
## Instruction Tuning: GeoSignal

Scientific domain adaptation has two main steps during instruction tuning. 
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4. 
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.

***The following is the illustration of the training domain-specific language model recipe:***
![recipe](https://big-cheng.com/k2/recipe.png)

- **Adapter Model on [Huggingface](https://huggingface.co/): [daven3/k2_it_adapter](https://huggingface.co/daven3/k2_it_adapter)**

For the design of the GeoSignal, we collect knowledge from various data sources, like: 

![geosignal](https://big-cheng.com/k2/geosignal.png)

GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.

The full-version will be upload soon, or email [daven](mailto:davendw@sjtu.edu.cn) for potential research cooperation.

task_categories:question-answering license:apache-2.0 region:us
{"license": "apache-2.0", "task_categories": ["question-answering"]}
TAGS
#task_categories-question-answering #license-apache-2.0 #region-us 


## Instruction Tuning: GeoSignal

Scientific domain adaptation has two main steps during instruction tuning. 
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4. 
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.

*The following is the illustration of the training domain-specific language model recipe:*
!recipe

- Adapter Model on Huggingface: daven3/k2_it_adapter

For the design of the GeoSignal, we collect knowledge from various data sources, like: 

!geosignal

GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.

The full-version will be upload soon, or email daven for potential research cooperation.

## Instruction Tuning: GeoSignal

Scientific domain adaptation has two main steps during instruction tuning. 
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4. 
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.

*The following is the illustration of the training domain-specific language model recipe:*
!recipe

- Adapter Model on Huggingface: daven3/k2_it_adapter

For the design of the GeoSignal, we collect knowledge from various data sources, like: 

!geosignal

GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.

The full-version will be upload soon, or email daven for potential research cooperation.
TAGS
#task_categories-question-answering #license-apache-2.0 #region-us 
 ## Instruction Tuning: GeoSignal

Scientific domain adaptation has two main steps during instruction tuning. 
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4. 
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.

*The following is the illustration of the training domain-specific language model recipe:*
!recipe

- Adapter Model on Huggingface: daven3/k2_it_adapter

For the design of the GeoSignal, we collect knowledge from various data sources, like: 

!geosignal

GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.

The full-version will be upload soon, or email daven for potential research cooperation.
passage: TAGS
#task_categories-question-answering #license-apache-2.0 #region-us 
## Instruction Tuning: GeoSignal

Scientific domain adaptation has two main steps during instruction tuning. 
- Instruction tuning with general instruction-tuning data. Here we use Alpaca-GPT4. 
- Instruction tuning with restructured domain knowledge, which we call expertise instruction tuning. For K2, we use knowledge-intensive instruction data, GeoSignal.

*The following is the illustration of the training domain-specific language model recipe:*
!recipe

- Adapter Model on Huggingface: daven3/k2_it_adapter

For the design of the GeoSignal, we collect knowledge from various data sources, like: 

!geosignal

GeoSignal is designed for knowledge-intensive instruction tuning and used for aligning with experts.

The full-version will be upload soon, or email daven for potential research cooperation.