Synthetic datasets for word scramble and arithmetic tasks described in the GPT3 paper.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('gpt3', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Energy consumption of artificial intelligence (AI) models in training is considerable, with both GPT-3, the original release of the current iteration of OpenAI's popular ChatGPT, and Gopher consuming well over **********-megawatt hours of energy simply for training. As this is only for the training model it is likely that the energy consumption for the entire usage and lifetime of GPT-3 and other large language models (LLMs) is significantly higher. The largest consumer of energy, GPT-3, consumed roughly the equivalent of *** Germans in 2022. While not a staggering amount, it is a considerable use of energy. Energy savings through AI While it is undoubtedly true that training LLMs takes a considerable amount of energy, the energy savings are also likely to be substantial. Any AI model that improves processes by minute numbers might save hours on shipment, liters of fuel, or dozens of computations. Each one of these uses energy as well and the sum of energy saved through a LLM might vastly outperform its energy cost. A good example is mobile phone operators, of which a ***** expect that AI might reduce power consumption by *** to ******* percent. Considering that much of the world uses mobile phones this would be a considerable energy saver. Emissions are considerable The amount of CO2 emissions from training LLMs is also considerable, with GPT-3 producing nearly *** tonnes of CO2. This again could be radically changed based on the types of energy production creating the emissions. Most data center operators for instance would prefer to have nuclear energy play a key role, a significantly low-emission energy producer.

Nemotron-3-8B-Base-4k Model Overview License

The use of this model is governed by the NVIDIA AI Foundation Models Community License Agreement. Description

Nemotron-3-8B-Base-4k is a large language foundation model for enterprises to build custom LLMs. This foundation model has 8 billion parameters, and supports a context length of 4,096 tokens. Nemotron-3-8B-Base-4k is part of Nemotron-3, which is a family of enterprise ready generative text models compatible with NVIDIA NeMo Framework. For other models in this collection, see the collections page.

NVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training and inferencing frameworks, guardrailing toolkits, data curation tools, and pretrained models, offering enterprises an easy, cost-effective, and fast way to adopt generative AI. To get access to NeMo Framework, please sign up at this link. References

Announcement Blog Model Architecture

Architecture Type: Transformer

Network Architecture: Generative Pre-Trained Transformer (GPT-3) Software Integration

Runtime Engine(s): NVIDIA AI Enterprise

Toolkit: NeMo Framework

To get access to NeMo Framework, please sign up at this link. See NeMo inference container documentation for details on how to setup and deploy an inference server with NeMo.

Sample Inference Code:

from nemo.deploy import NemoQuery

In this case, we run inference on the same machine

nq = NemoQuery(url="localhost:8000", model_name="Nemotron-3-8B-4K")

output = nq.query_llm(prompts=["The meaning of life is"], max_output_token=200, top_k=1, top_p=0.0, temperature=0.1) print(output)

Supported Hardware:

H100
A100 80GB, A100 40GB

Model Version(s)

Nemotron-3-8B-base-4k-BF16-1 Dataset & Training

The model uses a learning rate of 3e-4 with a warm-up period of 500M tokens and a cosine learning rate annealing schedule for 95% of the total training tokens. The decay stops at a minimum learning rate of 3e-5. The model is trained with a sequence length of 4096 and uses FlashAttention’s Multi-Head Attention implementation. 1,024 A100s were used for 19 days to train the model.

NVIDIA models are trained on a diverse set of public and proprietary datasets. This model was trained on a dataset containing 3.8 Trillion tokens of text. The dataset contains 53 different human languages (including English, German, Russian, Spanish, French, Japanese, Chinese, Italian, and Dutch) and 37 programming languages. The model also uses the training subsets of downstream academic benchmarks from sources like FLANv2, P3, and NaturalInstructions v2. NVIDIA is committed to the responsible development of large language models and conducts reviews of all datasets included in training. Evaluation Task Num-shot Score MMLU* 5 54.4 WinoGrande 0 70.9 Hellaswag 0 76.4 ARC Easy 0 72.9 TyDiQA-GoldP** 1 49.2 Lambada 0 70.6 WebQS 0 22.9 PiQA 0 80.4 GSM8K 8-shot w/ maj@8 39.4

• The calculation of MMLU follows the original implementation. See Hugging Face’s explanation of different implementations of MMLU.

** The languages used are Arabic, Bangla, Finnish, Indonesian, Korean, Russian and Swahili. Intended use

This is a completion model. For best performance, users are encouraged to customize the completion model using NeMo Framework suite of customization tools including Parameter-Efficient Fine-Tuning (P-tuning, Adapters, LoRA), and SFT/RLHF. For chat use cases, please consider using Nemotron-3-8B chat variants. Ethical use

Technology can have a profound impact on people and the world, and NVIDIA is committed to enabling trust and transparency in AI development. NVIDIA encourages users to adopt principles of AI ethics and trustworthiness to guide your business decisions by following the guidelines in the NVIDIA AI Foundation Models Community License Agreement. Limitations

The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts.
The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.

