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:
- Initial finetuning of Mistral-7B-v0.2 using structured and unstructured datasets.
- SLERP merge of the finetuned model with a model that performs better than
Mistral-7B-v0.2
onOpenLLM
benchmark: NeuralTrix-7B - 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 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
- Ukrainian StackExchange
- UAlpaca Dataset
- Ukrainian Subset from Belebele Dataset
- Ukrainian Subset from XQA
- ZNO Dataset provided in UNLP 2024 shared task
Datasets - Unstructured
- Ukrainian Wiki
Datasets - DPO
- Ukrainian translation of distilabel-indel-orca-dpo-pairs
💻 Usage
!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{boros-chivereanu-dumitrescu-purcaru-2024-llm-uk,
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.
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