Model Information
- Weather Forecast-Based Personalized Recommendation System with Gemma Model.
- Language: Korean
Description
- This project aims to fine-tune the Gemma2 2B model using weather data from the Korea Meteorological Administration (KMA). By utilizing the KMA Short-term Forecast API, we will fetch real-time weather data, which will be used to provide personalized recommendations for clothing, activities, and weather-related precautions. For example, on a rainy day, the system will notify users to bring an umbrella, and on cold days, it will suggest wearing appropriate warm clothing. The model will be trained using historical weather data from Korea to ensure localized and accurate recommendations. Once the model is fine-tuned, it will be deployed on Hugging Face, making it accessible via an API.
Base Model
- google/gemma-2-2b-it
- https://huggingface.co/google/gemma-2-2b-it
Datasets
- OBS_AWS_TIM(KMA)2021~2023
- Temperature, precipitation, wind, humidity, pressure, etc.
- https://data.kma.go.kr/data/grnd/selectAwsRltmList.do?pgmNo=56
Fine-tuning a Gemma model
[1. Access Gemma]
- 1-1. Navigate to the gemma-2b repository that you wish to use.
- 1-2. Click on "Acknowledge license" in the "Access Gemma on Hugging Face" section.
- 1-3. Click on "Authorize," check the required fields, and then click "Accept."
- 1-4. If you see the message "You have been granted access to this model" [Gated model], this means you now have access to the Gemma model.
[2. Access Tokens]
- 2-1. Click on your profile in the upper-right corner and select "Settings."
- 2-2. In the menu, click on "Access Tokens," then click on [+ Create new token].
- 2-3. Under [Token type], choose ['Read'] from the options: 'Fine-grained', 'Read', 'Write'.
- 2-4. Enter a name for the token, then click "Create token."
- 2-5. Save the generated Access Token, and use it for Hugging Face authentication, such as in notebook_login.
from huggingface_hub import notebook_login
notebook_login()
[3. Gemma-2-2b-it(instruction-tuned)]
[4. Data Preprocessing]
[5. Fine-Tuning]
[6. Prompt Engineering]