---
library_name: transformers
license: apache-2.0
---

# Model card for Mistral-7B-Instruct-Ukrainian

Mistral-7B-UK is a Large Language Model finetuned for the Ukrainian language.

Mistral-7B-UK is trained using the following formula:
1. Initial finetuning of [Mistral-7B-v0.2](mistralai/Mistral-7B-Instruct-v0.2) using structured and unstructured datasets.
2. SLERP merge of the finetuned model with a model that performs better than `Mistral-7B-v0.2` on `OpenLLM` benchmark: [NeuralTrix-7B](https://huggingface.co/CultriX/NeuralTrix-7B-v1)
3. DPO of the final model.





## Instruction format

In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens.

E.g.
```
text = "[INST]Відповідайте лише буквою правильної відповіді: Елементи експресіонізму наявні у творі: A. «Камінний хрест», B. «Інститутка», C. «Маруся», D. «Людина»[/INST]"
```

This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:

## Model Architecture
This instruction model is based on Mistral-7B-v0.2, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer

## Datasets - Structured
- [UA-SQUAD](https://huggingface.co/datasets/FIdo-AI/ua-squad/resolve/main/ua_squad_dataset.json)
- [Ukrainian StackExchange](https://huggingface.co/datasets/zeusfsx/ukrainian-stackexchange)
- [UAlpaca Dataset](https://github.com/robinhad/kruk/blob/main/data/cc-by-nc/alpaca_data_translated.json)
- [Ukrainian Subset from Belebele Dataset](https://github.com/facebookresearch/belebele)
- [Ukrainian Subset from XQA](https://github.com/thunlp/XQA)
- [ZNO Dataset provided in UNLP 2024 shared task](https://github.com/unlp-workshop/unlp-2024-shared-task/blob/main/data/zno.train.jsonl)

## Datasets - Unstructured
- Ukrainian Wiki

## Datasets - DPO
- Ukrainian translation of [distilabel-indel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs)
  
## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "SherlockAssistant/Mistral-7B-Instruct-Ukrainian"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```

## Citation

If you are using this model in your research and publishing a paper, please help by citing our paper:

**BIB**

@inproceedings{syvokon-etal-2024-shared,
    title = "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models",
    author = "Boros, Tiberiu and Chivereanu, Radu and Dumitrescu, Stefan Daniel and Purcaru, Octavian",
    booktitle = "Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING",
    month = may,
    year = "2024",
    address = "Torino, Italy",
    publisher = "European Language Resources Association",
}

**APA**

Boros, T., Chivereanu, R., Dumitrescu, S., & Purcaru, O. (2024). Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models. In Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association.

**MLA**

Boros, Tiberiu, Radu, Chivereanu, Stefan Daniel, Dumitrescu, Octavian, Purcaru. "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models." Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association, 2024.

**Chicago**

Boros, Tiberiu, Radu, Chivereanu, Stefan Daniel, Dumitrescu, and Octavian, Purcaru. "Fine-tuning and Retrieval Augmented Generation for Question Answering using affordable Large Language Models." . In Proceedings of the Third Ukrainian Natural Language Processing Workshop, LREC-COLING. European Language Resources Association, 2024.