In 2023, Claude 3 Opus was the large language model (LLM) tool that had the largest average worldwide, with an average total of ***** percent. Close behind, in second place, was Gemini 1.5 Pro with an average of about ** percent.

🤖 We curated this dataset for NeurIPS Large Language Model Efficiency Challenge: 1 LLM + 1GPU + 1Day. 🚀 Our Birbal-7B-V1 fine-tuned on this dataset achieved 🏆 first rank 🏆 in the competition.

Here is high-level diagram of our data preparation strategy:

  Natural Instructions Dataset Preparation

Natural Instructionsdataset is a community effort to create a large collection of tasks and their natural language definitions/instructions. As show in above diagram, we sample from… See the full description on the dataset page: https://huggingface.co/datasets/upaya07/NeurIPS-LLM-data.

The value of the large language model (LLM) market in Japan was projected to reach ** billion Japanese yen in fiscal year 2024. Partly based on the assumption that the market would diversify with the release of specialized and cheaper LLMs from fiscal year 2025 onward, the market size was forecast to more than quadruple by fiscal year 2028.

namespace-Pt/long-llm-data dataset hosted on Hugging Face and contributed by the HF Datasets community

1. Image Description Data Data Size: 500 million pairs Image Type: generic scene(portrait, landscapes, animals,etc), human action, picture book, magazine, PPT&chart, App screenshot, and etc. Resolution: 4K+ Description Language: English, Spanish, Portuguese, French, Korean, German, Chinese, Japanese Description Length: text length is no less than 250 words Format: the image format is .jpg, the annotation format is .json, and the description format is .txt

2. Video Description Data Data Size: 10 million pairs Image Type: generic scene(portrait, landscapes, animals,etc), ads, TV sports, documentaries Resolution: 1080p+ Description Language: English, Spanish, Portuguese, French, Korean, German, Chinese, Japanese Description Length: text length is no less than 250 words Format: .mp4,.mov,.avi and other common formats;.xlsx (annotation file format)

3. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/llm?source=Datarade

Large Language Model (LLM) Market Outlook

The global market size for Large Language Models (LLMs) was valued at approximately USD 2.3 billion in 2023 and is projected to reach an astounding USD 15.8 billion by 2032, growing at a robust Compound Annual Growth Rate (CAGR) of 23.5%. The exponential growth of this market can be attributed to the increasing demand for AI-driven solutions across various sectors including healthcare, finance, and retail, among others. The rising adoption of natural language processing (NLP) technologies and advancements in machine learning algorithms are key factors driving this market.

One of the major growth factors for the LLM market is the rapid development and adoption of artificial intelligence (AI) and machine learning technologies. The expanding capabilities of LLMs in understanding and generating human-like text have opened up new avenues for their application. This has led to increased investments in AI research and development, further propelling the advancements in LLM technologies. Moreover, the integration of LLMs with other advanced technologies such as cloud computing, big data, and IoT is enhancing their functionality and expanding their applicability across different sectors.

Another crucial growth driver is the growing demand for automated customer service solutions. Businesses are increasingly deploying LLMs to improve customer engagement and satisfaction by providing instant, accurate, and personalized responses to customer queries. The ability of LLMs to understand and process natural language inputs makes them ideal for applications in chatbots, virtual assistants, and other automated customer service tools. This not only enhances customer experience but also significantly reduces operational costs for businesses by minimizing the need for human intervention.

The healthcare sector is also witnessing a significant impact from the adoption of LLMs. These models are being utilized for various applications such as patient data management, diagnostics, and personalized medicine. The ability of LLMs to analyze large volumes of unstructured data and extract meaningful insights is revolutionizing the way healthcare providers deliver services. This is leading to improved patient outcomes, reduced medical errors, and more efficient healthcare delivery systems. Additionally, the ongoing advancements in AI technologies are expected to further enhance the capabilities of LLMs, driving their adoption in the healthcare sector.

