# Summary

This is a 🇹🇭 Thai-translated (GCP) dataset based on [MBZUAI/LaMini-instruction](MBZUAI/LaMini-instruction), The dataset was generated with a total of 2.58 million pairs of instructions and responses which later used to fine-tune the LaMini-LM model series. 
This dataset utilizes GPT-3.5-turbo and is based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN (Longpre et al., 2023), and Alpaca (Taori et al., 2023). 
For more information about the process of generating instruction dataset, please refer to [the accompanying paper](https://arxiv.org/abs/2304.14402).

Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation

### Special Thanks: 
- Mr. Harris Boonkerd (Data Annotator)

### Languages: Thai
### Version: 1.0
---
task_categories:question-answering size_categories:1M<n<10M language:th license:cc-by-nc-4.0 instruction-finetuning arxiv:2304.14402 region:us
{"language": ["th"], "license": "cc-by-nc-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["question-answering"], "tags": ["instruction-finetuning"]}
TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-Thai #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2304.14402 #region-us 

# Summary

This is a 🇹🇭 Thai-translated (GCP) dataset based on MBZUAI/LaMini-instruction, The dataset was generated with a total of 2.58 million pairs of instructions and responses which later used to fine-tune the LaMini-LM model series. 
This dataset utilizes GPT-3.5-turbo and is based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN (Longpre et al., 2023), and Alpaca (Taori et al., 2023). 
For more information about the process of generating instruction dataset, please refer to the accompanying paper.

Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation

### Special Thanks: 
- Mr. Harris Boonkerd (Data Annotator)

### Languages: Thai
### Version: 1.0
---
# Summary

This is a 🇹🇭 Thai-translated (GCP) dataset based on MBZUAI/LaMini-instruction, The dataset was generated with a total of 2.58 million pairs of instructions and responses which later used to fine-tune the LaMini-LM model series. 
This dataset utilizes GPT-3.5-turbo and is based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN (Longpre et al., 2023), and Alpaca (Taori et al., 2023). 
For more information about the process of generating instruction dataset, please refer to the accompanying paper.

Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation ### Special Thanks: 
- Mr. Harris Boonkerd (Data Annotator) ### Languages: Thai ### Version: 1.0
---
TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-Thai #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2304.14402 #region-us 
 # Summary

This is a 🇹🇭 Thai-translated (GCP) dataset based on MBZUAI/LaMini-instruction, The dataset was generated with a total of 2.58 million pairs of instructions and responses which later used to fine-tune the LaMini-LM model series. 
This dataset utilizes GPT-3.5-turbo and is based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN (Longpre et al., 2023), and Alpaca (Taori et al., 2023). 
For more information about the process of generating instruction dataset, please refer to the accompanying paper.

Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation ### Special Thanks: 
- Mr. Harris Boonkerd (Data Annotator) ### Languages: Thai ### Version: 1.0
---
passage: TAGS
#task_categories-question-answering #size_categories-1M<n<10M #language-Thai #license-cc-by-nc-4.0 #instruction-finetuning #arxiv-2304.14402 #region-us 
# Summary

This is a 🇹🇭 Thai-translated (GCP) dataset based on MBZUAI/LaMini-instruction, The dataset was generated with a total of 2.58 million pairs of instructions and responses which later used to fine-tune the LaMini-LM model series. 
This dataset utilizes GPT-3.5-turbo and is based on several existing resources of prompts, including self-instruct (Wang et al., 2022), P3 (Sanh et al., 2022), FLAN (Longpre et al., 2023), and Alpaca (Taori et al., 2023). 
For more information about the process of generating instruction dataset, please refer to the accompanying paper.

Supported Tasks: 
- Training LLMs
- Synthetic Data Generation
- Data Augmentation### Special Thanks: 
- Mr. Harris Boonkerd (Data Annotator)### Languages: Thai### Version: 1.0
---