https://github.com/ziweek/award-factory
🎓 Showcasing Project, in 2024 Google Machine Learning Bootcamp - 🏆🤖 Award-Factory: Awards lovingly crafted for you by a hilariously talented generative AI! #Google #Gemma:2b #fine-tuning #quantization
https://github.com/ziweek/award-factory
docker docker-compose fastapi fine-tuning gemma-2b google large-language-model llama-cpp nextjs quantization
Last synced: 3 months ago
JSON representation
🎓 Showcasing Project, in 2024 Google Machine Learning Bootcamp - 🏆🤖 Award-Factory: Awards lovingly crafted for you by a hilariously talented generative AI! #Google #Gemma:2b #fine-tuning #quantization
- Host: GitHub
- URL: https://github.com/ziweek/award-factory
- Owner: ziweek
- License: mit
- Created: 2024-05-12T07:18:56.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-05T16:45:26.000Z (5 months ago)
- Last Synced: 2025-02-10T08:48:45.814Z (5 months ago)
- Topics: docker, docker-compose, fastapi, fine-tuning, gemma-2b, google, large-language-model, llama-cpp, nextjs, quantization
- Language: TypeScript
- Homepage: https://award-factory.vercel.app
- Size: 15.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# award-factory
🎓 Showcasing Project, in 2024 Google Machine Learning Bootcamp 🎓
KOREAN
|
ENGLISH
Award-Factory: Awards crafted for you by a hilariously talented generative AI
Check out prototypes in the badge below
# 1. Introduction
> [!NOTE]
>
> - This project aims to develop a service where anyone can effortlessly create a customized certificate in just a few minutes, making it easy to celebrate and appreciate others.
> - Award Factory was conceived as a heartwarming project to spread happiness, inspired by the idea of creating special certificates for parents. Built with sustainability in mind, the service integrates front-end components and leverages the fine-tuned Google Gemma:2b model to deliver personalized award texts. While the service is not fully active due to server operation costs, a demo is available on Huggingface.
> - Advanced technologies like QLoRA quantization and llama-cpp optimizations were employed to reduce model size and improve performance, ensuring an efficient user experience in the future.
https://github.com/user-attachments/assets/2def17e0-46ea-4561-8b50-fc78d595b88b
App Design
Generated Awards
# Implementation
Google Gemma:2B Finetuning
Implemented prompt engineering and QLoRA-based quantization fine-tuning using the Google/Gemma-2b-it model with PEFT techniques to optimize personalized award text generation tailored to user preferences.
llama-cpp Quantization
Applied quantization with the Q5_K_M option in llama-cpp, achieving a 63.3% reduction in model size and an 83.4% decrease in inference time without compromising performance, enabling faster and more efficient service.
```
$ llama.cpp/llama-quantize gguf_model/gemma-2b-it-award-factory-v2.gguf gguf_model/gemma-2b-it-award-factory-v2.gguf-Q5_K_M.gguf Q5_K_M
...
llama_model_quantize_internal: model size = 4780.29 MB
llama_model_quantize_internal: quant size = 1748.67 MB
main: quantize time = 17999.81 ms
main: total time = 17999.81 ms
```
```
$ ollama list
NAME ID SIZE MODIFIED
award-factory:q5 8df06172b64b 1.8 GB 19 seconds ago
award-factory:latest ae186115cc83 5.0 GB 28 minutes ago
```
Docker-compose
Utilized Docker Compose to containerize the backend and frontend services, ensuring consistency in deployment environments and facilitating scalable and maintainable full-stack web application development.
# Contribution
