SherlockAssistant_-_Mistral-7B-Instruct-Ukrainian-gguf

Quantization made by Richard Erkhov.

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Mistral-7B-Instruct-Ukrainian - GGUF

• Model creator: https://huggingface.co/SherlockAssistant/
• Original model: https://huggingface.co/SherlockAssistant/Mistral-7B-Instruct-Ukrainian/

Original model description:

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 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
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 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.