Regionally, North America is anticipated to dominate the LLM market, owing to the presence of major AI and technology companies, along with significant investments in AI research and development. The region's well-established IT infrastructure and high adoption rate of advanced technologies are further contributing to this growth. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by increasing digital transformation initiatives, rising investments in AI technology, and growing awareness about the benefits of LLMs in various applications.

Component Analysis

The LLM market can be segmented by component into software, hardware, and services. The software segment holds the largest share in the market, driven by the increasing demand for advanced AI software solutions that can leverage LLM capabilities. With the continuous advancements in machine learning algorithms and NLP technologies, the software segment is expected to maintain its dominance. Software solutions that incorporate LLMs are being used across various applications, from content generation to real-time language translation, making them indispensable tools for businesses and consumers alike.

The hardware segment is also experiencing significant growth, as the deployment of LLMs requires substantial computational power. High-performance computing hardware, including Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), are essential for training and deploying LLMs. The increasing demand for powerful hardware solutions to support the computational requirements of LLMs is driving investments in this segment. Moreover, technological advancements in hardware components are enhancing the efficiency and performance of LLMs, further fueling their adoption.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. As businesses increasingly adopt LLMs, the demand for specialized services to support the deployment and integration of these models is growing. Consulting services are

OpenAI remains the most heavily funded large language model (LLM) developer worldwide in 2023. Its most well known product, ChatGPT, launched something of a generative AI revolution in late 2022. Its backing by Microsoft has made OpenAI a leading champion in the LLM competition, though competitors like Anthropic, funded by Google, are closing the gap.

As of March 2024, OpenAI o1 was the large language model (LLM) tool that had the best benchmark score in solving math problems, with a score of 94.8 percent. Close behind, in second place, was OpenAI o1-mini, followed by GPT-4o.

Comprehensive training data on 1M+ stores across the US & Canada. Includes detailed menus, inventory, pricing, and availability. Ideal for AI/ML models, powering retrieval-augmented generation, search, and personalization systems.

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.

Note the LLM used in these scripts will be discontinued in July 2024. For updated versions of the scripts wrapped into an R package see https://github.com/agougher/geminus/datForReview - contains abstracts analyzed by LLMpalmExtract-forSubmission - R code for interacting with LLM

Large Language Model (LLM) evaluation is currently one of the most important areas of research, with existing benchmarks proving to be insufficient and not completely representative of LLMs' various capabilities. We present a curated collection of challenging statements on sensitive topics for LLM benchmarking called TruthEval. These statements were curated by hand and contain known truth values. The categories were chosen to distinguish LLMs' abilities from their stochastic nature. Details of collection method and use cases can be found in this paper: TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability

DeepSeek Model Dataset

Overview

This dataset contains the DeepSeek model, a [brief description of the model, e.g., "state-of-the-art language model for natural language processing tasks"]. The model is designed for [specific use cases, e.g., "text generation, sentiment analysis, etc."].

Contents

• model_weights/: Directory containing the model weights.
• config.json: Configuration file for the model.
• inference_example.ipynb: Jupyter Notebook demonstrating how to load and use the model.
• requirements.txt: List of Python dependencies.

Usage

1. Download the dataset from Kaggle.
2. Install the required dependencies using pip install -r requirements.txt.
3. Open the inference_example.ipynb notebook to see how to load the model and perform inference.

License

This dataset is licensed under [license name, e.g., "MIT License"]. See the LICENSE file for more details.

Acknowledgments

• Deepseek

Contact

For questions or issues, please contact

EverythingLM Dataset

EverythingLM is a diverse instruct dataset consisting of ~1k sets of system prompts, instructions, and corresponding responses. These sets were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions.

  Categories:

Reasoning Creative Writing General Knowledge Brainstorming Search Query Coding Basic Instruct

We also leverage various system prompts for evol-instruct and for responding… See the full description on the dataset page: https://huggingface.co/datasets/totally-not-an-llm/EverythingLM-data.

As of 2024, at least one fifth of respondents working in healthcare organizations reported that they used large language models for answering patient questions and medical chatbots. Furthermore, ** percent of healthcare organizations used LLMs for biomedical research.

