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dhtocks/
nllb-200-distilled-350M_en-ko

Name: nllb-200-distilled-350M_en-ko
Availability: InStock

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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

python

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

bibtex

@misc{,
  title={NLLB-200 distilled_350M_en-ko},
  author={Saechan Oh},
  year={2024}
}