Dataset Card for uplimit-instruction-tuning-dataset

This dataset has been created with distilabel.

Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:

distilabel pipeline run --config "https://huggingface.co/datasets/looking4daf/uplimit-instruction-tuning-dataset/raw/main/pipeline.yaml"

or explore the configuration:

distilabel pipeline info --config "https://huggingface.co/datasets/looking4daf/uplimit-instruction-tuning-dataset/raw/main/pipeline.yaml"

Dataset structure

The examples have the following structure per configuration:

Configuration: default

---

{
    "distilabel_metadata": {
        "raw_input_text_generation_1": [
            {
                "content": "Based on the information provided in the prompt, \"Uplimit\" is an unknown entity or concept that requires further investigation and understanding. Here\u0027s a potential long question:\n\n\"What is the meaning and significance of \u0027Uplimit\u0027, and how does it relate to various fields such as technology, finance, education, or social sciences? Can you provide examples of instances where the term \u0027Uplimit\u0027 has been used in different contexts, and what insights can be derived from these applications? Additionally, could you explore the theoretical foundations behind the concept of \u0027Uplimit\u0027, including any relevant theories, models,",
                "role": "user"
            }
        ],
        "raw_output_text_generation_1": "The term \"Uplimit\" appears to be an unknown entity or concept that may require further investigation and understanding. Based on the limited information available, here\u0027s a potential long answer:\n\nThe term \"Uplimit\" seems to refer to a hypothetical limit beyond which something cannot go higher or exceed certain boundaries. However, its exact meaning and significance remain unclear without more context. The use of this term might suggest that there exists a maximum capacity or ceiling for achieving a particular goal, objective, or performance level. It could also imply that there are constraints or limitations that prevent individuals or systems from reaching their full potential.\n",
        "statistics_text_generation_1": {
            "input_tokens": 128,
            "output_tokens": 128
        }
    },
    "instruction": "Based on the information provided in the prompt, \"Uplimit\" is an unknown entity or concept that requires further investigation and understanding. Here\u0027s a potential long question:\n\n\"What is the meaning and significance of \u0027Uplimit\u0027, and how does it relate to various fields such as technology, finance, education, or social sciences? Can you provide examples of instances where the term \u0027Uplimit\u0027 has been used in different contexts, and what insights can be derived from these applications? Additionally, could you explore the theoretical foundations behind the concept of \u0027Uplimit\u0027, including any relevant theories, models,",
    "model_name": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    "response": "The term \"Uplimit\" appears to be an unknown entity or concept that may require further investigation and understanding. Based on the limited information available, here\u0027s a potential long answer:\n\nThe term \"Uplimit\" seems to refer to a hypothetical limit beyond which something cannot go higher or exceed certain boundaries. However, its exact meaning and significance remain unclear without more context. The use of this term might suggest that there exists a maximum capacity or ceiling for achieving a particular goal, objective, or performance level. It could also imply that there are constraints or limitations that prevent individuals or systems from reaching their full potential.\n"
}

This subset can be loaded as:

from datasets import load_dataset

ds = load_dataset("looking4daf/uplimit-instruction-tuning-dataset", "default")

Or simply as it follows, since there's only one configuration and is named default:

from datasets import load_dataset

ds = load_dataset("looking4daf/uplimit-instruction-tuning-dataset")