Vikhrmodels
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Vikhr-Llama-3.2-1B-Instruct

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 ---
 library_name: transformers
+model_name: Vikhr-Llama-3.2-1B-instruct
+base_model:
+  - meta-llama/Llama-3.2-1B-Instruct
+language:
+  - ru
+license: apache-2.0
+datasets:
+  - Vikhrmodels/GrandMaster-PRO-MAX
 ---
+# 💨📱 Vikhr-Llama-3.2-1B-instruct
+#### RU
+Инструктивная модель на основе Llama-3.2-1B-Instruct, обученная на русскоязычном датасете GrandMaster-PRO-MAX. В 5 раз эффективнее базовой модели, и идеально подходит для запуска на слабых или мобильных устройствах.
+#### EN
+Instructive model based on Llama-3.2-1B-Instruct, trained on the Russian-language dataset GrandMaster-PRO-MAX. It is 5 times more efficient than the base model, making it perfect for deployment on low-power or mobile devices.
+## GGUF
+- [Vikhrmodels/Vikhr-Llama-3.2-1B-instruct-GGUF](https://huggingface.co/Vikhrmodels/Vikhr-Llama-3.2-1B-instruct-GGUF)
+## Особенности:
+- 📚 Основа / Base: [Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)
+- 🇷🇺 Специализация / Specialization: **RU**
+- 💾 Датасет / Dataset: [GrandMaster-PRO-MAX](https://huggingface.co/datasets/Vikhrmodels/GrandMaster-PRO-MAX)
+## Попробовать / Try now:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bJpLmplDGkMbfOLO2CH6IO-2uUZEaknf?usp=sharing)
+## Описание:
+#### RU
+Vikhr-Llama-3.2-1B-instruct — это компактная языковая модель, обученная на датасете GrandMaster-PRO-MAX, специально доученная для обработки русского языка. Эффективность модели в 5 раз превышает базовую модель, а её размер не превышает 3GB, что делает её отличным выбором для запуска на слабых и мобильных устройствах.
+#### EN
+Vikhr-Llama-3.2-1B-instruct is a compact language model trained on the GrandMaster-PRO-MAX dataset, specifically designed for processing the Russian language. Its efficiency is 5 times higher than the base model, and its size does not exceed 3GB, making it an excellent choice for deployment on low-power and mobile devices.
+## Обучение / Train:
+#### RU
+Для создания **Vikhr-Llama-3.2-1B-instruct** использовался метод SFT (Supervised Fine-Tuning). Мы обучили модель на синтетическом датасете **Vikhrmodels/GrandMaster-PRO-MAX** (150k инструкций) с поддержкой CoT (Chain-Of-Thought), используя промпты для GPT-4-turbo.
+Скрипт для запуска SFT можно найти в нашей библиотеке на GitHub: [effective_llm_alignment](https://github.com/VikhrModels/effective_llm_alignment/).
+#### EN
+To create **Vikhr-Llama-3.2-1B-instruct**, the SFT (Supervised Fine-Tuning) method was used. We trained the model on a synthetic dataset **Vikhrmodels/GrandMaster-PRO-MAX** (150k instructions) with support for CoT (Chain-Of-Thought), utilizing prompts for GPT-4-turbo.
+The script for running SFT can be found in our GitHub repository: [effective_llm_alignment](https://github.com/VikhrModels/effective_llm_alignment/).
+## Пример кода для запуска / Sample code to run:
+**Рекомендуемая температура для генерации: 0.3** / **Recommended generation temperature: 0.3**.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+# Загрузка модели и токенизатора
+model_name = "Vikhrmodels/Vikhr-Llama-3.2-1B-instruct"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+# Подготовка входного текста
+input_text = "Напиши очень краткую рецензию о книге гарри поттер."
