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---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language:
- amh
- arb
- ary
- ars
- acq
- arz
- apc
- ben
- ceb
- dan
- deu
- ell
- eng
- eus
- fil
- fin
- fra
- gle
- guj
- hat
- hau
- hin
- hun
- ibo
- ind
- ita
- jav
- jpn
- kan
- kir
- kor
- kur
- lit
- mal
- mar
- mlg
- msa
- mya
- nep
- nld
- nso
- nya
- pan
- pes
- pol
- por
- pus
- rus
- sin
- sna
- snd
- som
- spa
- sqi
- srp
- sun
- swa
- swe
- tam
- tel
- tha
- tur
- ukr
- urd
- vie
- wol
- xho
- yor
- zho
- zul
license: odc-by
multilinguality:
- multilingual
size_categories:
- 10<n<1K
source_datasets:
- allenai/tulu-3-sft-mixture
task_categories:
- other
dataset_info:
  features:
  - name: id
    dtype: string
  - name: messages
    list:
    - name: content
      dtype: string
    - name: role
      dtype: string
  - name: source
    dtype: string
  splits:
  - name: train
    num_examples: 1000
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


# Dataset
This dataset contains 100 samples from the [Tulu 3 SFT Mixture](https://huggingface.co/datasets/allenai/tulu-3-sft-mixture/blob/main/README.md).

## Dataset Structure
Each example in the dataset contains the standard instruction-tuning data points as follow:

- `id` (str): a unique identifier
- `messages` (list): message format used for supervised fine-tuning (this contains user prompt and assistant responses)
- `source` (str): the source dataset for the given sample

## License

This dataset is licensed under ODC-BY-1.0. It is intended for research and educational use in accordance with Ai2's [Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third party models that are subject to separate terms governing their use. For more information on license and terms, consult each subset linked above.

## Citation

If Tülu3 or any of the related materials were helpful to your work, please cite:
```
@article{lambert2024tulu3,
  title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},
  author = {
    Nathan Lambert and 
    Jacob Morrison and 
    Valentina Pyatkin and 
    Shengyi Huang and 
    Hamish Ivison and 
    Faeze Brahman and 
    Lester James V. Miranda and 
    Alisa Liu and 
    Nouha Dziri and 
    Shane Lyu and 
    Yuling Gu and 
    Saumya Malik and 
    Victoria Graf and 
    Jena D. Hwang and 
    Jiangjiang Yang and
    Ronan Le Bras and
    Oyvind Tafjord and
    Chris Wilhelm and
    Luca Soldaini and 
    Noah A. Smith and 
    Yizhong Wang and 
    Pradeep Dasigi and 
    Hannaneh Hajishirzi
  },
  year = {2024},
  email = {[email protected]}
}
```