Instructions to use tartuNLP/Llama-2-7b-Ukrainian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tartuNLP/Llama-2-7b-Ukrainian with Transformers:

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

pipe = pipeline("text-generation", model="tartuNLP/Llama-2-7b-Ukrainian")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tartuNLP/Llama-2-7b-Ukrainian")
model = AutoModelForCausalLM.from_pretrained("tartuNLP/Llama-2-7b-Ukrainian", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tartuNLP/Llama-2-7b-Ukrainian with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tartuNLP/Llama-2-7b-Ukrainian"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tartuNLP/Llama-2-7b-Ukrainian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tartuNLP/Llama-2-7b-Ukrainian

SGLang

How to use tartuNLP/Llama-2-7b-Ukrainian 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 "tartuNLP/Llama-2-7b-Ukrainian" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tartuNLP/Llama-2-7b-Ukrainian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tartuNLP/Llama-2-7b-Ukrainian" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tartuNLP/Llama-2-7b-Ukrainian",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tartuNLP/Llama-2-7b-Ukrainian with Docker Model Runner:
```
docker model run hf.co/tartuNLP/Llama-2-7b-Ukrainian
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-2-7b-Ukrainian

Model Details

Model Description

Llama-2-7b-Ukrainian is a bilingual pre-trained model supporting Ukrainian and English. Continued pre-training from Llama-2-7b on 5B tokens consisting of 75% Ukrainian documents and 25% English documents from CulturaX.

Paper: To Err Is Human, but Llamas Can Learn It Too

Training Hyperparameters

Hyperparameter	Value
Training steps	19080
Batch size	256
Weight decay	0.1
Context length	1024
Learning rate	2e-5 linear decay to 2e-6
Precision	bf16
Optimizer	AdamW

Citation

BibTeX:

@article{luhtaru2024err,
  title={To Err Is Human, but Llamas Can Learn It Too},
  author={Luhtaru, Agnes and Purason, Taido and Vainikko, Martin and Del, Maksym and Fishel, Mark},
  journal={arXiv preprint arXiv:2403.05493},
  year={2024}
}