---
dataset_info:
  features:
  - name: prompt
    dtype: string
  - name: messages
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  splits:
  - name: train_douban_sft
    num_bytes: 5567696
    num_examples: 3086
  - name: train_human_value_sft
    num_bytes: 806635
    num_examples: 1007
  - name: train_logi_qa_sft
    num_bytes: 666517
    num_examples: 421
  - name: train_ruozhiba_sft
    num_bytes: 228494
    num_examples: 240
  - name: train_segmentfault_sft
    num_bytes: 1068526
    num_examples: 458
  - name: train_wiki_sft
    num_bytes: 27611061
    num_examples: 10603
  - name: train_wikihow_sft
    num_bytes: 11069103
    num_examples: 1485
  - name: train_xhs_sft
    num_bytes: 2551884
    num_examples: 1508
  - name: train_zhihu_sft
    num_bytes: 13986060
    num_examples: 5631
  - name: train_sft
    num_bytes: 63555976
    num_examples: 24439
  - name: test_sft
    num_bytes: 228494
    num_examples: 240
  download_size: 67916523
  dataset_size: 127340446
configs:
- config_name: default
  data_files:
  - split: train_douban_sft
    path: data/train_douban_sft-*
  - split: train_human_value_sft
    path: data/train_human_value_sft-*
  - split: train_logi_qa_sft
    path: data/train_logi_qa_sft-*
  - split: train_ruozhiba_sft
    path: data/train_ruozhiba_sft-*
  - split: train_segmentfault_sft
    path: data/train_segmentfault_sft-*
  - split: train_wiki_sft
    path: data/train_wiki_sft-*
  - split: train_wikihow_sft
    path: data/train_wikihow_sft-*
  - split: train_xhs_sft
    path: data/train_xhs_sft-*
  - split: train_zhihu_sft
    path: data/train_zhihu_sft-*
  - split: train_sft
    path: data/train_sft-*
  - split: test_sft
    path: data/test_sft-*
task_categories:
- question-answering
language:
- zh
pretty_name: e
size_categories:
- 10K<n<100K
---
# COIG-CQIA-sft: COIG-CQIA for sft in alignment-handbook

数据完全来自于[COIG-CQIA](https://huggingface.co/datasets/m-a-p/COIG-CQIA)。

暂时忽略了chinese_traditional，coig_pc，exam，finance这些转换麻烦或者语义上不适合当QA数据集的subset。
其中train_sft是全集，test_sft是ruozhiba，以便代码能够跑通。

经过reformat的本数据集可以直接使用[alignment-handbook](https://github.com/huggingface/alignment-handbook) 进行sft。

可以使用[zephyr-7b-beta/sft/config_qlora.yaml](https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_qlora.yaml)进行尝试，模型和数据集都可以使用本地文件夹。


```bibtex
@misc{bai2024coig,
    title={COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning}, 
    author={Bai, Yuelin and Du, Xinrun and Liang, Yiming and Jin, Yonggang and Liu, Ziqiang and Zhou, Junting and Zheng, Tianyu and Zhang, Xincheng and Ma, Nuo and Wang, Zekun and others},
    year={2024},
    eprint={2403.18058},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
  }
```