Gugugo-koen-7B-V1.1

Detail repo: https://github.com/jwj7140/Gugugo 👁 Gugugo

Base Model: Llama-2-ko-7b

Training Dataset: sharegpt_deepl_ko_translation.

I trained with 1x A6000 GPUs for 90 hours.

Prompt Template

KO->EN

### 한국어: {sentence}</끝>
### 영어:

EN->KO

### 영어: {sentence}</끝>
### 한국어:

There are GPTQ, AWQ, and GGUF support.

https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GPTQ

https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-AWQ

https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1-GGUF

Implementation Code

from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
import torch
repo = "squarelike/Gugugo-koen-7B-V1.1"
model = AutoModelForCausalLM.from_pretrained(
 repo,
 load_in_4bit=True
 device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)

class StoppingCriteriaSub(StoppingCriteria):
 def __init__(self, stops = [], encounters=1):
 super().__init__()
 self.stops = [stop for stop in stops]

 def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor):
 for stop in self.stops:
 if torch.all((stop == input_ids[0][-len(stop):])).item():
 return True

 return False

stop_words_ids = torch.tensor([[829, 45107, 29958], [1533, 45107, 29958], [829, 45107, 29958], [21106, 45107, 29958]]).to("cuda")
stopping_criteria = StoppingCriteriaList([StoppingCriteriaSub(stops=stop_words_ids)])

def gen(lan="en", x=""):
 if (lan == "ko"):
 prompt = f"### 한국어: {x}</끝>\n### 영어:"
 else:
 prompt = f"### 영어: {x}</끝>\n### 한국어:"
 gened = model.generate(
 **tokenizer(
 prompt,
 return_tensors='pt',
 return_token_type_ids=False
 ).to("cuda"),
 max_new_tokens=2000,
 temperature=0.3,
 # no_repeat_ngram_size=5,
 num_beams=5,
 stopping_criteria=stopping_criteria
 )
 return tokenizer.decode(gened[0][1:]).replace(prompt+" ", "").replace("</끝>", "")


print(gen(lan="en", x="Hello, world!"))