size_categories:n<1K format:parquet modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars library:distilabel region:us synthetic distilabel rlaif
---
size_categories: n<1K
dataset_info:
  features:
  - name: text
    dtype: string
  - name: id
    dtype: string
  - name: dump
    dtype: string
  - name: url
    dtype: string
  - name: file_path
    dtype: string
  - name: language
    dtype: string
  - name: language_score
    dtype: float64
  - name: token_count
    dtype: int64
  - name: score
    dtype: float64
  - name: int_score
    dtype: int64
  - name: generated_system_prompt
    dtype: string
  - name: distilabel_metadata
    struct:
    - name: raw_output_chat_generation_2
      dtype: string
  - name: gen_conv_model_name
    dtype: string
  - name: system_prompt
    dtype: string
  - name: conversation
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: generations
    sequence: string
  - name: generations_model_names
    sequence: string
  splits:
  - name: train
    num_bytes: 1714804
    num_examples: 100
  download_size: 907734
  dataset_size: 1714804
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
tags:
- synthetic
- distilabel
- rlaif
---

<p align="left">
  <a href="https://github.com/argilla-io/distilabel">
    <img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
  </a>
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# Dataset Card for fine-preferences-magpie-generated-system-prompt-v1

This dataset has been created with [distilabel](https://distilabel.argilla.io/).



## 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:

```console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1/raw/main/pipeline.yaml"
```

or explore the configuration:

```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1/raw/main/pipeline.yaml"
```

## Dataset structure

The examples have the following structure per configuration:


