Gemago 2b

🚀 Gemago 2B Model Card

Gemago 2B is a lightweight English-and-Korean translation model based on Gemma, enabling English/Korean to Korean/English translation.

🚀 Quick Start

Below we share some code snippets on how to get quickly started with running the model. First make sure to pip install -U transformers, then copy the snippet from the section that is relevant for your usecase.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("devworld/gemago-2b")
model = AutoModelForCausalLM.from_pretrained("devworld/gemago-2b")

def gen(text, max_length):
    input_ids = tokenizer(text, return_tensors="pt")
    outputs = model.generate(**input_ids, max_length=max_length)
    return tokenizer.decode(outputs[0])

def e2k(e):
    input_text = f"English:\n{e}\n\nKorean:\n"
    return gen(input_text, 1024)

def k2e(k):
    input_text = f"Korean:\n{k}\n\nEnglish:\n"
    return gen(input_text, 1024)

Running the model with transformers

✨ Features

Model Information

Translate English/Korean to Korean/English.

Description

Gemago is a lightweight English-and-Korean translation model based on Gemma.

Context Length

Models are trained on a context length of 8192 tokens, which is equivalent to Gemma.

📚 Documentation

Original Gemma Model Page: Gemma

Model Page On Github: Gemago

Resources and Technical Documentation:

• Blog(Korean)
• Original Google's Gemma-2B
• Training Code @ Github: Gemma-EasyLM (Orginial by Beomi)

Terms of Use: Terms

Authors: Orginal Google, Fine-tuned by DevWorld

📄 License

The model is licensed under [apache-2.0, gemma].