初始化项目，由ModelHub XC社区提供模型

Model: Yehor/kulyk-uk-en
Source: Original Platform

README.md

@@ -0,0 +1,92 @@
+---
+model-index:
+- name: Kulyk-UK-EN
+  results:
+  - task:
+      type: text-generation
+    dataset:
+      type: facebook/flores
+      name: FLORES
+      split: devtest
+    metrics:
+    - type: bleu
+      value: 36.27
+      name: BLEU
+library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+language:
+- en
+- uk
+pipeline_tag: text-generation
+tags:
+- liquid
+- lfm2
+- edge
+datasets:
+- lang-uk/FiftyFiveShades
+base_model:
+- LiquidAI/LFM2-350M
+---
+
+A lightweight model to do machine translation from Ukrainian to English based on recently published LFM2 model. Use [demo](https://huggingface.co/spaces/Yehor/uk-en-translator) to test it.
+
+Also, there's another model: [kulyk-en-uk](https://huggingface.co/Yehor/kulyk-en-uk)
+
+**Run with Docker (CPU)**:
+
+```
+docker run -p 3000:3000 --rm ghcr.io/egorsmkv/kulyk-rust:latest
+```
+
+**Run using Apptainer (CUDA)**:
+
+1. 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
+```
+
+2. 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](https://en.wikipedia.org/wiki/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)
+- `accelerate` with DeepSpeed
+- Memory usage: 22.212GB-22.458GB
+- torch 2.7.1
+
+**Acknowledgements**:
+
+- [Dmytro Chaplynskyi](https://huggingface.co/dchaplinsky) for providing compute to train this model
+- [lang-uk](https://huggingface.co/lang-uk) members for their compilation of different MT datasets