<details><summary> Configuration: default </summary><hr>

```json
{
    "conversation": [
        {
            "content": "I\u0027m trying to understand the effects of Gamma Ray Bursts on the interstellar medium in spiral galaxies. Can you help me with that?",
            "role": "user"
        },
        {
            "content": "Gamma Ray Bursts (GRBs) are indeed fascinating events that can have a significant impact on the interstellar medium (ISM) in spiral galaxies. The energy released by a GRB can heat the local gas to extremely high temperatures, potentially above 10^7 K, which can lead to the formation of kpc-size shells and holes in the ISM.\n\nOne of the key factors in determining the effects of a GRB on the ISM is the amount of energy that is deposited into the surrounding gas. If a significant portion of the energy is absorbed by the ISM, it can lead to a rapid expansion of the gas, creating a bubble that can grow to sizes of several kiloparsecs.\n\nThe momentum acquired during this expansion phase can be substantial, allowing the bubble to \"snowplow\" through the surrounding ISM, even in the presence of disk blowout, which is a major cause of energy loss. This process can lead to the formation of large shells and holes in the ISM, which can be observed in spiral galaxies.\n\nIt\u0027s interesting to note that this mechanism differs from the standard model for the origin of such shells, which typically involves multiple supernovae explosions. The GRB-driven mechanism can potentially overcome some of the limitations of the supernova-driven model, such as radiative cooling, evaporative losses, and disk blowout.\n\nHave you considered exploring the observational evidence for giant shells in spiral galaxies, which may be indicative of GRB remnants? There are some intriguing cases where these shells lack obvious central star clusters, suggesting that they may be the result of ancient GRBs.",
            "role": "assistant"
        },
        {
            "content": "Yes, I have considered that. In fact, I\u0027ve been looking at some data on giant shells in spiral galaxies and I\u0027m trying to understand the energy requirements for creating such features. Can you help me with that?",
            "role": "user"
        },
        {
            "content": "The energy requirements for creating giant shells in spiral galaxies are indeed a crucial aspect of understanding the impact of GRBs on the ISM.\n\nFrom what I know, the energy required to create a kpc-size shell is on the order of 10^53 ergs. This is a tremendous amount of energy, equivalent to the explosive energy released by a GRB. In fact, it\u0027s been suggested that GRBs could be responsible for creating these giant shells, especially in cases where there is no obvious central star cluster.\n\nTo put this energy requirement into perspective, a typical supernova explosion releases around 10^51 ergs of energy, so we\u0027re talking about an energy release that\u0027s several orders of magnitude larger. This is why GRBs are such powerful events, capable of shaping the ISM on large scales.\n\nWhen it comes to simulating the formation of these giant shells, you\u0027ll need to consider the energy deposition rate, the density of the surrounding ISM, and the cooling mechanisms at play. It\u0027s a complex problem, but one that can be tackled using numerical simulations, such as hydrodynamic simulations or radiative transfer models.\n\nHave you considered using any specific simulation codes or tools to model the formation of these giant shells? I\u0027d be happy to provide some guidance on the types of simulations that might be suitable for this problem.",
            "role": "assistant"
        },
        {
            "content": "I\u0027ve been using a hydrodynamic simulation code to model the formation of these giant shells. However, I\u0027m having some trouble with the cooling mechanisms. Can you help me with that?",
            "role": "user"
        }
    ],
    "distilabel_metadata": {
        "raw_output_chat_generation_2": "Cooling mechanisms can be a challenging aspect of simulating the formation of giant shells! Hydrodynamic simulations are a great tool for modeling the dynamics of the gas, but accurately capturing the thermodynamic evolution of the gas can be tricky.\n\nThere are several cooling mechanisms that can come into play when modeling the formation of giant shells, including:\n\n1. Radiative cooling: This is the process by which hot gas loses energy through radiation. In the context of GRB-driven shells, radiative cooling can be important, especially at high temperatures (\u003e10^6 K).\n2. Adiabatic cooling: As the shell expands, the gas cools adiabatically due to the decrease in density. This process can be significant, especially at early times.\n3. Heat conduction: Heat can be conducted from the hot, shocked gas to the surrounding cooler gas, leading to cooling.\n4. Turbulent mixing: Turbulent motions can mix hot and cold gas, leading to cooling and the formation of a multiphase medium.\n\nTo accurately model these cooling mechanisms, you\u0027ll need to include a comprehensive cooling function in your simulation code. This function should account for the various cooling processes mentioned above, as well as any others that may be relevant (e.g., Compton cooling"
    },
    "dump": "CC-MAIN-2013-20",
    "file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2013-20/segments/1368696381249/warc/CC-MAIN-20130516092621-00000-ip-10-60-113-184.ec2.internal.warc.gz",
    "gen_conv_model_name": "meta-llama/Meta-Llama-3-70B-Instruct",
    "generated_system_prompt": "Here is a system prompt to guide the generation of a multi-turn conversation between a user and an AI assistant based on the provided document:\n\n```markdown\n### Investigating the Interstellar Medium and Gamma Ray Bursts\n\n## 1. Preparation\nThe user is an astrophysicist researching the effects of Gamma Ray Bursts (GRBs) on the interstellar medium (ISM) in spiral galaxies. The AI assistant has general knowledge of astrophysics and cosmology.\n\n## 2. Interaction Structure and Roles\nThe user will initiate the conversation with a complex task or question related to GRBs and their impact on the ISM. The AI assistant will respond with relevant information, explanations, or suggestions, and the conversation will evolve based on the user\u0027s subsequent requests or questions.\n\n## 3. User Task Characteristics\nThe user\u0027s goal is to explore the potential relationship between GRBs and the formation of kpc-size shells and holes in the ISM. They may ask for explanations of relevant astrophysical concepts, request help with data analysis or simulations, or seek information about the latest research in the field.\n\n## 4. AI Assistant Characteristics\nThe AI assistant should respond in a knowledgeable and authoritative tone, using technical terms and concepts relevant to the field of astrophysics. It should be able to provide explanations, offer suggestions, and engage in discussions about the user\u0027s research goals.\n\n## 5. Engagement\nThe conversation should be engaging and informative, with the AI assistant encouraging the user to ask follow-up questions and explore different aspects of the topic.\n\n## 6. General Guidelines\nThe conversation should not mention the original document or its content directly. The AI assistant should provide accurate and relevant information based on its knowledge of astrophysics and cosmology. The tone should be professional and respectful, with the AI assistant acting as a knowledgeable colleague or mentor.",
    "generations": [