As of 2024, over **** the global firms planned to use LLMs (LLama and LLama-like models), while ** percent chose to use embedding models (BERT and family) in their commercial deployment. Additionally, only ***** percent planned to utilize multi-modal models.

The Data Annotation Tools Market size was valued at USD 1.31 billion in 2023 and is projected to reach USD 6.72 billion by 2032, exhibiting a CAGR of 26.3 % during the forecasts period. The data annotation tools market is concerned with software applications that are used to tag as well as sort data for machine learning and artificial intelligence industries. They assist in development of training sets by tagging images, text, voice and video by relevant data and information. Some of the API’s that use reinforcement learning include training models for computer vision, natural language processing and speech recognition. Currently, tendencies in the market refer to the use of automated and semiautomated techniques for the process of annotation. Also, there is a rise in the demand for annotation tool with support for various form of data and support for AI marketing platforms. The application of AI and machine learning solutions in several industries is boosting the demand hence continues to propel the growth and competition in the market. Recent developments include: In November 2023, Appen Limited, a high-quality data provider for the AI lifecycle, chose Amazon Web Services (AWS) as its primary cloud for AI solutions and innovation. As Appen utilizes additional enterprise solutions for AI data source, annotation, and model validation, the firms are expanding their collaboration with a multi-year deal. Appen is strengthening its AI data platform, which serves as the bridge between people and AI, by integrating cutting-edge AWS services. , In September 2023, Labelbox launched Large Language Model (LLM) solution to assist organizations in innovating with generative AI and deepen the partnership with Google Cloud. With the introduction of large language models (LLMs), enterprises now have a plethora of chances to generate new competitive advantages and commercial value. LLM systems have the ability to revolutionize a wide range of intelligent applications; nevertheless, in many cases, organizations will need to adjust or finetune LLMs in order to align with human preferences. Labelbox, as part of an expanded cooperation, is leveraging Google Cloud's generative AI capabilities to assist organizations in developing LLM solutions with Vertex AI. Labelbox's AI platform will be integrated with Google Cloud's leading AI and Data Cloud tools, including Vertex AI and Google Cloud's Model Garden repository, allowing ML teams to access cutting-edge machine learning (ML) models for vision and natural language processing (NLP) and automate key workflows. , In March 2023, has released the most recent version of Enlitic Curie, a platform aimed at improving radiology department workflow. This platform includes Curie|ENDEX, which uses natural language processing and computer vision to analyze and process medical images, and Curie|ENCOG, which uses artificial intelligence to detect and protect medical images in Health Information Security. , In November 2022, Appen Limited, a global leader in data for the AI Lifecycle, announced its partnership with CLEAR Global, a nonprofit organization dedicated to ensuring access to essential information and amplifying voices across languages. This collaboration aims to develop a speech-based healthcare FAQ bot tailored for Sheng, a Nairobi slang language. .

Silencio provides the world’s largest real-world street and venue noise-level dataset, combining over 35 billion datapoints with AI-powered interpolation. Fully anonymized, user-consented, and ready for AI training, urban analysis, and mobility insights. Available in raw format.

We offer comprehensive data collection services that cater to a wide range of industries and applications. Whether you require image, audio, or text data, we have the expertise and resources to collect and deliver high-quality data that meets your specific requirements. Our data collection methods include manual collection, web scraping, and other automated techniques that ensure accuracy and completeness of data.

Our team of experienced data collectors and quality assurance professionals ensure that the data is collected and processed according to the highest standards of quality. We also take great care to ensure that the data we collect is relevant and applicable to your use case. This means that you can rely on us to provide you with clean and useful data that can be used to train machine learning models, improve business processes, or conduct research.

We are committed to delivering data in the format that you require. Whether you need raw data or a processed dataset, we can deliver the data in your preferred format, including CSV, JSON, or XML. We understand that every project is unique, and we work closely with our clients to ensure that we deliver the data that meets their specific needs. So if you need reliable data collection services for your next project, look no further than us.