitoring at every stage. I have fine-tuned the pre-trained model which contains multiple AI skills like text to SQL conversion, text to XML conversion, and product information. My question is about the best practice to create and manage the dataset for these multiple skills using a single Gemini model. After fine-tuning, generally, we evaluate and examine the model output. My question is around re-tuning the model again for the mistakes it is making with the previous dataset. For further fine-tuning, do we need to keep the dataset we used for the previous fine-tuning and append the new dataset or as the model is already trained with the previous dataset and we just need to keep the data for the mistakes it is making?
Moreover, How frequently should a model be retrained to maintain its relevance and accuracy? Are there specific triggers or indicators that signal the need for retraining, such as changes in data distribution or task requirements?
One aspect I'm particularly interested in is the evaluation criteria for assessing the performance of a Gemini AI model. What metrics or benchmarks should be considered to determine the quality of the model's outputs? Are there specific evaluation techniques that have proven to be particularly reliable or informative in this context?
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