language:ko license:mit region:us
---
license: mit
language:
- ko
---

# Translated into Korean with DeepL
All Texts are translated with DeepL. (Machine Translated.)
- Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and all datas are merged into single file and splitted by few code and hand.
  - This is sample/test processing of data set creation with DeepL.
- Original Dataset: totally-not-an-llm/EverythingLM-data-V2


# EverythingLM V2 Dataset

**EverythingLM V2** is a diverse instruct dataset consisting of 1k of human-assistant conversations. These sets were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions.

### Differences for V1:

- All data in V2 is generated by GPT4
- Higher quality dataset generation pipeline:
  - More humalike seed prompts
  - Fixed some bugs in the script
  - More diverse creative writing
  - More diverse seed prompts in general
  - Attempt not to overfit the model on complex instructions by occasionally skipping evol
 
### Cost:
Reproducing this dataset would cost roughly $40.

### Instruction Categories:

- Reasoning
- Creative Writing
- General Knowledge
- Brainstorming
- Search Query
- Coding
- Basic Instruct

We also leverage various system prompts for evol-instruct and for responding to prompts.
This dataset has also been filtered to remove OpenAI alignment.

### How it stands out:

- Long, detailed outputs
- Humanlike creativity
- CoT reasoning
- Complex & challenging tasks

### Plans:

- Train Llama 7b & 13b models (13b model V1 trained)
- Train Llama 70b QLoRA
- Generate V2 of the dataset, with more categories and GPT-4 (DONE) ✓

Included in this repo is the script to generate the dataset.