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Model: SherlockAssistant/Mistral-7B-Instruct-Ukrainian
Source: Original Platform

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README.md

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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)
+- [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**
+
+```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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+  ],
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+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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+  "eos_token_id": 2,
+  "transformers_version": "4.38.0.dev0"
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special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "rstrip": false,
+    "single_word": false
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+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}