QuantFactory/llama-3.1-Asian-Bllossom-8B-Translator-GGUF
This is quantized version of MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator created using llama.cpp
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This model is a multilingual translation model fine-tuned on LLaMA 3.1 Instruct base model. It enables mutual translation between the following Southeast Asian languages:
- Korean
- Vietnamese
- Indonesian
- Cambodian (Khmer)
- Thai
Acknowledgements
AICA
Model Details
The model is designed for translating short text segments between any pair of the supported languages.
Supported language pairs:
- Korean ↔ Vietnamese
- Korean ↔ Indonesian
- Korean ↔ Cambodian
- Korean ↔ Thai
- Vietnamese ↔ Indonesian
- Vietnamese ↔ Cambodian
- Vietnamese ↔ Thai
- Indonesian ↔ Cambodian
- Indonesian ↔ Thai
- Cambodian ↔ Thai
Model Description
This model is specifically optimized for Southeast Asian language translation needs, focusing on enabling communication between these specific language communities.
The extensive training data of 20M examples (1M for each translation direction) provides a robust foundation for handling common expressions and basic conversations across these languages.
Model Architecture
Base Model: meta-llama/Llama-3.1-8B-Instruct
Bias, Risks, and Limitations
- Performance is limited to short sentences and phrases
- May not handle complex or lengthy text effectively
- Translation quality may vary depending on language pair and content complexity
Evaluation results
Source Language | Target Language | BLEU Score | ROUGE-1 | ROUGE-L |
---|---|---|---|---|
Korean | Vietnamese | 56.70 | 81.64 | 76.66 |
Korean | Cambodian | 71.69 | 89.26 | 88.20 |
Korean | Indonesian | 58.32 | 80.39 | 76.63 |
Korean | Thai | 63.26 | 78.88 | 72.29 |
Vietnamese | Korean | 49.01 | 75.57 | 72.74 |
Vietnamese | Cambodian | 78.26 | 90.74 | 90.32 |
Vietnamese | Indonesian | 65.96 | 83.08 | 81.46 |
Vietnamese | Thai | 65.93 | 81.09 | 76.57 |
Cambodian | Korean | 49.10 | 72.67 | 69.75 |
Cambodian | Vietnamese | 63.42 | 81.56 | 79.09 |
Cambodian | Indonesian | 61.41 | 79.67 | 77.75 |
Cambodian | Thai | 70.91 | 81.85 | 77.66 |
Indonesian | Korean | 53.61 | 77.14 | 74.29 |
Indonesian | Vietnamese | 68.21 | 85.41 | 83.10 |
Indonesian | Cambodian | 78.84 | 90.81 | 90.35 |
Indonesian | Thai | 67.12 | 81.54 | 77.19 |
Thai | Korean | 45.59 | 72.48 | 69.46 |
Thai | Vietnamese | 61.55 | 81.01 | 78.24 |
Thai | Cambodian | 78.52 | 91.47 | 91.16 |
Thai | Indonesian | 58.99 | 78.56 | 76.40 |
Example
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator",
torch_dtype="auto",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
"MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator",
)
input_text = "안녕하세요? 아시아 언어 번역 모델 입니다."
def get_input_ids(source_lang, target_lang, message):
assert source_lang in ["Korean", "Vietnamese", "Indonesian", "Thai", "Cambodian"]
assert target_lang in ["Korean", "Vietnamese", "Indonesian", "Thai", "Cambodian"]
input_ids = tokenizer.apply_chat_template(
conversation=[
{"role": "system", "content": f"You are a useful translation AI. Please translate the sentence given in {source_lang} into {target_lang}."},
{"role": "user", "content": message},
],
tokenize=True,
return_tensors="pt",
add_generation_prompt=True,
)
return input_ids
input_ids = get_input_ids(
source_lang="Korean",
target_lang="Vietnamese",
message=input_text,
)
output = model.generate(
input_ids.to(model.device),
max_new_tokens=128,
)
print(tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True))
Contributor
- 원인호 ([email protected])
- 김민준 ([email protected])
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Base model
meta-llama/Llama-3.1-8B