+# Токенизация и генерация текста
+input_ids = tokenizer.encode(input_text, return_tensors="pt")
+output = model.generate(
+  input_ids,
+  max_length=1512,
+  temperature=0.3,
+  num_return_sequences=1,
+  no_repeat_ngram_size=2,
+  top_k=50,
+  top_p=0.95,
+  )
+# Декодирование и вывод результата
+generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+print(generated_text)
+```
+#### Ответ модели / Model response:
+> **Краткая рецензия на книгу "Гарри Поттер"**
+>
+> "Гарри Поттер" — это серия книг, написанная Дж. К. Роулинг, которая стала культовой в мире детских литературы. Книги рассказывают о жизни и приключениях молодого ученика по имени Гарри Поттер, который стал знаменитым по своей способности к магии.
+>
+> **Основные моменты:**
+>
+> 1.  **Введение в мир Гарри Поттера:** Книги начинаются с описания Гарри, его семьи и школы, где он изучает магию. Гарри — необычный ученик, который не имеет магических способностей, но обладает уникальным умом и способностью к решению проблем.
+>
+> 2.  **Социальные и политические аспекты:** В книгах рассматриваются социальные и политические аспекты, такие как правительство, магические общества, и их взаимодействие.
+>
+> 3.  **Магические приключения:** Гарри и его друзья, включая Рон и Хэл, сталкиваются с множеством магических угроз, включая злодеев, такие как Волшебный Войнук и Сатан.
+>
+> 4.  **Развитие персонажей:** В книгах развиваются персонажи, их мотивации и отношения с другими персонажами.
+>
+> 5.  **Философские и моральные вопросы:** Книги затрагивают темы, такие как вера, доброта, справедливость и моральные дилеммы.
+>
+> **Заключение:**
+>
+> "Гарри Поттер" — это не только история о молодом ученике, но и глубокое исследование человеческого опыта, социальных норм и моральных дилемм. Книги привлекают читателей своими захватывающими сюжетами, яркими персонажами и глубокими философскими размышлениями. Они являются не только увлекательным приключением, но и важным источником вдохновения для многих людей.
+## Метрики на ru_arena_general / Metrics on ru_arena_general
+| **Model**                                   | **Score** | **95% CI**      | **Avg Tokens** | **Std Tokens** | **LC Score** |
+| ------------------------------------------- | --------- | --------------- | -------------- | -------------- | ------------ |
+| kolibri-vikhr-mistral-0427                  | 22.41     | +1.6 / -1.6     | 489.89         | 566.29         | 46.04        |
+| storm-7b                                    | 20.62     | +2.0 / -1.6     | 419.32         | 190.85         | 45.78        |
+| neural-chat-7b-v3-3                         | 19.04     | +2.0 / -1.7     | 927.21         | 1211.62        | 45.56        |
+| **Vikhrmodels-Vikhr-Llama-3.2-1B-instruct** | **19.04** | **+1.3 / -1.6** | **958.63**     | **1297.33**    | **45.56**    |
+| gigachat_lite                               | 17.2      | +1.4 / -1.4     | 276.81         | 329.66         | 45.29        |
+| Vikhrmodels-vikhr-qwen-1.5b-it              | 13.19     | +1.4 / -1.6     | 2495.38        | 741.45         | 44.72        |
+| meta-llama-Llama-3.2-1B-Instruct            | 4.04      | +0.8 / -0.6     | 1240.53        | 1783.08        | 43.42        |
+### Авторы
+- Sergei Bratchikov, [NLP Wanderer](https://t.me/nlpwanderer), [Vikhr Team](https://t.me/vikhrlabs)
+- Nikolay Kompanets, [LakoMoor](https://t.me/lakomoor), [Vikhr Team](https://t.me/vikhrlabs)
+- Konstantin Korolev, [Vikhr Team](https://t.me/vikhrlabs)
+- Aleksandr Nikolich, [Vikhr Team](https://t.me/vikhrlabs)
+```
+@article{nikolich2024vikhr,
+  title={Vikhr: The Family of Open-Source Instruction-Tuned Large Language Models for Russian},
+  author={Aleksandr Nikolich and Konstantin Korolev and Sergey Bratchikov and Nikolay Kompanets and Artem Shelmanov},
+  journal={arXiv preprint arXiv:2405.13929},
+  year={2024},
+  url={https://arxiv.org/pdf/2405.13929}
+}
+```