README.md

---
license: apache-2.0
datasets:
- squarelike/sharegpt_deepl_ko_translation
language:
- en
- ko
pipeline_tag: translation
---

# Gugugo-koen-7B-V1.1-GPTQ
Detail repo: [https://github.com/jwj7140/Gugugo](https://github.com/jwj7140/Gugugo)
![Gugugo](./logo.png)

**This is GGUF model from [squarelike/Gugugo-koen-7B-V1.1](https://huggingface.co/squarelike/Gugugo-koen-7B-V1.1)**

**Base Model**: [Llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)

**Training Dataset**: [sharegpt_deepl_ko_translation](https://huggingface.co/datasets/squarelike/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:
```

## **Implementation Code**
```python
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=0.1,
        do_sample=True,
        stopping_criteria=stopping_criteria
    )
    return tokenizer.decode(gened[0][1:]).replace(prompt+" ", "").replace("</끝>", "")


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