"gpt3.5-gpt4-input-output-echram.zip" :

Input and output to GPT-3.5 and GPT-4 based on ECHR dataset published in JSON format in this paper for argument component classification only i.e. clauses that are argumentative (conclusion/premise), extracted from the JSON file

Note: Output of the model is under OpenAI Terms & policies.

Please cite our paper also if you use this dataset: Performance analysis of large language models in the domain of legal argument mining

You can click here for BibTex or copy the text below.

@ARTICLE{10.3389/frai.2023.1278796,

AUTHOR={Al Zubaer, Abdullah and Granitzer, Michael and Mitrović, Jelena },

TITLE={Performance analysis of large language models in the domain of legal argument mining},

JOURNAL={Frontiers in Artificial Intelligence},

VOLUME={6},

YEAR={2023},

URL={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796},

DOI={10.3389/frai.2023.1278796},

ISSN={2624-8212},

ABSTRACT={Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.}}

LLM Dataset - Prompts and Generated Texts

The dataset contains prompts and texts generated by the Large Language Models (LLMs) in 32 different languages. The prompts are short sentences or phrases for the model to generate text. The texts generated by the LLM are responses to these prompts and can vary in length and complexity.

Researchers and developers can use this dataset to train and fine-tune their own language models for multilingual applications. The dataset provides a rich and diverse collection of outputs from the model, demonstrating its ability to generate coherent and contextually relevant text in multiple languages.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset - Full dataset

Models used for text generation:

• GPT-3.5,
• GPT-4

Languages in the dataset:

Arabic, Azerbaijani, Catalan, Chinese, Czech, Danish, German, Greek, English, Esperanto, Spanish, Persian, Finnish, French, Irish, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Malayalam, Maratham, Netherlands, Polish, Portuguese, Portuguese (Brazil), Slovak, Swedish, Thai, Turkish, Ukrainian

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ff60c93f09ec82a765aa39678e4aa0a58%2Fsnapedit_1709731090855.jpeg?generation=1709738798916444&alt=media" alt="">

🧩 This is just an example of the data. Leave a request here to learn more

Content

CSV File includes the following data: - from_language: language the prompt is made in, - model: type of the model (GPT-3.5, GPT-4 and Uncensored GPT Version), - time: time when the answer was generated, - text: user prompt, - response: response generated by the model

🚀 You can learn more about our high-quality unique datasets here

keywords: dataset, machine learning, natural language processing, artificial intelligence, deep learning, neural networks, text generation, language models, openai, gpt-3, data science, predictive modeling, sentiment analysis, keyword extraction, text classification, sequence-to-sequence models, attention mechanisms, transformer architecture, word embeddings, glove embeddings, chatbots, question answering, language understanding, text mining, information retrieval, data preprocessing, feature engineering, explainable ai, model deployment

Dive into the future of education with the Deep Learning Tutor Dataset – a pioneering resource designed to empower the creation of sophisticated, adaptive AI tutors. This dataset is meticulously curated to facilitate the fine-tuning of advanced large language models like GPT-4o, enabling them to internalize specialized pedagogical conversation patterns and expert teaching methodologies.

This collection represents a significant step towards developing intelligent educational systems that can truly adapt to individual student needs, provide nuanced feedback, and foster deeper understanding. By leveraging the power of deep learning and state-of-the-art LLMs, this dataset paves the way for a new generation of personalized learning experiences.

Key Features & Contents:

• Specialized Pedagogical Conversation Data: An extensive collection of educational dialogue, carefully structured to represent effective tutoring interactions. This includes examples of:
  • Expert Explanations: Clear, concise, and multi-faceted explanations of complex concepts.
  • Adaptive Feedback: Responses tailored to student understanding levels, common misconceptions, and learning styles.
  • Guided Inquiry: Dialogue patterns that encourage critical thinking and problem-solving.
  • Conceptual Clarification: Interactions focused on identifying and addressing misunderstandings.
  • Motivational Prompts: Examples of how to engage and encourage learners.
• Structured for Fine-tuning GPT-4o: The dataset is provided in a format optimized for fine-tuning OpenAI's GPT-4o, allowing the model to go beyond general knowledge and adopt a truly pedagogical persona.
• Foundational for Adaptive Tutoring Systems: This data is the bedrock for training AI systems that can dynamically adjust their teaching approach based on student performance, engagement, and learning progress.

Applications:

• Building Next-Generation AI Tutors: Develop intelligent tutors capable of empathetic, effective, and adaptive teaching.
• Research in AI in Education (AIEd): A valuable resource for researchers exploring the application of LLMs in educational contexts, dialogue systems, and personalized learning.
• Enhancing E-Learning Platforms: Integrate AI-driven tutoring capabilities into existing or new online learning environments.
• Developing Conversational AI for Learning: Train models to understand and generate educational dialogues that mimic expert human tutors.
• Personalized Learning Initiatives: Contribute to systems that offer highly individualized learning paths and support.

How to Leverage This Dataset: Fine-tuning Your AI Tutor

The primary utility of this dataset is to fine-tune a powerful LLM like GPT-4o, imbuing it with the specific conversational and pedagogical skills required for adaptive tutoring.

