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Mistral-7B-Instruct-Ukrainian

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 ---
 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)
+## 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 = "Radu1999/Mistral-Instruct-Ukrainian-SFT"
+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"])
+```