Gemago 2B Model Card
Original Gemma Model Page: Gemma
Model Page On Github: Gemago
Resources and Technical Documentation:
Terms of Use: Terms
Authors: Orginal Google, Fine-tuned by DevWorld
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.
Usage
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.
Running the model with transformers
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)
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