Prerequisites: * An OpenAI account with API access. * Familiarity with the OpenAI Platform and fine-tuning concepts.

Step 1: Download the Dataset Download the educational_conversation_data.jsonl file from this Kaggle dataset.

Step 2: Initiate GPT-4o Fine-tuning This process will train GPT-4o to emulate the expert teaching methodologies embedded within the dataset. 1. Upload Data: Navigate to the "Fine-tuning" section in your OpenAI Platform. Upload the educational_conversation_data.jsonl file. 2. Create Fine-tuning Job: * Base Model: gpt-4o (or gpt-4o-mini for more cost-effective experimentation). * Epochs: 3 (A common starting point; adjust based on dataset size and desired performance). * Learning Rate Multiplier: 2 (A good initial value; can be tuned). * Batch Size: 1 (Often effective for pedagogical data, but can be adjusted). * Note: These parameters are recommendations. Experimentation may be required to achieve optimal results for your specific application. 3. Start Job: Initiate the fine-tuning process. Once complete, you will receive a new custom model ID, representing your fine-tuned pedagogical AI.

Step 3: Integrate Your Fine-tuned Model The fine-tuned model ID can now be used with OpenAI's API to power your adaptive AI tutor. You can integrate it into: * A custom chat interface. * An existing educational platform. * A research prototype for conversational AI in education.

Files in This Dataset:

• educational_conversation_data.jsonl: The core dataset containing the specialized pedagogical conversation patterns and expert teaching methodologies, formatted for OpenAI fine-tuning.
• README.md: (Optional, but good practice) A brief overview of the dataset and usage.

The AI content detection market is experiencing rapid growth, driven by the proliferation of AI-generated content and increasing concerns regarding plagiarism, academic dishonesty, and misinformation. The market, estimated at $250 million in 2025, is projected to experience a robust Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033, reaching approximately $1.5 billion by 2033. This expansion is fueled by several key factors. Firstly, the rise of sophisticated AI writing tools like GPT-3 and others necessitates the development of equally advanced detection mechanisms. Secondly, educational institutions, news organizations, and businesses are increasingly adopting AI detection tools to ensure the authenticity and originality of content, fostering trust and maintaining academic integrity. Thirdly, evolving regulatory landscapes are pushing for greater transparency and accountability regarding AI-generated content, further stimulating market demand. The market segmentation reveals a strong emphasis on text content detection, which currently dominates, but the image and video content detection segments are showing significant growth potential as AI-generated media become more prevalent. The market’s growth is not without challenges. The evolving nature of AI algorithms, coupled with the potential for adversarial attacks aimed at circumventing detection, represents a key restraint. Furthermore, the accuracy and reliability of detection tools remain crucial concerns, requiring continuous improvement in algorithms and training data. Competitive landscape is also intensifying as numerous companies are entering this space, leading to price competition and a focus on differentiating features. Nevertheless, the overall trend points towards significant market expansion as AI content generation continues its rapid evolution and the need for robust detection mechanisms increases across various sectors. North America currently holds a significant market share, owing to early adoption and strong regulatory frameworks, but the Asia Pacific region is anticipated to witness the fastest growth in the coming years due to increasing digital literacy and technological advancements.

I created this dataset using gpt-3.5turbo.

This dataset is a completely brand new and improved iteration of the dataset I released earlier (the 6.5k example one).

There is no overlap between the data contained in this dataset and any other data I shared earlier -- all the examples are brand new.

I created this dataset because I noticed that I didn't have enough data -- as I kept adding examples, the model continued to improve!

There are several improvements that went into the creation of this dataset, most prominently the length and quality of the excerpt that I used to prompt gpt-3.5turbo.

If you find this dataset useful, please leave an upvote! 😊 Thank you! 🙏🙏🙏

GPT-3 is the most energy-intensive AI program trained in 2024, with over **** megawatt hours consumed to train the model. Produced in 2020, the model ended up being far more energy intensive than models produced in 2023, most of which were under *** MWh.

This dataset is a combination of Stanford's Alpaca (https://github.com/tatsu-lab/stanford_alpaca) and FiQA (https://sites.google.com/view/fiqa/) with another 1.3k pairs custom generated using GPT3.5 Script for tuning through Kaggle's (https://www.kaggle.com) free resources using PEFT/LoRa: https://www.kaggle.com/code/gbhacker23/wealth-alpaca-lora GitHub repo with performance analyses, training and data generation scripts, and inference notebooks: https://github.com/gaurangbharti1/wealth-alpaca… See the full description on the dataset page: https://huggingface.co/datasets/gbharti/finance-alpaca.

Introduction

This repository holds the data file for translating TechLinked, which talks about mostly technology and science news. Raw data is in the data/ folder. Scripts generate OpenAI's ChatCompletion Fine-tuning API formatted training data in jsonl format. -2000 variants are designed to be used with GPT-3 with 8192 tokens context length limit. -8192 variants are designed to be used with GPT-4o mini with 128000 context window and 16384 max output tokens.

  How to add… See the full description on the dataset page: https://huggingface.co/datasets/metricv/metricsubs-chunktranslate.