NLLB-200 Distilled-350M_en2ko
The NLLB-200 model showed outstanding performance in translation task and contributed to solving problems with low-resource languages. Despite their efforts, it is still hard to run 600M or more than 1B model for those who have not enough computing environment. So I made much smaller model that expertized translaing English to Korean. you can also run it with cpu (No mixed-precision, No Quantization).
Model
-
Model: model is based on NLLB-200 600M
- Parameters: 350,537,728 (350M)
- Encoder layers: 12 -> 3
- Decoder layers: 12 -> 3
- FFN dimension: 4096 (same)
- Embed dimension: 1024 (same)
- Vocab size: 256206 (same)
-
Licnese: CC-BY-NC
Data
- Training Data: NLLB dataset
- Evaluation Data: Flores-200 dataset
Metric
- CPU: Intel (R) Xeon(R) CPU @ 2.20GHz (16 cores)
- GPU: NVIDIA L4 24GB
| #Params | chrF(++) | GPU Inference time (s) | CPU Inference time (s) | |
|---|---|---|---|---|
| NLLB-200 3.3B | 3.3B | 34.3 | 0.98 s | 4.65 s |
| NLLB-200 1.3B | 1.3B | 32.1 | 0.89 s | 2.46 s |
| NLLB-200 600M | 600M | 32 | 0.43 s | 1.52 s |
| NLLB-200 350M (ours) | 350M | 24.6 | 0.24 s | 1.43 s |
Usage
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained('dhtocks/nllb-200-distilled-350M_en-ko', forced_bos_token_id=256098)
tokenizer = AutoTokenizer.from_pretrained('dhtocks/nllb-200-distilled-350M_en-ko', src_lang='eng_Latn', tgt_lang='kor_Hang')
inputs = tokenizer('[YOUR_INPUT]', return_tensors="pt")
output = model.generate(**inputs)
print(tokenizer.decode(output[0]))
Citation
@misc{,
title={NLLB-200 distilled_350M_en-ko},
author={Saechan Oh},
year={2024}
}