dc85b46d2c
Model: QuantFactory/llama-3.1-Asian-Bllossom-8B-Translator-GGUF Source: Original Platform
152 lines
5.0 KiB
Markdown
152 lines
5.0 KiB
Markdown
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---
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library_name: transformers
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license: llama3.1
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language:
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- ko
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- vi
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- id
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- km
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- th
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metrics:
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- bleu
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- rouge
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/llama-3.1-Asian-Bllossom-8B-Translator-GGUF
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This is quantized version of [MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator](https://huggingface.co/MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator) created using llama.cpp
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# Original Model Card
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# Model Card for Model ID
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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:
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- Korean
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- Vietnamese
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- Indonesian
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- Cambodian (Khmer)
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- Thai
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## Acknowledgements
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AICA <img src="https://aica-gj.kr/images/logo.png" width="20%" height="20%">
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## Model Details
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The model is designed for translating short text segments between any pair of the supported languages.
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Supported language pairs:
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- Korean ↔ Vietnamese
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- Korean ↔ Indonesian
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- Korean ↔ Cambodian
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- Korean ↔ Thai
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- Vietnamese ↔ Indonesian
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- Vietnamese ↔ Cambodian
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- Vietnamese ↔ Thai
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- Indonesian ↔ Cambodian
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- Indonesian ↔ Thai
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- Cambodian ↔ Thai
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### Model Description
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This model is specifically optimized for Southeast Asian language translation needs, focusing on enabling communication between these specific language communities.
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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.
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### Model Architecture
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Base Model: meta-llama/Llama-3.1-8B-Instruct
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## Bias, Risks, and Limitations
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- Performance is limited to short sentences and phrases
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- May not handle complex or lengthy text effectively
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- Translation quality may vary depending on language pair and content complexity
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## Evaluation results
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| Source Language | Target Language | BLEU Score | ROUGE-1 | ROUGE-L |
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|----------------|-----------------|------------|---------|---------|
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| Korean | Vietnamese | 56.70 | 81.64 | 76.66 |
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| Korean | Cambodian | 71.69 | 89.26 | 88.20 |
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| Korean | Indonesian | 58.32 | 80.39 | 76.63 |
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| Korean | Thai | 63.26 | 78.88 | 72.29 |
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| Vietnamese | Korean | 49.01 | 75.57 | 72.74 |
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| Vietnamese | Cambodian | 78.26 | 90.74 | 90.32 |
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| Vietnamese | Indonesian | 65.96 | 83.08 | 81.46 |
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| Vietnamese | Thai | 65.93 | 81.09 | 76.57 |
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| Cambodian | Korean | 49.10 | 72.67 | 69.75 |
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| Cambodian | Vietnamese | 63.42 | 81.56 | 79.09 |
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| Cambodian | Indonesian | 61.41 | 79.67 | 77.75 |
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| Cambodian | Thai | 70.91 | 81.85 | 77.66 |
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| Indonesian | Korean | 53.61 | 77.14 | 74.29 |
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| Indonesian | Vietnamese | 68.21 | 85.41 | 83.10 |
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| Indonesian | Cambodian | 78.84 | 90.81 | 90.35 |
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| Indonesian | Thai | 67.12 | 81.54 | 77.19 |
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| Thai | Korean | 45.59 | 72.48 | 69.46 |
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| Thai | Vietnamese | 61.55 | 81.01 | 78.24 |
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| Thai | Cambodian | 78.52 | 91.47 | 91.16 |
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| Thai | Indonesian | 58.99 | 78.56 | 76.40 |
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## Example
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```py
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator",
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torch_dtype="auto",
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"MLP-KTLim/llama-3.1-Asian-Bllossom-8B-Translator",
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)
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input_text = "안녕하세요? 아시아 언어 번역 모델 입니다."
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def get_input_ids(source_lang, target_lang, message):
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assert source_lang in ["Korean", "Vietnamese", "Indonesian", "Thai", "Cambodian"]
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assert target_lang in ["Korean", "Vietnamese", "Indonesian", "Thai", "Cambodian"]
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input_ids = tokenizer.apply_chat_template(
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conversation=[
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{"role": "system", "content": f"You are a useful translation AI. Please translate the sentence given in {source_lang} into {target_lang}."},
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{"role": "user", "content": message},
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],
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tokenize=True,
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return_tensors="pt",
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add_generation_prompt=True,
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)
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return input_ids
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input_ids = get_input_ids(
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source_lang="Korean",
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target_lang="Vietnamese",
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message=input_text,
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)
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output = model.generate(
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input_ids.to(model.device),
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max_new_tokens=128,
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)
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print(tokenizer.decode(output[0][len(input_ids[0]):], skip_special_tokens=True))
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```
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## Contributor
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- 원인호 (wih1226@seoultech.ac.kr)
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- 김민준 (mjkmain@seoultech.ac.kr)
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