Instructions to use Yehor/kulyk-uk-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yehor/kulyk-uk-en with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Yehor/kulyk-uk-en")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Yehor/kulyk-uk-en")
model = AutoModelForMultimodalLM.from_pretrained("Yehor/kulyk-uk-en")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use Yehor/kulyk-uk-en with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Yehor/kulyk-uk-en",
	filename="kulyk-uk-en-q8_0.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Yehor/kulyk-uk-en with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Yehor/kulyk-uk-en:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Yehor/kulyk-uk-en:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Yehor/kulyk-uk-en:Q8_0
# Run inference directly in the terminal:
llama-cli -hf Yehor/kulyk-uk-en:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Yehor/kulyk-uk-en:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Yehor/kulyk-uk-en:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Yehor/kulyk-uk-en:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Yehor/kulyk-uk-en:Q8_0

Use Docker

docker model run hf.co/Yehor/kulyk-uk-en:Q8_0

LM Studio
Jan

vLLM

How to use Yehor/kulyk-uk-en with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yehor/kulyk-uk-en"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yehor/kulyk-uk-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Yehor/kulyk-uk-en:Q8_0

SGLang

How to use Yehor/kulyk-uk-en with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Yehor/kulyk-uk-en" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yehor/kulyk-uk-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Yehor/kulyk-uk-en" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yehor/kulyk-uk-en",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Yehor/kulyk-uk-en with Ollama:
```
ollama run hf.co/Yehor/kulyk-uk-en:Q8_0
```

Unsloth Studio

How to use Yehor/kulyk-uk-en with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Yehor/kulyk-uk-en to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Yehor/kulyk-uk-en to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Yehor/kulyk-uk-en to start chatting

Atomic Chat new
Docker Model Runner
How to use Yehor/kulyk-uk-en with Docker Model Runner:
```
docker model run hf.co/Yehor/kulyk-uk-en:Q8_0
```

Lemonade

How to use Yehor/kulyk-uk-en with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Yehor/kulyk-uk-en:Q8_0

Run and chat with the model

lemonade run user.kulyk-uk-en-Q8_0

List all available models

lemonade list

kulyk-uk-en

File size: 2,263 Bytes

---
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