A lightweight model to do machine translation from Ukrainian to English based on recently published LFM2 model. Use demo to test it.
Also, there's another model: kulyk-en-uk
Run with Docker (CPU):
docker run -p 3000:3000 --rm ghcr.io/egorsmkv/kulyk-rust:latest
Run using Apptainer (CUDA):
- Run it using shell:
apptainer shell --nv ./kulyk.sif
Apptainer> /opt/entrypoints/kulyk --verbose --n-len 1024 --model-path-ue /project/models/kulyk-uk-en.gguf --model-path-eu /project/models/kulyk-en-uk.gguf
- Run it as a webservice:
apptainer instance start --nv ./kulyk.sif kulyk-ws
# go to http://localhost:3000
Facts:
- Fine-tuned with 40M samples (filtered by quality metric) from ~53.5M for 1.4 epochs
- 354M params
- Requires 1 GB of RAM to run with bf16
- BLEU on FLORES-200: 36.27
- Tokens per second: 229.93 (bs=1), 1664.40 (bs=10), 8392.48 (bs=64)
- License: lfm1.0
Info:
- Model name is inherited from name of Sergiy Kulyk who was chargé d'affaires of Ukraine in the United States
Training Info:
- Learning Rate: 3e-5
- Learning Rate scheduler type: cosine
- Warmup Ratio: 0.05
- Max length: 2048
- Batch Size: 10
packed=True- Sentences <= 1000 chars
- Gradient accumulation steps: 4
- Used Flash Attention 2
- Time for epoch: 32 hours
- 2 cards of NVIDIA RTX 3090 Ti (24G)
acceleratewith DeepSpeed- Memory usage: 22.212GB-22.458GB
- torch 2.7.1
Acknowledgements:
- Dmytro Chaplynskyi for providing compute to train this model
- lang-uk members for their compilation of different MT datasets