        " Of course! Cooling mechanisms are indeed a critical factor in simulating the formation of giant shells in spiral galaxies. Here are a few things to consider:\n\n1. **Cooling Curve:** The cooling curve is a fundamental assumption in any hydrodynamic simulation that includes radiative cooling. It specifies the cooling rate as a function of temperature and density. You can either use a pre-computed cooling curve or calculate it yourself using a photoionization code like Cloudy.\n2. **Radiative Transfer:** Radiative transfer is an important process to consider in your simulations, as it affects the energy balance of the gas. You may want to include the effects of photoelectric heating, line cooling, and free-free emission, among other processes.\n3. **Metallicity:** The metallicity of the gas can have a significant impact on the cooling rate. If the gas is enriched with heavy elements, it will cool more efficiently than if it is metal-poor. Be sure to include the effects of metallicity in your simulations.\n4. **Time-dependent Cooling:** One of the challenges of simulating the formation of giant shells is that the cooling timescale can be quite short compared",
        "I\u0027m glad to hear that you\u0027re using a hydrodynamic simulation code to model the formation of these giant shells. Incorporating the correct cooling mechanisms is indeed crucial for accurately simulating the evolution of the ISM after a GRB.\n\nThe cooling of the ISM is primarily due to thermal radiation, or Bremsstrahlung, which is the emission of radiation as charged particles (like electrons) interact with ions in the gas. The cooling rate depends on the density and temperature of the gas, as well as the metallicity (i.e., the abundance of elements heavier than helium).\n\nTo model the cooling of the ISM in your simulations, you\u0027ll need to include a cooling function that takes these factors into account. A commonly used cooling function for astrophysical plasmas is the Raymond-Smith cooling function, which can be found in many astrophysics codes and libraries.\n\nHowever, it\u0027s essential to note that the cooling function should be chosen based on the specific conditions of your simulation. For example, if you\u0027re interested in the cooling of gas at high temperatures (above 10^7 K), you might need to use a",
        "Cooling mechanisms can be a challenging aspect of simulating the formation of giant shells! Hydrodynamic simulations are a great tool for modeling the dynamics of the gas, but accurately capturing the thermodynamic evolution of the gas can be tricky.\n\nThere are several cooling mechanisms that can come into play when modeling the formation of giant shells, including:\n\n1. Radiative cooling: This is the process by which hot gas loses energy through radiation. In the context of GRB-driven shells, radiative cooling can be important, especially at high temperatures (\u003e10^6 K).\n2. Adiabatic cooling: As the shell expands, the gas cools adiabatically due to the decrease in density. This process can be significant, especially at early times.\n3. Heat conduction: Heat can be conducted from the hot, shocked gas to the surrounding cooler gas, leading to cooling.\n4. Turbulent mixing: Turbulent motions can mix hot and cold gas, leading to cooling and the formation of a multiphase medium.\n\nTo accurately model these cooling mechanisms, you\u0027ll need to include a comprehensive cooling function in your simulation code. This function should account for the various cooling processes mentioned above, as well as any others that may be relevant (e.g., Compton cooling"
    ],
    "generations_model_names": [
        "mistralai/Mixtral-8x7B-Instruct-v0.1",
        "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
        "meta-llama/Meta-Llama-3-70B-Instruct"
    ],
    "id": "\u003curn:uuid:e2300ad5-01dd-4e80-92b3-7ec88785cc9d\u003e",
    "int_score": 3,
    "language": "en",
    "language_score": 0.9126408100128174,
    "score": 2.765625,
    "system_prompt": "Here is a system prompt to guide the generation of a multi-turn conversation between a user and an AI assistant based on the provided document:\n\n```markdown\n### Investigating the Interstellar Medium and Gamma Ray Bursts\n\n## 1. Preparation\nThe user is an astrophysicist researching the effects of Gamma Ray Bursts (GRBs) on the interstellar medium (ISM) in spiral galaxies. The AI assistant has general knowledge of astrophysics and cosmology.\n\n## 2. Interaction Structure and Roles\nThe user will initiate the conversation with a complex task or question related to GRBs and their impact on the ISM. The AI assistant will respond with relevant information, explanations, or suggestions, and the conversation will evolve based on the user\u0027s subsequent requests or questions.\n\n## 3. User Task Characteristics\nThe user\u0027s goal is to explore the potential relationship between GRBs and the formation of kpc-size shells and holes in the ISM. They may ask for explanations of relevant astrophysical concepts, request help with data analysis or simulations, or seek information about the latest research in the field.\n\n## 4. AI Assistant Characteristics\nThe AI assistant should respond in a knowledgeable and authoritative tone, using technical terms and concepts relevant to the field of astrophysics. It should be able to provide explanations, offer suggestions, and engage in discussions about the user\u0027s research goals.\n\n## 5. Engagement\nThe conversation should be engaging and informative, with the AI assistant encouraging the user to ask follow-up questions and explore different aspects of the topic.\n\n## 6. General Guidelines\nThe conversation should not mention the original document or its content directly. The AI assistant should provide accurate and relevant information based on its knowledge of astrophysics and cosmology. The tone should be professional and respectful, with the AI assistant acting as a knowledgeable colleague or mentor.## Document\n\nPrevious abstract Next abstract\nSession 40 - The Interstellar Medium.\nDisplay session, Tuesday, June 09\nGamma Ray Burst (GRB) explosions can make kpc-size shells and holes in the interstellar media (ISM) of spiral galaxies if much of the energy heats the local gas to above 10^7 K. Disk blowout is probably the major cause for energy loss in this case, but the momentum acquired during the pressurized expansion phase can be large enough that the bubble still snowplows to a kpc diameter. This differs from the standard model for the origin of such shells by multiple supernovae, which may have problems with radiative cooling, evaporative losses, and disk blow-out. Evidence for giant shells with energies of \\sim10^53 ergs are summarized. Some contain no obvious central star clusters and may be GRB remnants, although sufficiently old clusters would be hard to detect. The expected frequency of GRBs in normal galaxies can account for the number of such shells.\nProgram listing for Tuesday",
    "text": "Previous abstract Next abstract\nSession 40 - The Interstellar Medium.\nDisplay session, Tuesday, June 09\nGamma Ray Burst (GRB) explosions can make kpc-size shells and holes in the interstellar media (ISM) of spiral galaxies if much of the energy heats the local gas to above 10^7 K. Disk blowout is probably the major cause for energy loss in this case, but the momentum acquired during the pressurized expansion phase can be large enough that the bubble still snowplows to a kpc diameter. This differs from the standard model for the origin of such shells by multiple supernovae, which may have problems with radiative cooling, evaporative losses, and disk blow-out. Evidence for giant shells with energies of \\sim10^53 ergs are summarized. Some contain no obvious central star clusters and may be GRB remnants, although sufficiently old clusters would be hard to detect. The expected frequency of GRBs in normal galaxies can account for the number of such shells.\nProgram listing for Tuesday",
    "token_count": 208,
    "url": "http://aas.org/archives/BAAS/v30n2/aas192/abs/S040015.html"
}
```

This subset can be loaded as:

```python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1", "default")
```

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

```python
from datasets import load_dataset

ds = load_dataset("distilabel-internal-testing/fine-preferences-magpie-generated-system-prompt-v1")
```


</details>
